October 2025 Summaries
100 posts from Imply
Filter
Month:
Year:
Post Summaries
Back to Blog
The Internet of Things (IoT) involves networks of connected devices that generate large amounts of timestamped data across various industries such as energy, manufacturing, and supply chain, requiring rapid ingestion, processing, and analysis to extract valuable insights. IoT environments face unique challenges due to their intersection of physical hardware and digital software, including data diversity, security concerns, and the need for specialized skills. Most IoT applications utilize streaming platforms like Apache Kafka or Amazon Kinesis for data ingestion, followed by data cleaning and storage in real-time databases optimized for analytics. IoT analytics can be categorized into descriptive, predictive, and prescriptive analytics, each serving different needs from long-term trend analysis to real-time decision-making. Apache Druid is highlighted as a real-time analytics database well-suited for IoT due to its ability to handle large volumes of fast-moving data, support complex queries, and scale elastically, making it an effective solution for managing the dynamic and varied nature of IoT data.
Oct 29, 2025
2,589 words in the original blog post.
Rick Jacobs explores the integration of OpenAI's ChatGPT with Apache Druid to create a sentiment analysis application capable of processing large datasets in real-time. Leveraging the natural language processing capabilities of ChatGPT, particularly its advanced models like GPT-3 and GPT-4, the application can accurately analyze sentiments from vast sources like social media. Apache Druid, known for its high-performance analytics on large volumes of data, complements ChatGPT by enabling efficient querying and visualization of sentiment data. This integration allows businesses to make quick, data-driven decisions based on real-time insights into customer feedback or market trends. Jacobs outlines a four-step process involving data gathering through Twitter's API, sentiment determination using ChatGPT, data ingestion into Druid, and visualization of results. He emphasizes the scalability and speed of Druid, particularly in environments requiring rapid analysis of streaming data, and suggests potential applications across various domains such as customer feedback analysis, brand monitoring, political analysis, social media marketing, and crisis management.
Oct 29, 2025
3,212 words in the original blog post.
Imply Polaris, a Database-as-a-Service built on Apache Druid, has introduced several new features aimed at enhancing global reach, security, data ingestion, and visualization capabilities. Now available across multiple AWS regions, Polaris optimizes data transfer and complies with data regulations, while its enhanced security features include private networking options and the use of AWS API Keys for JDBC connections. The platform simplifies data ingestion with schema auto-discovery, flexible table modes, support for SQL datatypes, and SQL-based ingestion, allowing seamless integration with existing SQL systems. Polaris also supports the ingestion of both event data and metadata from Apache Kafka, enabling comprehensive data analysis. Visualization enhancements include private-label branding and secure embedding links for a branded user experience. Additionally, Polaris offers cost optimization through tiered pricing and smaller project sizes, making it more accessible for users.
Oct 29, 2025
1,690 words in the original blog post.
ACID, a set of principles essential for transaction systems, comprises atomicity, consistency, isolation, and durability properties that ensure reliable transactions. Apache Druid, while not a transactional database, provides certain ACID-like guarantees suitable for analytics applications. Druid achieves atomicity through batch and real-time ingestion processes, committing data only when all components succeed, thus preventing inconsistent analytics results. Although it lacks traditional database constraints like primary and foreign keys, Druid maintains a form of consistency by rejecting non-conforming data and using commands like LATEST for retrieving current states. Isolation is upheld by handling data ingestion and query processes separately, minimizing read-write conflicts, while durability is secured by committing data to persistent deep storage, ensuring recovery in case of system failures. These capabilities allow Druid to manage large data volumes, real-time ingestion, and high query concurrency, aligning closely with the needs of modern analytics applications.
Oct 29, 2025
836 words in the original blog post.
Utility management, particularly in the context of electric grid operations, relies heavily on accurate energy consumption predictions, making data analysis a critical component. Apache Druid, with its scalable and responsive architecture, offers significant advantages for handling the massive and complex data needs of utility management, aiding in balancing energy supply and demand and accommodating decentralized energy grids. Innowatts, an AI-enabled SaaS platform serving over 45 million customers, transitioned from a complex data infrastructure to Apache Druid, which simplified their architecture, improved forecasting accuracy, and reduced costs. This transition enabled more precise data handling, such as meter-level insights and real-time data exploration, and eliminated previous bottlenecks related to schema changes, ultimately enhancing their service offerings and customer value.
Oct 29, 2025
1,023 words in the original blog post.
Streaming data has become a fundamental aspect of modern data engineering, with platforms like Apache Kafka being adopted by over 80% of Fortune 100 companies, and major cloud providers offering their own streaming services. This shift towards streaming data is driven by the need for quick, reliable, and scalable event delivery to support diverse applications, including real-time analytics. As businesses strive to analyze data as events occur, traditional batch-oriented systems are being replaced by streaming-native architectures, with databases like Apache Druid offering real-time querying capabilities. Druid's integration with Kafka allows organizations to handle large volumes of data efficiently, enabling immediate access and analysis. Companies such as Reddit and Confluent have leveraged this technology to power applications that provide interactive and real-time insights, demonstrating the potential of streaming data to revolutionize operational visibility and decision-making processes. As streaming becomes ubiquitous, businesses that adopt a streaming-native approach to analytics are poised to gain a competitive edge, with streaming technology paving the way for innovative use cases and applications.
Oct 29, 2025
1,603 words in the original blog post.
AWS IoT Core is a fully managed cloud service that enables organizations to efficiently manage and scale their Internet of Things (IoT) device networks without the need for building their own infrastructure. Since its launch in 2015, it has been widely adopted by major companies like Volkswagen, Wallbox, and Bosch for its ability to handle large volumes of real-time data through features like the MQTT broker, Device Shadow, and integration with Amazon's Alexa. AWS IoT Core supports various communication protocols and offers extensive security measures, making it versatile for different industries and use cases. It facilitates seamless device communication, data processing, and integration with other AWS services such as Amazon Kinesis and SageMaker, thus reducing development time and costs while ensuring reliability and scalability. Additionally, the integration of Imply, a real-time analytics database built on Apache Druid, enhances AWS IoT Core's capabilities by providing rapid data analysis and insights, thereby optimizing operations and enabling predictive maintenance.
Oct 29, 2025
3,289 words in the original blog post.
In the context of increasing urban populations and rising temperatures, mass transit systems are vital for reducing congestion and pollution, thereby enhancing urban livability. Real-time analytics are essential for optimizing these systems, offering insights into usage patterns, revenue, and service effectiveness, which can help authorities in schedule adaptation, route efficiency assessment, and fare adjustments. IoT devices complement this by enabling functions like predictive maintenance and passenger communication, although continuous data availability is crucial for safety-critical operations. Apache Druid is highlighted as a scalable and high-performance solution for mass transit analytics, capable of handling large volumes of real-time data efficiently. A case study of an analytics provider for over 100 public transit networks illustrates the benefits of switching from PostgreSQL to Druid, noting significant cost reductions and improved scalability and reliability. The transition also supports future-proofing by facilitating a move from batch to streaming data processing using Amazon Kinesis, enhancing both data consistency and reliability.
Oct 29, 2025
842 words in the original blog post.
Apache Druid 26.0 introduces schema auto-discovery, a feature that allows the database to automatically detect and adjust to changes in data fields and types, effectively combining the strengths of strongly-typed and schemaless data structures. This innovation addresses the challenges developers face with schema changes, which can typically slow productivity due to the need for coordination and communication. By automatically updating tables to reflect changes in the source data, Druid simplifies data management and enhances query performance without requiring existing data to be reprocessed. This functionality is particularly beneficial in environments like retail, where data needs and structures can rapidly evolve. Druid's ability to ingest and adjust to streaming data in real-time, with support for platforms like Kafka, Kinesis, and Pulsar, reinforces its position as a leading solution for real-time analytics, providing developers with reliable, up-to-date insights with minimal overhead.
Oct 29, 2025
1,343 words in the original blog post.
Apache Druid revolutionizes real-time analytics with its automatic schema discovery feature, which is particularly beneficial for handling diverse, evolving data sources without the need for manual schema management. This capability allows Apache Druid to seamlessly adapt to changes in data structures and sizes, making it ideal for event-driven streaming data. Users can focus on data analysis rather than schema maintenance, as the database automatically infers the structure of incoming data, thus enhancing flexibility, scalability, and usability. The guide explores setting up a schema-less data ingestion process using Apache Druid and Kafka, demonstrating how to manage streaming data sources with ease, and illustrating the benefits of real-time data processing. By automating schema detection, Apache Druid not only simplifies the onboarding of new data sources but also ensures data quality by identifying anomalies, ultimately empowering users to derive actionable insights efficiently.
Oct 29, 2025
1,908 words in the original blog post.
Imply Polaris, a real-time analytics Database-as-a-Service (DBaaS), has introduced several new features and enhancements, including expanded ingestion capabilities and increased reliability through multi-availability zone operations. Polaris offers a fully managed Druid-as-a-Service, enabling rapid data insights without infrastructure setup, and now includes the Multi-Stage Query (MSQ) engine as the default for batch ingestion, enhancing speed and reliability. The platform supports native, connector-less data ingestion from services like Kafka, Confluent Cloud, and Amazon Kinesis, ensuring exactly-once data semantics. With its recent availability in the AWS eu-central-1 region in Frankfurt, Polaris aims to cater to a broader customer base, and its integration with Schema Registries improves data governance and resilience. Additionally, new SQL functions like DATE_EXPAND and UNNEST facilitate finer-granularity data analysis, making complex data processing simpler for developers.
Oct 29, 2025
923 words in the original blog post.
Apache Druid manages data retention using load rules to transition segments between historical tiers and ultimately purge them from the cluster. A common misconception about these rules is that their retention periods are consecutive, while in reality, they overlap. This misunderstanding can lead to premature data removal, as illustrated by a scenario where only 5 days of data are retained instead of the expected 7. The article explains how to correct such mistakes and recover segments before their permanent deletion, highlighting that segments marked as unused are still stored in deep storage and can be restored unless deleted by the coordinator kill task. The default settings in Druid generally prevent automatic segment deletion until they are 90 days old, allowing recovery through the "Mark as Used all segments" function. This recovery process, which re-applies the load rules, ensures that previously affected data is reloaded and accessible until it naturally ages out. Understanding and adjusting these load/drop rules are crucial, but fortunately, errors are correctable if the segment data and metadata have not been permanently removed.
Oct 29, 2025
820 words in the original blog post.
Imply Polaris has introduced new features for time series analysis, aimed at enhancing real-time analytics capabilities across vast data sets, particularly in IoT and other industries. The platform, which simplifies the use of Apache Druid, now includes time series aggregation, interpolation, and functions like time-weighted averages to tackle challenges such as storage optimization and operational complexity. These tools facilitate efficient data management, enable accurate data gap filling, and allow complex analysis like anomaly detection and predictive maintenance, making it easier for businesses to derive actionable insights from sensor data. By addressing data continuity and usability issues, Polaris supports applications in various fields, including smart grids, finance, manufacturing, and more, offering a scalable solution for real-time data processing and visualization.
Oct 29, 2025
2,261 words in the original blog post.
Ibotta, a cash-back rewards platform, has enhanced its fraud prevention capabilities by integrating Imply, a tool that provides real-time analytics, to address the challenges posed by fast-evolving digital fraud. The integration has allowed Ibotta to overcome previous limitations such as slow data access, obsolete dashboards, and technical barriers in their existing systems, which were not equipped to handle the rapid pace of fraud detection and response. Imply's data river architecture enables immediate access to real-time data, allowing analysts to quickly identify and address fraud, thereby reducing potential losses and safeguarding relationships with clients and users. This system democratizes data access across Ibotta, empowering various teams to leverage real-time insights for broader business applications beyond fraud detection, including ad operations, campaign monitoring, and product analytics. The adoption of Imply aligns with Ibotta's core value of fostering innovation by enabling non-specialists to contribute to data-driven decision-making, ultimately enhancing the company's mission to make every purchase rewarding.
Oct 29, 2025
2,707 words in the original blog post.
Renewable energy, particularly solar power, plays a crucial role in addressing climate change, and the integration of real-time data analytics is transforming its operations. Solar power generation faces unique challenges, such as the remote locations of utility-scale installations that often lack reliable network communications, leading to delays in data transmission. The widespread adoption of solar systems and incentives like net metering increase data demands and user traffic, necessitating robust data handling solutions. Apache Druid offers significant advantages for solar power data management, including scalability, durability, immediate accessibility, concurrency, consistency, and support for time-series data. A leading photovoltaic inverter manufacturer switched from Microsoft SQL Server to Apache Druid due to scalability issues, enabling real-time data handling from hundreds of thousands of inverters and improving customer experiences with rapid data retrieval and automated management tasks. The company now handles immense data volumes efficiently, resolving issues with late-arriving data and considering using Druid for advanced metrics, aiming to provide customers with deeper insights and enhanced analytics capabilities.
Oct 29, 2025
997 words in the original blog post.
Imply's Developer Center serves as a resource for guiding users through scenarios and features related to Apache Druid and Imply products, offering tutorials, articles, and solutions for improving user experience. Highlighted in the Fall 2024 edition are several notable contributions, including a detailed guide on metrics observability authored by a team led by Anil Gupta, which outlines building a monitoring solution using Prometheus metrics and Imply Polaris. Another feature by Kyle Hoondert focuses on connecting Pivot to multiple Druid clusters to enhance data visibility, while Ben Hopp explores Polaris' time series capabilities for IoT applications. Senthil Vallinayagam discusses combining SQL window functions with data sketches for efficient analytics, and Ravin Trivedi presents the utility of the Broker Log Parser for analyzing query patterns. The content underscores Imply's commitment to enhancing data management capabilities and offers users the chance to engage with innovative tools and techniques.
Oct 29, 2025
774 words in the original blog post.
In a blog post by Rick Jacobs, the process of migrating analytics data from Snowflake to Apache Druid is explored, emphasizing the need for high-performance analytical queries as data volumes increase. While Snowflake is popular for its scalability, Druid is highlighted for its optimized query performance through indexing and real-time analytics capabilities. The post details a step-by-step migration process that includes extracting data from Snowflake using a Python script and loading it into Druid, with a focus on using indexing to enable faster data retrieval. The blog also provides a code walkthrough for the migration, discussing prerequisites such as having accounts on both platforms and detailing the use of Snowflake Streams or ETL tools for continuous data updates. Ultimately, the author advocates for organizations to consider Druid's capabilities to enhance their data warehousing strategies by unlocking real-time analytics and scalability.
Oct 29, 2025
1,625 words in the original blog post.
At the inaugural in-person Druid Summit, a panel discussion focused on the evolving landscape of data lakehouses and Apache Druid, highlighting the integration of Druid with Apache Iceberg and Delta Lake tables. Industry experts, including Atul Mohan, Gian Merlino, Will Xu, and Abishek Balaji Radhakrishnan, explored topics such as metadata platforms like DataHub for unified schemas, enhancements to Druid's query engine, and the technical challenges of handling deletes in data. They also discussed the potential for querying lakehouse tables directly with Druid, a feature expected in an upcoming release, and provided insights into future enhancements to Druid's storage layer. The session underscored the blurring lines between batch and streaming workloads, with advancements like Apache Iceberg reducing query latency and Druid's new capabilities making it a formidable player in the data space. The panel sparked significant interest and discussion, reflecting Druid's growing importance in the data ecosystem.
Oct 29, 2025
694 words in the original blog post.
Navigating the complexities of batch data ingestion, especially when integrating Apache Airflow with Apache Druid, is the focus of this guide, emphasizing the importance of batch processes for historical data when real-time streams falter. It provides a comprehensive walkthrough on setting up Airflow using Docker, highlighting the need to configure and update the system to include the Druid Provider, which is crucial for smooth data ingestion into Druid. The guide details configuring the environment, adding necessary connections, and parameterizing ingestion specifications using Jinja templating to enable repeatable and reliable data pipelines. Additionally, it outlines creating and executing a Directed Acyclic Graph (DAG) in Airflow, using a sample for Druid native ingestion, and underscores troubleshooting methods to ensure the correct operation of batch ingestion tasks, including checking logs to verify the validity and parameter interpretation of the ingestion specs.
Oct 29, 2025
1,421 words in the original blog post.
Apache Kafka, Flink, and Druid form a powerful open-source architecture for real-time data applications, addressing the limitations of traditional batch workflows by facilitating seamless data freshness, scale, and reliability throughout the entire data process. Kafka serves as the streaming platform, efficiently distributing massive data streams with fault tolerance and data consistency. Apache Flink complements Kafka by providing a high-throughput, unified batch and stream processing engine that enables real-time data manipulation and monitoring with exactly-once semantics. Apache Druid rounds out the architecture by delivering high-performance, real-time analytics, supporting sub-second queries and efficiently handling both streaming and historical data. This combination is utilized by companies like Lyft, Pinterest, and Reddit to power applications such as IoT analytics, security diagnostics, and customer insights, making the Kafka-Flink-Druid stack an essential tool for scaling real-time data workflows.
Oct 29, 2025
2,068 words in the original blog post.
Apache Druid's new feature, Query from Deep Storage, introduced in Druid 28.0, allows users to perform queries directly from its deep storage layer, optimizing both performance and cost by eliminating the need to preload all data onto Druid’s data servers. This feature enables a more elastic and simplified data architecture by keeping only necessary real-time data in memory while storing older, less frequently accessed data in deep storage, where queries can run asynchronously to avoid impacting real-time query latency. This development broadens Druid’s capabilities, enabling on-demand historical data analysis, exports, downloads, and complex reporting without requiring additional high-performance storage or compute resources, thus supporting both real-time and non-real-time workloads in a single system. The integration of query-time Broadcast Joins and Shuffle Joins further enhances Druid’s ability to handle large, complex queries efficiently, making the deep storage not just a backup option but an integral part of the analytics system. The deep storage layer also facilitates data redundancy, fault tolerance, and cluster scaling by reducing data movement during scaling operations, thus positioning Druid as a comprehensive solution for real-time analytics applications with extended historical analysis capabilities.
Oct 29, 2025
1,533 words in the original blog post.
Imply Polaris offers a compelling solution for businesses seeking real-time analytics by providing a fully managed, cloud-native database-as-a-service based on Apache Druid, without the associated operational complexities. While Apache Druid is known for its speed and scalability, managing it independently can be challenging and costly, requiring significant engineering effort. Polaris addresses these challenges with features such as enhanced auto-scaling, built-in visualization tools, and unique capabilities for advanced analytics, making it easier and more cost-effective for businesses to manage large volumes of streaming data. It also offers enterprise-grade security and reliability, including robust encryption and compliance certifications, ensuring data safety and regulatory adherence. With 24/7 committer-led support, Polaris reduces the total cost of ownership by offloading infrastructure management and providing expert guidance, allowing businesses to focus on deriving insights rather than managing infrastructure.
Oct 29, 2025
1,236 words in the original blog post.
Founded in 2011, GameAnalytics was the first analytics provider specifically for the gaming industry, and it now collects data from over 100,000 games played by 1.75 billion people monthly. Initially, GameAnalytics faced scalability challenges with their data architecture, which relied on Amazon DynamoDB for historical queries and an in-house system for real-time queries. The limitations of this setup, particularly with ad-hoc analysis, prompted a switch to Druid, a database designed for flexible data exploration without the need for pre-computation. Druid's architecture, which separates ingestion and queries into different nodes and supports multi-dimensional filtering, enabled better scalability and faster query responses, with response times averaging 600 milliseconds. Additionally, Druid's integration with AWS tools and ability to unify real-time and historical data allowed GameAnalytics to streamline their infrastructure, reducing their data footprint and improving cost efficiency. After transitioning to Druid, GameAnalytics now ingests 25 billion events daily and can handle up to 100,000 queries per hour, enabling them to focus on delivering valuable features to their customers.
Oct 29, 2025
702 words in the original blog post.
In 2022, Apache Druid experienced significant growth and recognition within the tech industry, driven by new analytics use cases and a robust community of over 14,000 members and 500 contributors. The year saw the introduction of Project Shapeshift, aimed at enhancing Druid's cloud-native capabilities, and the release of two major updates, Druid 0.23 and 24.0, featuring a multi-stage query engine and support for nested JSON columns. These advancements improved performance and simplified data processes, bolstering Druid's role in modern analytics applications. Concurrently, Imply, the company behind Druid, secured $100 million in Series D funding, launched the fully managed Imply Polaris database-as-a-service, and gained traction with over 500 accounts, including prominent companies like Zillow. Imply also engaged with the global tech community through events and collaborations, further solidifying its presence in the real-time analytics space and setting the stage for continued innovation in 2023.
Oct 29, 2025
1,418 words in the original blog post.
Imply Polaris, a Druid-powered platform for real-time analytics, is now available on Microsoft Azure, expanding its cloud offering beyond AWS. This integration allows organizations to leverage Polaris’s capabilities within the Azure ecosystem, providing seamless data ingestion, visualization, and queries while benefiting from Azure's extensive Platform as a Service offerings. As a fully managed service, Polaris offers an easy-to-use, scalable, and secure environment, ensuring optimal performance through automatic tuning and continuous upgrades. The Azure integration includes support for various Azure batch and streaming data services, enhancing connectivity and data ingestion. With this expansion, Polaris aims to meet the demands of Azure-first customers, enabling multi-cloud strategies and further accelerating its growth, as evidenced by a significant increase in its customer base and data volume over the past year. Organizations can subscribe to Polaris through the Azure Marketplace, where it will be available as a SaaS Pay-as-you-go offering, with integrated billing allowing Polaris usage to count against Microsoft Azure committed spend contracts.
Oct 29, 2025
756 words in the original blog post.
The text explores the decision-making process organizations face when choosing between building a custom security analytics solution or purchasing an existing one, emphasizing the critical role of customization, scalability, and cost in this decision. It highlights the evolving nature of cybersecurity threats, such as social engineering and DDoS attacks, and the importance of rapid detection and response. Apache Druid is presented as an ideal database solution for in-house security monitoring due to its speed, scalability, and compatibility with streaming technologies like Amazon Kinesis and Apache Kafka. The text also provides a case study of the Development Bank of Singapore (DBS), illustrating how Druid helped the bank achieve real-time compliance, enhance data retention, and improve fraud detection through advanced analytics and machine learning capabilities, ultimately leading to faster and more efficient operations.
Oct 29, 2025
1,500 words in the original blog post.
In the evolving landscape of analytics, the demand for high queries per second (QPS) is becoming increasingly significant as companies like Confluent, Target, and Pinterest integrate analytics into their operations, extending beyond traditional reports and dashboards. High QPS in analytics differs from high concurrency, focusing on processing many queries quickly rather than merely handling multiple simultaneous queries. Achieving high QPS is challenging due to the complex nature of queries, the volume of data processed, and the architecture of traditional data warehouses like Snowflake, BigQuery, and Redshift, which often prioritize cost efficiency over speed. Simply increasing hardware resources does not guarantee sub-second query performance, as efficient processing, strategic data storage, and minimized operational burdens are crucial. Apache Druid is highlighted as a solution specifically designed for high-performance, real-time analytics, offering low latency and cost-effective performance, as demonstrated by its use in large-scale applications at companies like Target and Confluent.
Oct 29, 2025
1,269 words in the original blog post.
Apache Druid serves as a pivotal database solution for machine learning workflows, addressing the rising challenges posed by the exponential growth of real-time data. Its architecture, optimized for speed, scale, and streaming data, supports various stages of the machine learning pipeline, from data exploration and feature engineering to real-time inference and model accuracy monitoring. Unlike traditional databases reliant on batch processing, Druid is adept at handling vast amounts of streaming data, making it suitable for applications such as fraud detection and recommendation engines. With subsecond query responses and support for complex analytics, Druid facilitates swift data discovery and exploration, crucial for preparing and refining training datasets. It also enables rapid retrieval and storage of pre-computed inferences, supporting time-sensitive decision-making in diverse sectors, including finance and retail. Companies like DBS, Sift, and Ibotta leverage Druid for its efficiency in processing large datasets and its ability to backfill time-based data, enhancing the evaluation and accuracy of machine learning models.
Oct 29, 2025
1,253 words in the original blog post.
OpenAI's acquisition of Rockset and the planned discontinuation of its services by September 2024 has prompted the need for Rockset users to find a new database solution, with Imply Polaris emerging as a strong alternative. Built on Apache Druid, Polaris offers a managed database-as-a-service designed for high concurrency and low latency queries at a petabyte scale, making it ideal for real-time analytics applications. It supports ANSI SQL, integrates seamlessly with Kafka and Kinesis, and provides features like infinite zoom and asynchronous queries, ensuring consistent performance and detailed data views. Although Polaris lacks full-text and vector search capabilities, it excels in handling fast aggregations across large datasets, complementing transactional databases. The transition from Rockset to Polaris is streamlined, involving data migration to S3 and ingestion into Polaris, with support for rewriting queries in Druid SQL. Polaris's open-source foundation ensures long-term access to core technology, and its extensive SQL support and automation reduce the learning curve and developer workload, making it an attractive option for organizations needing to maintain subsecond response times and scalability.
Oct 29, 2025
1,364 words in the original blog post.
Managing user access to data is a fundamental aspect of data platforms, and Role-Based Access Control (RBAC) is a common method to accomplish this in Druid. Basic RBAC in Druid allows limitations on READ and WRITE access at the datasource level, while Imply View Manager, an experimental feature in the Imply Druid distribution, enables more granular control by restricting access to specific rows and columns. Imply Views act like traditional database views, providing object definitions over a datasource without duplicating data, thus implementing row and column-level security. The Imply View Manager requires specific READ and WRITE permissions granted via the Druid UI or APIs and is managed by an extension loaded during cluster startup. The document provides detailed examples of how to create views and assign user permissions using the Druid console and API, demonstrating the effectiveness of Imply Views in enhancing data security.
Oct 29, 2025
1,196 words in the original blog post.
Online analytical processing (OLAP) and online transactional processing (OLTP) databases serve distinct roles in managing and leveraging data within applications, with OLTP being optimized for high-speed, small-scale transactions across numerous users and OLAP designed for complex, large-scale data analysis to inform business decisions. OLTP systems execute operations such as creating, reading, updating, and deleting data, ensuring ACID compliance for transactional integrity, and are widely used in scenarios like airline bookings or e-commerce transactions. In contrast, OLAP databases handle intensive data analysis, employing schemas like star or snowflake to facilitate quick data retrieval across multiple dimensions, suitable for generating insights into business performance or strategic planning. The document also discusses the evolving domain of real-time analytics, which combines the characteristics of both OLTP and OLAP to address the need for immediate insights from streaming data, particularly in sectors like security and digital advertising. Apache Druid, an open-source database, exemplifies this integration by supporting real-time data ingestion and complex analytical queries, offering features like schema autodetection and query-on-arrival, thus enabling organizations to efficiently manage and analyze large volumes of data.
Oct 29, 2025
2,982 words in the original blog post.
Apache Druid 26.0 introduces several new features aimed at enhancing real-time analytics capabilities, including schema auto-discovery, shuffle joins, and support for UNNEST SQL commands and array data types. Schema auto-discovery enables automatic detection and adaptation to schema changes, reducing manual effort and improving data quality, while shuffle joins allow for efficient processing of large data sets by partitioning and reshuffling data across multiple nodes. The UNNEST SQL command facilitates the handling of nested arrays by converting them into individual rows, enhancing data manipulation possibilities. Additionally, the introduction of an array data type supports storing multiple values in a single column, thereby simplifying queries and calculations while accommodating variable-length data. These advancements aim to improve the performance, flexibility, and scalability of data processing within Druid, making it more adaptable for diverse and complex data analytics tasks.
Oct 29, 2025
1,189 words in the original blog post.
Maggie Brewster's guide from August 3, 2023, details the process of creating a simple web application that uses Pivot's new embedding features to display different charts for each user selected from a dropdown menu. The application is built with React and Express, utilizing create-react-app to set up a basic React app with TypeScript and BlueprintJS for UI components. It includes a UsersMenu component to simulate user switching, where each user has specific access filters for data visualization. The backend involves an Express server that signs embed links based on user filters, using cryptographic signing to ensure secure access. A function fetches these signed links, which are then displayed in an iframe, dynamically updating whenever a user is selected or logged in, showcasing the integration of frontend and backend processes to deliver personalized data visualizations.
Oct 29, 2025
2,145 words in the original blog post.
Building a modern streaming analytics pipeline can be efficiently achieved using Confluent Cloud and Imply's Polaris, both provided as SaaS solutions for event streaming and real-time analytics, respectively. This setup involves generating a simulated clickstream event stream that is processed using Confluent Cloud's managed ksqlDB and then ingested into Imply Polaris for visualization. Traditional analytics relied on OLTP and OLAP databases with batch ETL processes, which were often slow and outdated for modern needs. In contrast, streaming analytics using a Kappa architecture simplifies the process by utilizing a single data path to handle both real-time and historical data, as exemplified by Apache Druid, which powers Imply Polaris. The tutorial demonstrates how to set up and manage data ingestion, transformation, and filtration using Confluent Cloud's ksqlDB and the seamless integration with Polaris, ultimately allowing users to create dashboards without writing complex code. This approach highlights the evolution from batch to streaming ETL, emphasizing the flexibility and immediacy of modern data processing techniques.
Oct 29, 2025
3,645 words in the original blog post.
Apache Druid is designed to meet the performance and scalability demands of data-driven applications by optimizing query processing through various data modeling techniques. The system employs a fan-out/fan-in approach for executing queries, distributing them across multiple data processes and merging results at the Broker level to deliver efficient, real-time data exploration. Key strategies to enhance query performance in Druid include rollup for data aggregation, secondary partitioning to improve query pruning, sorting to enhance data locality and compression, and leveraging indexes to expedite filtering operations. These techniques collectively improve the parallelism and efficiency of query execution, ensuring fast analytics on both real-time and historical data. By understanding and applying these data modeling methods, users can significantly boost Druid's query response times, making it a powerful tool for building scalable and high-performance data applications.
Oct 29, 2025
4,927 words in the original blog post.
After attending CriblCon 2025, the author highlights the synergy between Cribl Stream and Imply Lumi as a solution to the challenges posed by legacy observability platforms, which often require compromises between cost, retention, and performance. Cribl Stream offers control over data pipelines by filtering, enriching, and compressing data, while Imply Lumi provides a powerful query layer that stores data with full fidelity and allows for fast, flexible searches without rehydration. The integration of these two tools eliminates traditional trade-offs, enabling teams to manage data more effectively without the need for retraining or altering existing workflows. This modern observability architecture supports seamless integration with existing tools and reduces costs, offering a scalable, open, and efficient solution that adapts to growing data needs.
Oct 29, 2025
728 words in the original blog post.
Imply Polaris, now available on Microsoft Azure, is a real-time analytics database-as-a-service (DBaaS) built on Apache Druid, designed to empower developers and organizations to efficiently handle streaming and batch data with sub-second query capabilities. The integration with Azure supports multi-cloud strategies, allowing users to build real-time analytics applications with ease, featuring user-friendly APIs, built-in visualization tools, and seamless data ingestion without pre-planning. Polaris offers fully managed services with auto-scaling, bi-weekly updates, and significant cost efficiencies, reducing total cost of ownership by up to 93%. It prioritizes security and resilience through features like data encryption, private networking, and a fault-tolerant design, ensuring zero downtime. Now generally available, users can start with a 30-day free trial to explore its potential for real-time data needs.
Oct 29, 2025
622 words in the original blog post.
Anticipation builds for the first in-person Druid Summit, set to take place on October 22, 2024, at the Grand Bay Hotel in Redwood City, California. The event will feature keynotes that reflect on the evolution and future of Apache Druid, with insights into how its event-first approach is reshaping data management. Distinguished speakers such as Eric Tschetter, Gabriel Tavridis, and Gian Merlino will discuss advancements in both open-source Druid and Imply Polaris, highlighting new features like MSQ interactive and full SQL for Druid. The summit will also include panels on streaming, lakehouse, user experience, and deployment, featuring industry experts from companies like Apple, Google, and Atlassian. These sessions aim to provide attendees with practical insights into real-time data architecture, the role of Druid in the lakehouse paradigm, and effective data visualization strategies. With a focus on practical tips and emerging trends, the Druid Summit promises to be an essential event for those interested in the latest developments in data analytics.
Oct 29, 2025
759 words in the original blog post.
Apache Druid 28.0.0 is a significant release for the open-source distributed database, featuring over 420 commits and contributions from 57 developers, aimed at enhancing real-time analytics. Key updates include improved SQL compliance and the graduation of async queries, deep storage queries, and UNNEST to general availability, with added support for UNION ALL and experimental window functions. The release also introduces default nested column ingestion, multi-topic Kafka ingestion, and experimental concurrent append and replace features to manage late-arriving data more efficiently. Additionally, it offers enhanced support for Azure BLOB storage and SQL-based array ingestion, along with upgrades to the Apache Calcite library for better ANSI SQL support. These enhancements are designed to improve query stability and performance, providing a more flexible querying experience and enabling more efficient data management. Users are encouraged to consult the upgrade notes due to some non-backward-compatible changes and to share feedback via the Druid Slack channel.
Oct 29, 2025
1,585 words in the original blog post.
Imply Polaris offers a solution for embedding analytics into applications, allowing developers to integrate robust visualization options without needing to build their own user interface, thereby providing end-users with real-time insights directly within the app. Polaris is built on Druid-as-a-Service, providing a fully managed cloud service with capabilities for data ingestion and visualization, allowing users to extract insights quickly without additional infrastructure. The platform addresses the build versus buy dilemma by offering APIs and a low-code UI workflow for easy setup, enabling developers to focus on enhancing their application's unique features while ensuring scalability and security. Embedded analytics is crucial for modern applications, as users expect seamless access to data insights, and Polaris supports this need with real-time performance, multitenancy controls, and governed data access, ensuring that users only see the data relevant to them.
Oct 29, 2025
1,028 words in the original blog post.
In a data-driven landscape, organizations often utilize AWS S3 for its scalable, cost-effective, and secure storage capabilities, but when real-time analytics and rapid data querying are required, Apache Druid becomes a preferred choice due to its high-performance features. The blog discusses the process of migrating data from S3 to Druid, highlighting the benefits of such a migration, which include Druid's ability to handle real-time data ingestion and maintain exceptional query performance even under heavy loads. The blog provides a detailed explanation of the migration process, including accessing AWS S3 data, creating a Druid ingestion specification, and verifying the data transfer through SQL querying. By integrating S3's durability with Druid's analytics capabilities, organizations can improve data management efficiency and gain valuable insights, facilitating better decision-making and enhancing analytical applications.
Oct 29, 2025
1,698 words in the original blog post.
Imply's new podcast, "Tales at Scale," delves into the world of real-time analytics, featuring stories from developers and engineers, with a focus on Apache Druid. Apache Druid's origin story is recounted by co-creator Eric "Cheddar" Tschetter, who describes the initial challenges faced by a data team in 2011 requiring rapid aggregation of real-time data for digital ad auctions, leading to the creation of a new database. Despite existing solutions such as relational databases, Hadoop, and HBase, the need for handling multiple data dimensions efficiently sparked the development of Druid, which was built to support transparency and visibility needs in ad marketplaces. Over time, Druid expanded its capabilities, notably through the use of sketches for data approximations, and embraced open-source development to refine its features and enhance scalability. Today, Druid supports SQL query integration, has applications across various domains like telecommunications, and continues to evolve in the crowded real-time analytics space, positioning itself as a top choice for building analytics applications.
Oct 29, 2025
3,395 words in the original blog post.
Splunk's recent partnership with Snowflake highlights the growing trend of data federation, where data is queried directly from its existing location rather than being duplicated, a strategy aimed at reducing costs and enhancing efficiency. This approach, already utilized by platforms like Snowflake, BigQuery, and Redshift, is now being embraced for business and security data by Splunk, which recently announced federated search capabilities with Snowflake at their .conf event. Meanwhile, Imply Lumi extends this concept to observability data, focusing on handling high-velocity workloads such as logs and metrics with features like cost-efficient storage and fast query performance. The announcement underscores the industry's shift away from siloed data toward more integrated systems, allowing businesses to access and act on unified business and operational insights. While Splunk's federated search for Snowflake will be widely available in mid-2026, Imply Lumi offers immediate availability for those seeking federation solutions now, emphasizing its compatibility with existing tools and infrastructures.
Oct 29, 2025
694 words in the original blog post.
Imply Lumi is introduced as an innovative Observability Warehouse designed to enhance existing observability tools like Splunk by providing a cost-effective, high-performance data layer that allows for seamless integration without requiring workflow changes or data trade-offs. Lumi offers the ability to offload logs from Splunk, reducing storage costs while maintaining accessibility, enabling users to search and analyze years of data instantly without delays typically associated with rehydrating archived logs. The quickstart guide demonstrates how easily logs can be uploaded into Lumi, structured for immediate query readiness, and explored using familiar Splunk-style queries, all while preserving existing dashboards and alerts. This approach aims to provide Splunk users with full visibility, lower total cost of ownership, and a future-proof architecture that supports expansion across other platforms like Grafana or AI copilots, ensuring speed, scalability, and affordability in managing log data.
Oct 29, 2025
762 words in the original blog post.
Apache Druid and Apache Pinot are open-source databases designed for real-time analytics, each offering unique advantages depending on specific project needs. Druid is noted for its automatic indexing, ability to perform SQL-based transformations during data ingestion, and its capacity to separate queries by priority through query laning, making it suitable for projects that require a mix of high-performance and cost-effective data management. Druid’s elastic architecture allows it to scale efficiently, leveraging a combination of local and deep storage to maintain high performance and low costs. Pinot, developed by LinkedIn, requires manual index management, which can be tailored for specific use cases, and uses a JSON specification for data ingestion without built-in transformation capabilities, often necessitating additional tools. While Druid has a larger community with over 2,000 deployments and is supported by the commercial entity Imply, Pinot, supported by StarTree, is smaller but offers users the opportunity to influence the project's future. Both databases support high concurrency, subsecond query performance, and ingestion of streaming and batch data, making them viable options for organizations seeking to combine real-time data streams with historical insights.
Oct 29, 2025
2,853 words in the original blog post.
Security analytics is a data-driven approach to identifying, preventing, and resolving cybersecurity threats by analyzing data from various sources such as network logs and security devices. This method aids in early threat detection, improved incident response, and enhanced visibility into an organization's environment, allowing teams to proactively manage potential risks. Security analytics platforms can automate alerts, provide real-time monitoring, and utilize machine learning for anomaly detection, thus supporting decision-making and compliance with regulatory requirements. Organizations face the decision of building or buying a security analytics platform based on factors such as flexibility, pricing, and scale, with Apache Druid highlighted as a leading open-source database for building such solutions. Druid offers fast analytics on large datasets with features like stream compatibility and real-time data processing, though it may present operational challenges without additional support. Imply, a managed version of Druid, simplifies deployment while maintaining high performance and scalability, making it a compelling choice for teams seeking to develop their own security analytics platforms.
Oct 29, 2025
2,792 words in the original blog post.
Data tiering in Imply and Apache Druid is a strategy used to optimize database performance by organizing data based on its value, usage frequency, and the urgency of queries. "Hot" tiers handle recent, frequently accessed data that require fast query responses, while "cold" tiers store older, less frequently accessed data suited for long-running queries and strategic analysis. Imply leverages Apache Druid's architecture, which involves dividing data into segments across various nodes, to implement tiering strategies based on either storage or compute needs. Storage tiering organizes data by the time it was generated or by the frequency of queries, using different hardware configurations to optimize performance. Compute tiering, on the other hand, is used for environments with multitenant clusters and mixed workloads, allowing data separation by operational requirements such as CPU or RAM. These tiering strategies ensure efficient data management and query performance, tailored to specific operational needs.
Oct 29, 2025
1,317 words in the original blog post.
Imply Polaris, a real-time analytics Database-as-a-Service powered by Apache Druid, has introduced several enhancements to improve user experience and analytical capabilities. Recent updates include the ability to query deep storage, enabling cost-effective historical data analysis alongside real-time processing, and the integration of window functions for more sophisticated calculations. Data pipeline management has been streamlined with the ability to ingest from multiple Kafka topics, and enhanced visibility through Kinesis ingestion metrics allows for better monitoring and alerting. New visualization options, such as flat table and gauge formats, enhance data exploration, while non-showable dimensions and unmet alert evaluations improve embedded visualizations and alert transparency. Additionally, updates to conditional formatting, project capacity metrics, and SQL compliance align with user needs for efficient project planning and standardized query handling, making Imply Polaris a more comprehensive and user-friendly platform for real-time analytics.
Oct 29, 2025
1,824 words in the original blog post.
Imply Polaris, a real-time analytics Database-as-a-Service (DBaaS), has introduced several new features to enhance its functionality and security, focusing on data ingestion, performance monitoring, and query capabilities. The updates include the expansion of security options with AWS PrivateLink and VPC Peering for more secure data ingestion from Kafka, as well as an API to export performance metrics to external observability tools like Datadog and Prometheus. New query capabilities have been added, particularly for time-series and session analysis, including functions for data interpolation and a Sampled GROUP BY feature for complex queries on large datasets. Additionally, Polaris has enhanced its data visualization capabilities with dual-axis charts, allowing users to illustrate relationships between variables with different magnitudes. Data ingestion automation has been simplified, enabling users to transition from sample ingestion via the UI to production automation using the API. A free 30-day trial is available for users interested in exploring these features further.
Oct 29, 2025
1,018 words in the original blog post.
CriblCon 2025 highlighted the growing demand for flexible, efficient data handling architectures in the face of increasing data volumes and AI integration. Key takeaways emphasized the importance of decoupling data movement from analysis, as demonstrated by Cribl's platform, which enables users like Under Armour and Johnson Controls to streamline data logistics while achieving significant cost savings and enhanced visibility. The event underscored a shift towards architectures that maintain operational continuity while expanding capabilities, allowing teams to achieve more with existing resources. Imply Lumi complements this by optimizing data storage and query efficiency without altering established workflows, enabling seamless integration with high-volume cloud-native sources. The overarching theme was the shift from compromise to possibility, with open data and decoupled pipelines forming the foundation for modern observability, paving the way for broader, more effective AI applications.
Oct 29, 2025
857 words in the original blog post.
Online analytical processing (OLAP) tools are critical for organizations aiming to perform complex analyses on large datasets, offering insights into operations, processes, and profitability. Unlike transactional databases, OLAP focuses on extracting actionable insights from historical data through various types, including Multidimensional OLAP (MOLAP), Relational OLAP (ROLAP), and Hybrid OLAP. These tools utilize data cubes for multidimensional analysis, supporting operations like rollup, drill down, slice, dice, and pivot to explore data from different perspectives. OLAP is versatile across sectors such as sales, finance, energy, and logistics, offering features like scalability, concurrency, and support for time series data. Apache Druid stands out as a high-performance analytics database, designed for real-time analytics with features supporting streaming data and rapid query responses, making it ideal for use cases requiring immediate insights. Compared to other solutions like Snowflake, Databricks, and Oracle, Druid excels in scenarios demanding low-latency, high-concurrency data processing, particularly in real-time environments. While Snowflake and Databricks offer robust solutions for batch processing and machine learning, respectively, Druid is specialized for rapid, real-time data analytics, supporting applications in fraud detection, industrial safety, and other domains with urgent data processing needs.
Oct 29, 2025
3,942 words in the original blog post.
Apache Druid, known for its real-time analytics capabilities, has become an essential tool for the music streaming industry, helping platforms analyze vast amounts of data efficiently and cost-effectively. With three major audio streaming platforms dominating 70% of the market, Druid provides insights into listener demographics, preferences, and engagement metrics, allowing artists to access real-time stats on audience reception. Key challenges for these platforms include speed, scale, accuracy, and cost management, which Druid addresses through its ability to handle complex queries quickly and at scale. The Imply solution enhances Druid's offerings with features like advanced observability, data aggregation, and dynamic cohort analysis, along with tools like Clarity for monitoring and Pivot for data visualization. Music streaming services benefit from Imply's robust architecture, which ensures high availability, scalability, and efficient query handling, enabling targeted marketing and fan engagement. Imply's professional support and innovative design patterns further bolster its appeal, making it a valuable partner in the evolving landscape of music streaming analytics.
Oct 29, 2025
993 words in the original blog post.
Druid Summit 2023, hosted virtually by Imply, gathered nearly 1,000 registrants from around the world to explore the growing importance of Apache Druid in real-time analytics. The event featured keynotes from industry leaders like Fangjin Yang, Gian Merlino, and Ben Rogojan, who highlighted Druid's impact in diverse industries and its strategic advantages over traditional data warehouses. Attendees gained insights into Druid's latest release, Druid 28.0, and its future trajectory, as well as best practices for real-time analytics applications. The summit included over 20 breakout sessions, showcasing real-world applications and technical innovations from companies such as Atlassian, Netflix, Rivian, and Splunk. Participants left with valuable knowledge on Druid's capabilities and a vision for its continued evolution in 2024.
Oct 29, 2025
886 words in the original blog post.
Real-time analytics is a powerful approach that enables immediate analysis of big data as it is generated, providing fresh data and fast insights necessary for latency-sensitive applications. Unlike traditional analytics, which relies on historical data and longer processing times, real-time analytics supports rapid decision-making by analyzing streaming data in real-time, making it crucial for applications such as security operations, product analytics, and IoT telemetry. Essential components of a real-time analytics architecture include event streaming, typically facilitated by tools like Apache Kafka and Amazon Kinesis, and a purpose-built database such as Apache Druid, which excels in handling large volumes of streaming data with sub-second query performance. This architecture allows for scalable, low-latency analytics that can handle complex queries and large datasets, as demonstrated by companies like Netflix, which uses real-time analytics for monitoring and ensuring high-quality user experiences. By integrating real-time data with historical insights, organizations can optimize operations, enhance user interactivity, and achieve faster, more informed decision-making, ultimately providing a competitive edge.
Oct 29, 2025
1,997 words in the original blog post.
Akshat Jain joined Imply on April 1, 2024, as a Software Engineer for the Apache Druid team, driven by his interest in databases and distributed systems cultivated during his studies at the Indian Institute of Technology Patna and his previous role at Yugabyte. Fascinated by the capabilities of Apache Druid, a high-performance, real-time analytics database, he saw the potential for impactful work in managing data growth and real-time insights. Despite lacking professional experience in databases, Akshat's enthusiasm and curiosity were valued during the interview process, and he joined a small, highly-focused team, predominantly based in India, known for its rapid pace and high talent density. Akshat also appreciated the open-source nature of the work, having been involved in open-source projects like Google Summer of Code, and has found the collaborative environment at Imply to be enriching. In his two months at the company, Akshat has experienced a structured onboarding process, supportive colleagues, and rigorous code reviews, contributing to his personal and professional growth, and he has already delivered a feature and demo with the team's support.
Oct 29, 2025
658 words in the original blog post.
Observability in modern systems aims to provide clarity and efficiency but often results in complexity and operational burdens for organizations. Traditional architectures require constant management and specialized expertise, leading to inefficiencies and diverting resources from innovation. Modern approaches, exemplified by companies like Netflix and Salesforce, are leveraging real-time datastore technology to reduce costs and operational overhead. Emerging trends such as automated infrastructure management, separation of compute and storage, and the integration of traces, logs, and metrics are enabling simpler and faster observability platforms. These platforms offer real-time insights without requiring extensive backend management and are designed to be accessible to developers and analysts. Vendors like Imply are at the forefront, offering solutions that eliminate operational overhead while providing speed and scalability, moving organizations from reactive monitoring to proactive optimization. The future of observability lies in zero-tuning architectures, AI-assisted insights, and unified telemetry, empowering teams to innovate without the typical constraints of managing observability systems.
Oct 29, 2025
1,002 words in the original blog post.
Apache Druid, an open-source distributed database for real-time analytics, has seen significant advancements in 2024 under Imply's stewardship, focusing on four strategic pillars: ecosystem expansion, analytics enhancement, performance optimization, and data protection. Imply launched Polaris, a fully managed cloud environment, to simplify the use of Druid by offering features like auto-scaling, seamless data ingestion, and real-time data visualization. The year was marked by over 1,348 commits and numerous product updates, including improvements in SQL compatibility, the development of a high-performance query engine named Dart, and enhanced security and governance features for Imply Polaris. As the company looks ahead to 2025, it plans to continue refining the Dart engine, introduce a Virtual Storage Fabric for unified storage management, and embed materialized views for faster query speeds, aiming to further reduce the total cost of ownership for its customers while enhancing the real-time data experience.
Oct 29, 2025
1,193 words in the original blog post.
Apache Druid 32.0 introduces significant enhancements for real-time analytics, focusing on stability, performance, and advancing experimental features. A major highlight is the introduction of Distributed Embedded Projection, which improves query performance by pre-computing aggregations while maintaining data consistency, effectively flattening latency curves for large datasets. The release also expands support for AWS Glue catalog with Iceberg table format, facilitates better Deltalake integration with improved column selection and snapshot access, and introduces join hints to optimize SQL-based ingestion queries. UI enhancements in the Explore view improve data visualization, while the new segment timeline view aids in managing data distribution. Additional features include automatic number guessing for CSV/TSV ingestion, gRPC/protobuf support for efficient SQL queries, and official support for the ZGC garbage collector to reduce latency in high-concurrency scenarios. For further exploration, users can access Druid 32.0 through Imply Polaris or various community resources.
Oct 29, 2025
1,174 words in the original blog post.
Imply Polaris, a Database-as-a-Service built from Apache Druid, revolutionizes modern real-time analytics by offering a unified platform that combines federated queries, separation of compute and storage, and real-time analytics. It supports fast, interactive queries and cost-efficient management of both fresh and historical data through its Async Query feature, which utilizes ephemeral infrastructure for on-demand querying. This approach allows users to efficiently conduct ad-hoc analysis, integrate export and download functionalities, and perform complex reporting with low-cost queries, all while optimizing the price-performance ratio by maintaining data consistency and minimizing operational costs. By consolidating real-time and historical data management into a single system, Imply Polaris enhances workflow efficiency and reduces the need for multiple tools, making it an attractive solution for organizations looking to maximize their data infrastructure investments.
Oct 29, 2025
974 words in the original blog post.
Imply Polaris, a fully managed Database-as-a-Service (DBaaS), has expanded its availability to the AWS eu-central-1 region in Frankfurt, allowing European customers to build real-time analytics applications more efficiently since its launch in March 2022. The service, which has seen growing demand from both small businesses and large multinationals with data in the EU, offers a free trial in both the EU and US regions and can be purchased through the AWS Marketplace with flexible payment plans. To support enterprises that have ingested substantial amounts of data into Imply Polaris, the company has enhanced the platform's resiliency and availability, offering an uptime service level agreement for real-time analytics operations. This expansion is part of Imply's broader strategy to extend Polaris across more AWS regions to cater to global customer needs.
Oct 29, 2025
247 words in the original blog post.
Imply has launched Imply Polaris, a database-as-a-service (DBaaS) designed for Apache Druid, on Amazon Web Services (AWS) in the Asia Pacific (Mumbai) region, specifically targeting developers in India seeking to create real-time analytics applications. This service enables organizations to leverage Druid's capabilities in providing interactive data experiences from streaming data, as exemplified by Paytm's use of Druid for a large-scale analytics application that resulted in significant infrastructure cost savings. Imply Polaris offers a fully managed cloud service that simplifies the development of analytics applications by handling infrastructure concerns, ensuring data sovereignty, and allowing easy integration with data streams from platforms like Confluent Cloud, Apache Kafka, and Amazon Kinesis. It supports automated tuning and configuration, expert-guided data modeling, and optimized data layouts, with features like curated dashboards for performance monitoring and dynamic visualization capabilities. The service is available across multiple AWS regions, ensuring compliance with data regulations, and streamlines data transfer to optimize network costs and performance while providing developers with tools to embed visualizations into applications.
Oct 29, 2025
655 words in the original blog post.
Observability is transitioning from traditional methods, which often rely on static dashboards and fragmented telemetry, to a more modern, event-driven approach that treats all observability data as real-time events. This shift addresses the challenges faced by organizations, such as data overload, high storage costs, and slow query performance, by enabling real-time analysis and AI-driven anomaly detection across unified datasets. Companies like Netflix, Ibotta, and Reddit illustrate the benefits of adopting event-driven architectures, which enhance their ability to detect issues swiftly and maintain seamless operations. Imply, built on Apache Druid, facilitates this transformation by offering a cloud-native solution that supports high-speed queries, real-time data analysis, and cost-efficiency, allowing teams to focus on understanding and acting on insights rather than merely collecting data.
Oct 29, 2025
774 words in the original blog post.
Snowflake and Apache Druid serve distinct roles in data analytics, with each offering unique advantages based on specific needs. Snowflake is a cloud-based data warehouse that excels in cost-effectiveness and scalability for regular reporting tasks, utilizing a micro-partition and on-demand container model to optimize performance and storage costs. It is ideal for organizations requiring reliable, scheduled reporting without the critical need for high-speed or real-time data processing. In contrast, Apache Druid is an open-source real-time analytics database designed for subsecond query responses and high concurrency, making it suitable for applications demanding immediate data interaction, such as operational visibility and user behavior analytics. Druid supports both real-time and historical data ingestion, ensuring continuous availability and performance at scale, and is highly adaptable, running on various platforms from on-premise to cloud-managed services. While Snowflake is preferred for regular reporting with less emphasis on speed, Druid excels in scenarios requiring rapid, interactive data exploration and real-time analytics.
Oct 29, 2025
1,256 words in the original blog post.
The blog post explores the integration of the Python Plotly library with Apache Druid to create dynamic data visualizations, highlighting the importance of visualizing complex data patterns for effective analytics. It details the process of connecting Python to a Druid database, executing SQL queries to fetch data, and using Plotly to create various visualizations such as pie charts and 3D scatter plots. The post also covers the creation of a web application using Flask to display data in a tabular format and render visualizations, emphasizing the strengths and considerations of using Plotly for interactive charting. Additionally, it touches on the capabilities of Druid for high-performance analytics on large datasets, the significance of data visualization in decision-making, and the continuous journey of learning and innovation in data visualization. The author, Rick Jacobs, shares insights from his extensive experience in technology and encourages readers to explore further with Imply Polaris for more advanced visualizations.
Oct 29, 2025
5,371 words in the original blog post.
Rick Jacobs, a Senior Technical Product Marketing Manager with extensive experience in technology, documents his exploration of Apache Druid, a real-time analytics database, as a reference for those interested in its capabilities. The blog covers the installation of Druid on a local machine, detailing prerequisites such as a Unix-like OS, Java, and Python, and guides users through setting up their environment and starting Druid services. It delves into Druid's SQL ingestion process, which allows users to import large datasets from various sources into Druid, and provides an overview of executing SQL queries to analyze data, leveraging Druid's efficient architecture for handling high query volumes in real-time. Jacobs provides practical examples such as querying Wikipedia data to showcase Druid's capabilities in real-time analytics, highlighting its ability to manage trillions of events with low latency. The blog serves as an introduction to Druid's powerful features for real-time decision-making and hints at future posts exploring integrations with other technologies.
Oct 29, 2025
1,257 words in the original blog post.
Imply Polaris, a real-time analytics Database-as-a-Service powered by Apache Druid, has introduced several new features aimed at enhancing data management, collaboration, and transformation. Key updates include improved metadata management capabilities, allowing for the ingestion of system fields from Amazon S3, which simplifies data integration and analysis. The platform also supports EARLIEST_BY and LATEST_BY functions for effective timestamp-based aggregation, particularly useful for time-series data. Additionally, users can now import and export JSON definitions for data cubes and dashboards to facilitate collaboration and data integrity. Audit Logs have been introduced to bolster security and oversight by tracking user management and authentication events. Finally, the inclusion of SQL PIVOT and UNPIVOT operators enhances data transformation by allowing users to restructure and aggregate data more effectively, aligning with SQL standards for more flexible analysis and reporting. These enhancements collectively improve the efficiency and user-friendliness of Imply Polaris for diverse analytics applications.
Oct 29, 2025
1,010 words in the original blog post.
Apache Druid 25.0 introduces significant updates, including the graduation of the Multi-Stage Query (MSQ) and nested column features to production-ready status, enhancing data ingestion and handling nested data from formats like Avro and Parquet. The release simplifies Druid cluster infrastructure with an experimental feature allowing ingestion tasks to run directly on Kubernetes without middle managers and introduces front-coded string dictionary compression to effectively reduce data footprint with minimal impact on query performance. The start-up process is improved with the new "start-druid" script, which automatically allocates system memory and threads, facilitating easier deployment. Additional enhancements include segment balancing, Kafka lookup support in the web console, and new metrics for better cluster monitoring. Users are encouraged to explore these updates and consider contributing to the Druid community.
Oct 29, 2025
802 words in the original blog post.
Celebrating a decade since its inception, Imply has evolved from developing Apache Druid—a pioneering open-source database that delivers subsecond real-time and historical data performance—into a comprehensive platform reshaping real-time analytics. Initially driven by a need for instant data insights without high costs, Druid's innovative approach has garnered a global community and widespread adoption across various industries, including finance and tech. Underpinned by significant investments, Imply has launched Polaris, a user-friendly, cloud-native Druid service that simplifies data management. The company continues to emphasize performance, simplicity, and scale while fostering an open-source ethos. As it looks to the future, Imply aims to advance real-time analytics and observability, promising further innovation in data infrastructure.
Oct 29, 2025
800 words in the original blog post.
Online Analytical Processing (OLAP) software is designed to enable detailed analysis of vast amounts of business data from diverse sources, helping organizations gain critical insights into operational performance and profitability. OLAP systems must overcome challenges such as dispersed data and the need for compatibility with various technologies, often employing ETL or streaming methods for data intake. These systems are adept at handling large data volumes and executing resource-intensive operations, although the rise of real-time analytics is pushing OLAP solutions to adapt for faster, more concurrent processing. OLAP databases fall into three categories: Multidimensional OLAP (MOLAP), which uses data cubes for intuitive analysis; Relational OLAP (ROLAP), which structures data like traditional SQL databases; and Hybrid OLAP (HOLAP), which combines attributes of both. Each type facilitates core OLAP operations like rollup, drill down, slice, dice, and pivot to provide different data perspectives. OLAP remains popular across industries due to its ability to democratize data analysis, offering powerful insights without requiring deep technical expertise, and enabling organizations to streamline processes and improve decision-making. Apache Druid exemplifies a modern OLAP solution by combining fast response times with the ability to handle complex queries at scale, supporting both transactional and analytical needs through features like schema autodetection and integration with streaming platforms like Amazon Kinesis and Apache Kafka.
Oct 29, 2025
3,550 words in the original blog post.
Imply Polaris, a real-time analytics Database-as-a-Service (DBaaS) powered by Apache Druid®, introduced several new features in September 2023 to enhance user experience in data integration, management, and visualization. The platform now supports multiple projects, allowing users to separate environments for development, staging, and production, and tailor projects for specific departments or regions, thus providing enhanced scalability and control. Automatic Table Creation has been added to streamline data ingestion workflows by automating table setup and reducing schema-related errors. Additionally, Credit-Based Trials and a Starter Project Size offer cost-effective options for new users to evaluate Polaris with $500 in credits and optimized resources for testing. Finally, integration with Grafana expands Polaris's data visualization capabilities, complementing existing support for tools like Tableau and Looker, thereby promoting flexibility and efficiency in data analysis workflows. These updates aim to simplify resource management, enhance project isolation, and provide users with a more seamless and adaptable analytics experience.
Oct 29, 2025
1,076 words in the original blog post.
Apache Kafka and Apache Druid have seen significant adoption as foundational technologies for data streaming and real-time analytics, respectively, with Kafka facilitating large-scale event delivery and Druid enabling high-concurrency subsecond queries that combine streaming and historical data. Together, they provide a robust solution for enterprises needing real-time analytics, such as Netflix, Walmart, and Reddit, with Druid offering built-in ingestion for Kafka and ensuring exactly-once event processing. While ksqlDB is a popular choice for stream processing, it often complements Druid for deeper data exploration and historical context analysis. Organizations benefit from Druid's ability to handle millions of events per second, leveraging its integration with Kafka-compatible platforms, and solutions like Imply Polaris enhance the ease of implementing these technologies.
Oct 29, 2025
921 words in the original blog post.
Imply Polaris, a cloud-native real-time analytics DBaaS built on Apache Druid, integrates with Microsoft Azure to provide a robust architecture for real-time analytics applications. Polaris offers seamless data ingestion through both streaming and batch methods using Azure services like Azure Event Hub, Blob Storage, and Data Lake Storage, while supporting pre-processing with tools like Apache Flink and Spark. Once ingested, Polaris allows users to query and visualize data using its in-built tools or third-party integrations like Tableau and Azure Managed Grafana. It provides flexibility in deployment with various project shapes and SKUs, ensuring efficient resource utilization with auto-scaling features, and emphasizes security and resiliency with encrypted data, private networking, and high availability through Azure's infrastructure. Users can manage access control, and the platform guarantees performance with dedicated Kubernetes resources, making it a comprehensive solution for extracting insights from diverse data sources.
Oct 29, 2025
739 words in the original blog post.
In a rapidly evolving data-centric landscape, effectively managing and analyzing data streams is crucial for businesses to derive valuable insights. Kafka serves as a robust message-broker system that efficiently handles real-time data feeds, while Druid acts as a high-performance real-time analytics database designed for swift queries and low-latency stream ingestion. Automating the ingestion of Kafka topics into Druid can significantly enhance operational efficiency and scalability by reducing manual workloads, eliminating time delays, and ensuring immediate access to the latest data insights. This process involves Python scripts that connect to the Kafka server to detect new topics and automatically initiate the ingestion process into Druid, utilizing features such as schema auto-detection to simplify data processing. The blog provides a comprehensive guide, complete with implementation code, to help organizations streamline their data pipelines and harness the power of their data for improved business performance.
Oct 29, 2025
2,935 words in the original blog post.
Imply Lumi is introduced as the industry's first Observability Warehouse, designed to address the growing challenges of data retention, search speed, and cost in observability platforms, which have become increasingly strained under modern data and AI/ML workloads. By offering a high-performance, shared data layer, Imply Lumi aims to overcome the architectural limitations that force organizations into trade-offs between data fidelity and insight speed. It draws parallels to the evolution of Business Intelligence (BI), which transitioned from siloed systems to a decoupled architecture that fueled its growth. Imply Lumi supports native integrations with tools like Splunk, Grafana, and AI platforms, allowing organizations to maintain existing workflows while benefiting from enhanced efficiency and reduced costs. Users can store extensive datasets, run complex queries, and leverage AI without disrupting current operations, enabling them to scale and adapt with confidence.
Oct 28, 2025
1,001 words in the original blog post.
The article explores the evolution of data processing from traditional transaction-focused systems to event-oriented infrastructure, highlighting the differences between events and transactions in data management. Initially, the high cost of computing and storage led to the development of On-Line Transaction Processing (OLTP) systems that prioritized the current state, while On-Line Analytics Processing (OLAP) later emerged to automate transaction analysis. As technology advanced, the distinction between transactions and events became more pronounced, with events representing immutable occurrences and transactions reflecting changeable states. The rise of event-native infrastructure, driven by the growth of the internet and streaming systems like Kafka, allows for the real-time analysis and storage of events. This shift is exemplified by companies like Amazon, which use event data to gain insights into customer behavior and improve e-commerce strategies. While Hybrid Transactional/Analytic Processing (HTAP) databases aim to integrate OLTP and OLAP, the need for specialized event-oriented systems persists, particularly for large-scale and real-time applications.
Oct 28, 2025
2,347 words in the original blog post.
Elasticsearch and Druid, while both managing large datasets, serve distinctly different purposes, with Elasticsearch specializing in search functionality and Druid excelling in real-time analytics. Elasticsearch, based on Apache Lucene, is designed for enterprise search, offering relevance-based results and flexibility in data structuring, ideal for scenarios where search semantics are prioritized over performance and concurrency. It ingests data into indexes stored in shards across clusters, using a JSON-based query language. In contrast, Druid, an Apache Software Foundation project, is optimized for speed and scale in real-time analytics, handling both streaming and batch data with subsecond response times, high concurrency, and continuous availability. It supports SQL queries and is particularly suited for applications requiring immediate data availability and interactive data exploration, as demonstrated by Nielsen Research's transition to Druid for improved performance and cost efficiency. While some use cases can employ either tool, such as operational visibility and IoT analytics, the choice between Elasticsearch and Druid hinges on specific requirements like query language preference, concurrency needs, and the importance of real-time data processing.
Oct 28, 2025
1,211 words in the original blog post.
Apache Druid 29.0, an open-source distributed database for real-time analytics, introduces enhancements in performance, ecosystem integration, and SQL standard compliance. The latest release supports numerical columns for EARLIEST/LATEST aggregators during data ingestion, improving data modeling for queries. The multi-stage query engine now includes broader cloud support, allowing Azure and GCP blob storage for fault tolerance and enabling CSV export to S3. SQL capabilities have expanded with PIVOT/UNPIVOT and unequal joins, while window functions have seen improvements. Druid 29.0 also features new extensions like Spectator Histogram and DDSketch, the latter offering better accuracy at quantile ends. Additionally, Druid can now ingest system field information, such as parquet partition folder names, and supports data sourcing from Deltalake, easing integration with existing data platforms. The release is part of a broader effort to boost performance, enhance compatibility with cloud ecosystems, and align with SQL standards, encouraging user feedback through community channels.
Oct 28, 2025
1,378 words in the original blog post.
The text explores how Rivian, known for its electric vehicles, and Thing-it, a smart asset management platform, utilize Apache Druid to handle the immense data challenges posed by the Internet of Things (IoT). As both companies expand, they face the need for faster query responses and greater data management efficiency, prompting their transition to Druid from previous databases. Rivian benefits from Druid's ability to rapidly process telemetry data and support over-the-air updates for its vehicles, while Thing-it leverages Druid to simplify developer tasks and enhance analytics for building management. Both companies deploy Druid using Amazon's Elastic Kubernetes Service (EKS) and appreciate its scalability and support for real-time analytics. Future plans include Rivian's aim to broaden Druid usage to handle growing data volumes, and Thing-it's interest in time series analytics for richer data insights. Through these efforts, both companies seek to achieve greater operational efficiency and data-driven decision-making.
Oct 28, 2025
1,570 words in the original blog post.
Apache Druid and ClickHouse are both open-source databases optimized for real-time analytics, offering unique features and capabilities that cater to different use cases. Druid is ideal for applications requiring high concurrency, automatic indexing, and the ability to handle both batch and stream data ingestion with schema auto-discovery, making it well-suited for dynamic data environments and high-priority queries. It supports a "query laning" feature that prioritizes different workloads and offers continuous backup through deep storage, ensuring zero data loss. Conversely, ClickHouse, developed by Yandex, is known for its monolithic architecture that consolidates all functions into a single server, using batch ingestion with primary and secondary indexes that require manual management. While it supports fewer concurrent queries and lacks Druid's automatic schema adjustments and backup capabilities, ClickHouse offers an extensive range of integrations and simplicity for stable environments with minimal schema changes. Both databases have specific strengths and limitations, making them suitable for different types of real-time analytics needs.
Oct 28, 2025
2,255 words in the original blog post.
The 2024 Druid Summit, held at the Grand Bay Hotel in Redwood City, highlighted the dynamic and collaborative nature of the Apache Druid community and its role in advancing data analytics at scale. The event featured presentations and discussions emphasizing Druid's capability to handle massive data volumes efficiently, with contributions from industry leaders like Netflix and Salesforce. Key advancements such as the Multi-Stage Query and the announcement of Apache Druid 31 with new experimental features were unveiled, signaling Druid's growing importance in modern data ecosystems. Attendees expressed enthusiasm for Druid's potential, particularly its future innovations like Projections, Dart, and a Virtual Storage Layer, which promise significant enhancements in query speed and data management. The summit underscored the community's commitment to innovation and the belief that Druid will continue to shape the future of event-driven analytics.
Oct 28, 2025
793 words in the original blog post.
MongoDB, a favored NoSQL database for online transactional processing, is often transitioned to Apache Druid for enhanced analytical capabilities as data scales. This blog elucidates the process of migrating data from MongoDB to Druid, highlighting steps such as connecting to MongoDB, extracting data, and ingesting it into Druid. Druid, designed for high-performance analytical queries, uses a columnar format and features an indexing engine and segmented data storage to significantly improve query performance. The guide provides a Python script for data extraction from MongoDB, storing it in CSV format, and creating an ingestion specification for Druid. It also discusses using Change Data Capture (CDC) methods for ongoing data updates and explores tools like MongoDB Change Streams and ETL solutions for managing these updates efficiently. Druid's real-time data stream support and focus on swift analytical queries make it a powerful choice for real-time analytics, enabling faster data-driven decision-making.
Oct 28, 2025
1,684 words in the original blog post.
Apache Druid 30.0 introduces significant enhancements across three key areas: ecosystem integrations, performance, and SQL capabilities. This release, incorporating over 409 commits from 50 contributors, improves data ingestion with updates to the Supervisors view, byte-based guardrails for Amazon Kinesis, and parallel segment creation for Kafka. Druid 30 also introduces experimental support for Rabbit MQ Superstream and enhanced Deltalake ingestion capabilities. Performance upgrades include support for GROUP BY and ORDER BY on complex columns, optimized IN and AND filters, and improvements in concurrent append and replace operations. A new centralized datasource schema reduces startup time by minimizing resource contention. The release supports SQL TABLE(APPEND) syntax for UNION operations and introduces experimental improvements to SQL window functions and dynamic parameter support for arrays. These developments aim to enhance Druid's scalability, reliability, and efficiency in handling real-time analytics.
Oct 28, 2025
1,978 words in the original blog post.
At Splunk .conf25, the focus was on enhancing digital resilience in the AI era, emphasizing the importance of data visibility and smarter response times. The event marked the debut of Imply Lumi, an Observability Warehouse designed to address the challenges of data accessibility and cost-efficiency in observability. By partnering with Splunk, Imply Lumi aims to reduce storage costs and improve data search speeds, enabling Splunk admins to keep more data accessible without increasing expenses. This collaboration allows for deeper insights and expanded use cases while maintaining familiarity with existing Splunk workflows. Attendees witnessed Imply Lumi in action, experiencing faster data retrieval and reduced storage footprints, which highlighted the potential for enhanced data handling and AI/ML workflow integration. The event set the stage for future advancements, with plans to return to .conf26 with more demonstrations and solutions for maximizing data potential.
Oct 28, 2025
678 words in the original blog post.
As applications grow in complexity and scale, organizations must decide whether to build or buy an observability solution, a decision influenced by factors such as cost, infrastructure complexity, scale, and the need for customization. Off-the-shelf solutions may offer quick deployment but could incur hidden costs and lack scalability for large data volumes, while building an in-house platform may reduce costs and better handle complex architectures. Apache Druid is highlighted as a robust choice for companies opting to build their own observability platform, offering fast data retrieval, compatibility with streaming technologies, and scalability to manage massive data ingestion efficiently. Salesforce's transition to Druid exemplifies the benefits of this approach, including significant storage savings and enhanced data exploration capabilities, which enable real-time insights and improved operational efficiency. Ultimately, the decision to build or buy hinges on an organization's specific needs, lifecycle stage, and long-term goals.
Oct 28, 2025
1,401 words in the original blog post.
KakaoBank, a South Korean digital bank, leverages Imply's Apache Druid and Pivot for advanced financial analytics to handle the vast amounts of data generated by its mobile-first platform. The bank, known for transforming traditional banking with its seamless services, uses Imply to tackle challenges associated with high data resolution and concurrency in financial data analysis. This system allows for the processing of numerous ad-hoc queries on massive datasets, enhancing the bank's ability to analyze transactional trends at a granular level. Previously reliant on formalized procedures for data requests, KakaoBank's adoption of Imply has enabled more dynamic and interactive data visualizations, reducing the workload for data analysts and empowering business users to conduct independent analyses. As a result, the bank aims to broaden the use of Imply across its organization to foster a deeper understanding of financial data, ultimately gaining a competitive advantage through improved analytical capabilities and data comprehension.
Oct 28, 2025
863 words in the original blog post.
The blog post explores the implementation and use of the UNNEST function in Apache Druid, which allows for the flattening of array-type columns into individual rows for more effective data analysis. This functionality is crucial for applications requiring the analysis of patterns from dense data representations, such as user behavior on websites. Druid's UNNEST function works by processing data at the segment level, converting array-type columns into rows of individual elements, which can then be aggregated and filtered. This approach is enhanced by the use of virtual columns, storage adapters, and cursors to efficiently manage and filter data. The blog also highlights the integration of Druid's UNNEST function with SQL through Apache Calcite, allowing users to perform operations similar to those in other databases. The implementation uses specific planning rules and query syntax to support complex operations like multiple levels of unnesting and filtering. Additionally, the post provides examples of how UNNEST can be applied in practice and acknowledges the contributions of various team members in bringing this feature to the Druid community.
Oct 28, 2025
4,355 words in the original blog post.
In the comparison of data storage formats for analytics, Apache Parquet, Apache Iceberg, and Apache Druid segments each offer distinct advantages tailored to specific use cases. Parquet is recognized for its efficient columnar storage, making it ideal for batch processing and read-intensive queries, particularly within systems using Apache Hadoop or Spark. Iceberg builds on Parquet's capabilities by adding schema evolution and transactional support, which are beneficial for managing large and dynamic datasets that require concurrent reads and writes. However, its snapshot-based approach may not be suitable for real-time analytics. Druid segments excel in environments demanding real-time data ingestion and immediate query performance, making them perfect for applications like user behavior analytics and financial fraud detection. Druid's architecture supports high concurrency and low-latency queries, offering immediate data visibility and scalability for real-time insights. Understanding each technology's strengths and limitations is crucial for aligning data storage strategies with organizational needs and workload demands.
Oct 28, 2025
3,086 words in the original blog post.
Apache Druid is an open-source real-time analytics database designed to handle high queries per second (QPS) efficiently, even with complex aggregations and filters across high-dimensional and high-cardinality data. It excels in processing interactive, sub-second queries by optimizing data storage and retrieval through time-series data organization and advanced indexing techniques like dictionary encoding and inverted indexes. Druid employs a scatter/gather technique, allowing for parallel processing across multiple computing nodes, and extensively uses multithreading with specialized thread pools to optimize performance under heavy loads. The database further enhances concurrency and high availability through fine-grained segment-level data replication, enabling concurrent processing on different nodes without manual management. With a focus on CPU efficiency and dynamic scaling, Druid supports diverse analytics queries across numerous segments and nodes, making it a popular choice for companies needing robust analytics capabilities. Imply's implementation of Druid has notably excelled in proof-of-concept challenges against competitors, demonstrating its capacity to deliver high QPS and quick scalability.
Oct 28, 2025
1,573 words in the original blog post.
With the rapid proliferation of IoT devices, effective real-time monitoring has become crucial for maintaining the performance, security, and efficiency of interconnected smart systems. IoT monitoring involves the identification, collection, organization, analysis, and management of data from a diverse array of devices, which include sensors, controllers, and actuators. This process not only aids in troubleshooting and preventing issues such as latencies and outages but also enhances safety, predictive maintenance, cost optimization, and resource efficiency. The sheer scale of data generated by IoT devices presents challenges in terms of volume, velocity, and variety, necessitating robust data storage and management solutions. Advanced analytics and real-time querying are essential for deriving actionable insights, while data visualization and alerting systems help stakeholders make informed decisions rapidly. IoT monitoring solutions must be scalable and capable of handling diverse data formats, real-time data ingestion, and late-arriving data to ensure timely and effective responses to the dynamic IoT environment.
Oct 28, 2025
4,288 words in the original blog post.
Apache Druid utilizes a specialized SQL dialect built on Apache Calcite to simplify its native query interface, allowing for efficient data ingestion and query execution. Unlike traditional SQL INSERT operations, Druid is optimized for ingesting large volumes of data from external sources, with a unique focus on time-based partitioning using a special __time column. The ingestion process is enhanced through primary and secondary partitioning, aimed at improving query performance by organizing data into segments based on time intervals and clustering dimensions. To accommodate these requirements, Druid introduces extended SQL syntax with PARTITIONED BY and CLUSTERED BY clauses, as well as a REPLACE statement for updating data within specified time periods. These extensions are integrated into the Calcite framework without altering its core, leveraging JavaCC for parsing and allowing for seamless upgrades. The process involves parsing SQL statements into a syntax tree, optimizing them through relational expressions, and ultimately converting them into native Druid queries for execution, showcasing the system's efficiency in handling complex data transformations and real-time analytics.
Oct 28, 2025
2,052 words in the original blog post.
Apache Druid leverages partitioning and pruning techniques to enhance its performance, scalability, and efficiency for real-time analytics. The primary partitioning is often based on time dimensions, where data is split into time chunks to facilitate parallel processing and segment pruning, which allows only necessary segments to be processed during queries. Secondary partitioning, achieved through the SQL CLUSTERED BY clause, further subdivides data based on additional dimensions, enhancing pruning and thus improving query performance and system efficiency. This method is particularly beneficial when dealing with large datasets or skewed distributions, as it helps in avoiding hotspotting by balancing segment sizes. By using SQL-based ingestion, users can control time granularity and optimize dimension-based pruning, making it straightforward to tailor data organization to analytical needs while maintaining robust performance.
Oct 28, 2025
1,784 words in the original blog post.
Real-time data is characterized by its immediate delivery from the source to end users or applications, with minimal processing to ensure low latency and high speed. It is crucial in time-sensitive applications like nautical navigation and patient monitoring, where delays can have significant repercussions. Real-time data comes in two forms: event data, which captures specific incidents, and streaming data, a continuous data flow. Real-time analytics, which provides timely insights from this data, is essential for making rapid decisions in sectors such as finance, security, and IoT. The benefits of real-time data include enhanced decision-making, improved operational efficiency, better customer experiences, and competitive advantages. However, managing real-time data involves challenges like handling high data volume and velocity, minimizing latency, ensuring data quality, and integrating diverse data types while maintaining security and compliance. Technologies like Apache Druid are designed to address these challenges by enabling fast and scalable data processing and analysis, supporting applications across various industries.
Oct 28, 2025
4,554 words in the original blog post.
Apache Druid 31.0 marks a significant advancement in the open-source distributed database, aimed at enhancing real-time analytics at scale. This release features over 525 commits from 45 contributors and introduces a multi-profile era, allowing for customizable performance and cost adjustments to accommodate diverse use cases. Notably, it introduces the new Dart query profile, designed to handle highly complex queries, such as high-cardinality GROUP BY and large joins, using a revolutionary query engine that leverages multi-threaded workers and in-memory shuffles. Enhancements also include support for SQL window functions within the multi-stage query (MSQ) engine, offering solutions for categorized analysis like cumulative sums and trend analysis. Additionally, Druid 31 improves data compaction processes with features such as concurrent append and replace, supervisor-based compaction scheduling, and MSQ-based auto-compaction, all aimed at optimizing query efficiency and resource management. The release also enhances the Explore view for interactive data exploration and includes an access control UI, broadening data accessibility for non-SQL users. With these updates, Druid 31 aims to expand its utility and efficiency for developers and analysts alike, while continuing to evolve its features and address any stability or functionality gaps in future releases.
Oct 28, 2025
1,077 words in the original blog post.
To address the challenges of scaling complex data queries efficiently, Apache Druid has incorporated two community extensions, Netflix's Spectator Histogram and Kong's DDSketch, to enhance statistical analysis. Spectator Histogram offers storage optimization for percentile calculations on positive integer values, making it particularly effective for common metrics in cloud services and web applications by reducing data size without compromising accuracy. This extension is especially beneficial for scenarios requiring efficient data representation, although it is limited to positive integers. On the other hand, DDSketch focuses on improving accuracy at the extremes of statistical distributions, specifically for high-percentile measurements like P90 and P10, which are crucial for latency measurement. DDSketch prioritizes true value error over rank error, providing a more stable and memory-efficient solution for high-cardinality data. Both extensions offer significant improvements in storage and computational efficiency, making them valuable tools for real-time data analysis and interactive queries within constrained memory environments.
Oct 28, 2025
1,322 words in the original blog post.
Druid Summit 2024 is set to take place on October 22 in San Francisco at the Grand Bay Hotel, marking its return to an in-person format after being canceled in 2020 due to the pandemic. This one-day conference offers developers, engineers, and data architects a chance to engage with new Apache Druid features, ecosystem integrations, and Imply products. Attendees can participate in hands-on workshops, network with expert users, and hear from organizations like Netflix and Salesforce about their Druid use cases. The event's agenda is organized around four themes: Deployment and Operations, Streaming, Lakehouse, and User Experience, with sessions covering topics like Kubernetes deployment, Apache Kafka ingestion, and data interaction tools such as Imply Pivot. The summit will also feature panel discussions, keynotes from Apache Druid PMC chairs, and an afterparty, promising a comprehensive and engaging experience for the Druid community.
Oct 28, 2025
601 words in the original blog post.
Apache Druid has introduced a new technique called incremental encoding, specifically front-coding, to optimize the storage and query performance of STRING columns within its segments. This method focuses on reducing segment sizes by storing only the suffixes of string values after identifying common prefixes, thereby enhancing the efficiency of memory usage and minimizing disk reads. The initiative capitalizes on the structure of Druid's dictionary encoding, which traditionally maps distinct string values to integer identifiers, to achieve significant space savings, particularly for data with large common prefixes like URLs. The front-coding approach has proven promising in preliminary tests, offering space savings without compromising read performance, and is slated to potentially become the default setting in future Druid releases. This change is part of ongoing efforts to refine Druid's segment format and explore further improvements, such as adaptive encoding strategies based on data characteristics. Users are encouraged to experiment with this new encoding strategy, albeit with caution regarding compatibility with older Druid versions, and provide feedback to guide future enhancements.
Oct 28, 2025
2,382 words in the original blog post.
Imply Pivot is a high-speed data exploration tool designed to transform self-service analytics by leveraging Apache Druid's capabilities, offering real-time interaction with massive datasets through an intuitive drag-and-drop interface. Unlike traditional BI tools that limit users to pre-aggregated data and predefined dashboards, Pivot allows for flexible, on-the-fly analysis with data cubes that enable users to drill down into details and explore data dynamically. The tool's user-friendly design eliminates the need for SQL expertise, making it accessible to a broader range of users and supporting a variety of visualization options to enhance data-driven decision-making. Pivot's seamless white-labeling and embedding features allow businesses to integrate analytics into their branded experiences, as demonstrated by companies like Twitch, ironSource, and GameAnalytics, which utilize Pivot to empower teams and customers with real-time insights. Additionally, Imply offers a fully managed Druid DBaaS called Polaris, combining Druid's architecture with Pivot's interface, enabling users to build analytics applications quickly.
Oct 28, 2025
1,308 words in the original blog post.
Imply Polaris introduces the use of upserts and Dimension Tables to enhance data management and analytics capabilities, focusing on data consistency and efficiency. Upserts in Polaris streamline database operations by combining insert and update actions, while Dimension Tables, a smaller version of upserts, store the latest snapshot of attribute data that evolves over time. These tables, which are integrated with event streaming pipelines like Kafka, allow for near-instant data updates and can be joined with event data for real-time analytics. Unlike traditional Lookup tables in Apache Druid, Dimension Tables simplify setup and maintenance, effectively leveraging JOINs to manage changing dimension values without the scalability issues associated with large datasets. The feature is already in use by companies such as MetaCX to achieve live identity normalization. The introduction of Dimension Tables marks the beginning of an Upsert roadmap, with future enhancements planned to support increased event throughput and comprehensive CDC input sources, aiming to enable analytics at a larger scale by facilitating the joining of decorative metadata with event transactions.
Oct 28, 2025
833 words in the original blog post.
Imply has introduced a series of interactive tutorials to help users learn Apache Druid through Jupyter Notebooks, which combine code and Markdown text, allowing users to explore Druid API fundamentals without context switching. The tutorials cover the Druid REST API, the Druid Python API, and Druid SQL, and can be accessed either individually or via a Docker container along with Apache Kafka. To facilitate ease of use, these tutorials can be run locally or through Docker Compose, which allows for various service combinations and easy updates with new Druid versions. Imply encourages community contributions to expand these educational resources, which are available in the Apache Druid GitHub repository.
Oct 28, 2025
618 words in the original blog post.
Apache Druid 27.0.0 introduces several enhancements aimed at improving stability and scalability, including Smart Segment Loading and an upgraded schema auto-discovery feature. The new release adds the ability to run asynchronous queries, allowing for efficient handling of long-running, resource-intensive queries by querying data directly from deep storage without pre-caching. This feature enables a more flexible allocation of compute and storage resources. Additionally, the release graduates the type-aware schema auto-discovery to general availability, implements a memory-based subquery limit for better memory management, and integrates support for reading data from Apache Iceberg. Platform compatibility has also been improved, with better Graviton support and official Java 17 compatibility, and enhancements to the Kubernetes native ingestion experience. The update encourages users to test these features in production use cases and provide feedback to the Druid community.
Oct 28, 2025
1,114 words in the original blog post.