January 2026 Summaries

Launching an embedded analytics feature from prototype to production can reveal significant performance challenges, particularly with latency and multi-tenant issues, which aren't simply resolved by choosing the fastest database. Key considerations for SaaS embedded analytics include handling high concurrency, multi-tenant isolation, resource management to prevent "noisy neighbors," and maintaining sub-second latency under load. Databases like Apache Pinot, Druid, and ClickHouse are designed to address these challenges with features like query laning, tenant concepts, and resource quotas. They excel in specific scenarios, such as user-facing analytics, operational dashboards, and high-concurrency environments, but require careful configuration and maintenance. Alternatively, platforms like Tinybird aim to simplify these complexities by automatically managing multi-tenant isolation and optimizing query performance, allowing teams to focus on delivering analytics features rather than database management. The decision on which system to use should be based on specific operational needs, concurrency requirements, and the team's capacity to manage database intricacies.

The integration of Azure Event Hubs with ClickHouse for high-throughput, real-time analytics can lead to performance issues, such as increased query times and consumer lag, due to a misunderstanding of Event Hubs' capabilities. Event Hubs excels at durable event ingestion compatible with Kafka but wasn't designed for sub-100ms analytics serving, which is ClickHouse's strength. The integration typically involves three patterns: direct consumption using the ClickHouse Kafka table engine, using Kafka Connect as an integration hub, or leveraging managed ingestion through ClickHouse Cloud ClickPipes. Each pattern has its advantages depending on operational complexity, throughput needs, and team expertise. Critical considerations include understanding Event Hubs' at-least-once delivery semantics, managing consumer offsets, dealing with throughput limits and throttling, and maintaining data quality through deduplication strategies. For teams seeking operational simplicity with real-time analytics, platforms like Tinybird offer managed ClickHouse with built-in streaming capabilities, eliminating the integration complexity while maintaining sub-second data serving for APIs and dashboards.

Azure Synapse, a data platform that efficiently orchestrates data transformations using Spark pools, manages data lake storage with ADLS Gen2, and provides SQL analytics capabilities, can face performance challenges when deployed for real-time analytics serving. Although Synapse is adept at orchestrating complex ETL workflows and managing data lake governance, it struggles with high concurrency workloads and sub-second query performance required for customer-facing dashboards. The integration with ClickHouse, known for low-latency query serving, offers a solution through various patterns, including using Synapse Spark with ClickHouse connectors or ADLS Gen2 as a staging layer. While these integrations provide specific benefits, they require significant engineering efforts, such as connector configuration, network setup, and schema mapping. Alternatively, platforms like Tinybird offer managed solutions with built-in data pipeline capabilities that simplify integration complexities, focusing on real-time analytics and SQL transformations without the need for Spark jobs, thus catering to teams prioritizing operational simplicity and sub-100ms serving for dashboards and APIs.

The text discusses the challenges and solutions associated with integrating AWS Glue and ClickHouse for data processing and analytics. It highlights the limitations of using AWS Glue for real-time analytics due to its batch processing nature, which can lead to latency issues in dashboard queries and real-time metrics. To address these challenges, the text outlines three integration patterns: using Glue for ETL processes to load data into ClickHouse for fast queries, employing Glue Data Catalog for managing metadata while ClickHouse queries data directly from Iceberg tables in S3, and combining both approaches in a hybrid architecture. These strategies aim to optimize the balance between batch processing and real-time analytics, ensuring efficient data transformation and query performance. Additionally, it introduces Tinybird as an alternative for real-time analytics with sub-second latency, emphasizing its streaming ingestion capabilities and SQL-based transformations, which simplify operations compared to traditional Glue setups. The decision between these approaches depends on specific organizational needs, such as real-time requirements, governance, and existing infrastructure investments.

The text discusses the challenges and solutions associated with integrating Azure Data Factory (ADF) with ClickHouse for analytics workflows, highlighting the limitations of ADF in real-time analytics serving. While ADF excels in orchestrating complex data workflows across Azure services through visual pipelines and managed connectors, it is not designed for sub-second query performance required for dashboards and customer-facing analytics. To integrate ClickHouse with ADF, three patterns are typically employed: staging data in Azure Blob Storage, pushing data via the HTTP interface, and orchestrating compute resources for complex scenarios. These patterns are selected based on factors like data volume and operational complexity, each offering distinct benefits and challenges. However, the text emphasizes that these methods require significant engineering efforts, including REST connector configuration, async insert tuning, and private network setup. Alternatively, Tinybird is introduced as a managed ClickHouse platform offering simpler real-time ingestion and transformation capabilities without the need for complex pipeline orchestration, suitable for teams prioritizing real-time analytics.

Tinybird Code is an AI-powered CLI tool that facilitates the development of Tinybird projects, offering structured skills compatible with various AI coding agents like Amp, Claude Code, and Codex. By installing the tinybird-best-practices skill, these agents can automatically apply a set of 20 rules that guide users through the entire Tinybird development lifecycle, including schema design, query optimization, testing, and deployment. The skill provides detailed instructions on project structure, datasource definitions, pipe and endpoint setups, materialized views, data operations, and optimization strategies, with practical code examples to demonstrate best practices. This tool, used by companies like Vercel and Canva, aims to enhance project efficiency by optimizing schema decisions and sorting keys to significantly reduce query latency. It emphasizes flexibility by allowing users to override its suggestions and offers a platform for community contributions to improve its functionality further.

The integration of Amazon S3 with ClickHouse allows for three distinct architectural patterns—data lake queries, batch ingestion, and tiered storage—each with unique performance characteristics. These patterns facilitate querying data lake files directly without loading them, using S3 as a batch ingestion source for MergeTree tables, and employing S3-backed MergeTree for tiered storage. Challenges arise due to common misunderstandings about these patterns, leading to issues such as high latency, cache inefficiencies, and unexpected costs from data transfers. While the s3() function supports exploratory analytics and infrequent queries, it lacks optimizations like indexes and caching, which are essential for production analytics. To optimize performance, considerations such as file size, network topology, and cache management are crucial. Tinybird offers a simplified approach by automatically handling ingestion optimization and caching, reducing the operational burden and delivering analytics from S3 data without requiring in-depth expertise in ClickHouse or AWS storage configurations. This approach highlights the importance of aligning data access patterns with S3's strengths and choosing solutions based on operational capacity and latency requirements rather than merely focusing on feature capabilities.

The integration of Amazon Kinesis with ClickHouse for real-time data analytics can be complex, with various architectural paths offering different trade-offs in terms of latency, replay capability, and operational complexity. While managed ingestion through ClickPipes simplifies operations by handling the entire data flow from Kinesis to ClickHouse Cloud, allowing for minimal operational overhead, the Kinesis → Firehose → S3 → ClickHouse path provides robust replay capabilities at the cost of introducing buffering latency. Alternatively, integrating Kinesis with Kafka before ClickHouse can leverage existing Kafka infrastructure but adds complexity. Most challenges in these integrations arise from misunderstanding Kinesis's resource model, such as shard limits and partition key choices, leading to issues like throttling and consumer lag. Teams must choose their path based on their operational capacity and the specific needs of their analytics workload, balancing the need for real-time capabilities with the practicalities of managing complex data pipelines. Solutions like Tinybird offer an alternative by eliminating much of the operational complexity, turning SQL queries into APIs and automating Kinesis data stream management, thereby allowing engineering teams to focus more on building analytics features rather than managing infrastructure.

Integrating Amazon MSK with ClickHouse for real-time analytics involves complex network and authentication configurations in AWS rather than issues with Kafka or ClickHouse itself. The primary challenges lie in AWS networking layers, including VPC architecture, security groups, and compatible authentication models, which are crucial for determining the success of the integration. ClickHouse offers two integration paths with MSK: the native Kafka Engine, which pulls data directly, and the MSK Connect Sink, which uses external workers to push data. The native Kafka Engine is simpler, requiring fewer operational components but demands correct network configurations. MSK Connect Sink involves managing more complex networking layers like VPC peering and requires Kafka Connect expertise. Authentication issues, particularly around IAM and SASL/SCRAM mechanisms, can impede integration, with SCRAM being a more reliable choice for compatibility. Performance tuning in ClickHouse involves managing consumer numbers and batch sizes to optimize throughput versus latency. Many teams face operational challenges with these integrations, and platforms like Tinybird are emerging to simplify the process by providing managed solutions that eliminate the need for deep AWS and networking expertise, thereby enabling faster and more efficient real-time data ingestion and analytics.

Tinybird has introduced support for querying the ClickHouse database using its native HTTP interface, enhancing connectivity with BI tools, SQL clients, and ORMs without needing additional plugins. This development replaces the deprecated Postgres-based BI connector, offering a more efficient integration method and providing direct access to data with the same performance and security guarantees as the existing API endpoints. As ClickHouse has gained popularity, the new interface allows users to connect directly to Tinybird, making it easier to build observability dashboards, explore data, and connect BI tools like Grafana, Superset, and Metabase. The interface, built on Tinybird's /v0/sql endpoint, supports system tables for metadata introspection, provides real-time observability over queries, and allows reuse of Tinybird pipes in BI tools. While it currently supports only read-only queries, further enhancements such as advanced system table support and self-hosted infrastructure are anticipated, simplifying integration and offering more flexibility in data querying and visualization.

The author shares extensive insights and lessons learned from years of managing ClickHouse clusters, focusing on the challenges and best practices for maintaining such systems, particularly in the context of a company like Tinybird. They highlight the ease of setting up a ClickHouse cluster but emphasize the complexities of keeping it operational, especially when dealing with petabyte-scale clusters and high query loads. The discussion covers the architectural choices, such as the use of replicas and shards, and the shift towards cloud storage for cost efficiency and management advantages. The author also delves into the intricacies of data ingestion, pointing out common pitfalls like data duplication and system overloads, and suggests strategies for balancing batch sizes and managing merges. Additionally, the text touches on the costs associated with running ClickHouse clusters, the need for skilled personnel to manage these systems, the importance of careful configuration, and the challenges of upgrading the database without downtime or data loss. There is also a focus on the limitations of ClickHouse's cloud storage capabilities compared to other systems, and the importance of testing configurations and monitoring system performance to ensure stability and efficiency.

Log analysis at scale, especially in multi-tenant architectures, presents unique challenges that go beyond just storing and searching logs. This discussion emphasizes the importance of effective log ingestion, storage, and retrieval in user-facing systems. Different approaches to log ingestion include direct logging, utilizing a sidecar container, and hybrid methods that accommodate evolving needs. For storage, managing multi-tenancy effectively requires strategies like separating storage and compute, or identifying logs with unique client IDs to avoid inefficiencies. Efficient log retrieval involves pre-calculating aggregates to provide quick insights and leveraging pagination to reduce latency, ensuring users can explore logs swiftly without overwhelming systems. While free text search over logs remains complex, tools like Tinybird offer scalable solutions for log analytics, as demonstrated by companies like Vercel. The overall emphasis is on choosing methods that balance simplicity, cost-efficiency, and scalability to enhance the user experience in log exploration and analysis.

ClickHouse® is an open-source, column-oriented SQL database, ideal for real-time analytics, originally developed by Alexey Milovidov at Yandex for Yandex.Metrica, and now widely used by companies like Uber and Spotify. Tinybird, founded in 2019, builds on ClickHouse® to offer a real-time data platform with added infrastructure and services, making it easier for developers to create user-facing analytics. The primary difference between Tinybird and ClickHouse Cloud lies in the level of abstraction and control; Tinybird provides higher-level abstractions that reduce developer friction and expedite production, whereas ClickHouse Cloud offers more granular control over database settings. While both platforms simplify database management and support multiple clouds, Tinybird focuses on minimizing development time with features like single-click API generation and built-in observability tools, whereas ClickHouse Cloud provides more configuration flexibility and is suitable for teams with deep ClickHouse® expertise. The choice between the two depends on specific requirements, with Tinybird suited for those prioritizing ease of use and integrated services, and ClickHouse Cloud for those needing detailed control and customization.

ParadeDB is a PostgreSQL extension designed to provide Elasticsearch-quality search capabilities directly within Postgres, avoiding the need for separate search engines and maintaining ACID guarantees. However, this approach can strain the database's resources and may not be ideal for large-scale analytics. Alternative solutions often fall into two categories: those that extend Postgres with different features and those that use external systems optimized for specific tasks. Tinybird, built on ClickHouse®, is recommended for teams needing analytics at scale, offering real-time data processing, instant API publication, and managed infrastructure, effectively separating transactional workloads from analytical ones. Other options include PostgreSQL's native full-text search for simplicity, pg_trgm for fuzzy search, and external systems like Elasticsearch for robust search capabilities. Meilisearch and Typesense prioritize ease of use and fast integration, while Quickwit focuses on log analytics with cost-efficient storage. The choice of alternative depends on specific needs, whether it's full-text search, real-time analytics, or handling massive data volumes efficiently.

MongoDB is an effective database solution when flexibility, rapid iteration, and ecosystem access are priorities, but its performance may falter with analytics, cost management, or operational complexity, prompting exploration of alternatives. Tinybird emerges as a key choice for teams requiring real-time analytics, providing sub-100ms query performance by leveraging ClickHouse's capabilities, and allowing seamless integration with existing MongoDB operations through Change Data Capture (CDC). Other alternatives like Amazon DocumentDB, Azure Cosmos DB, and PostgreSQL offer varying degrees of compatibility and features, each suited to specific use cases such as global distribution, transactional needs, or document flexibility combined with SQL power. The text emphasizes understanding the specific limitations of MongoDB in the context of the intended workload and choosing the right tool, whether that involves enhancing MongoDB with an analytical layer like Tinybird, or opting for other database solutions like FerretDB, DynamoDB, or Elasticsearch, based on the unique requirements of the project.

Choosing between Bring Your Own Cloud (BYOC) and fully managed deployment options for ClickHouse® is crucial for real-time analytics, affecting compliance, operational burden, and costs. BYOC allows ClickHouse® to run as a managed service within a user's cloud account, offering control over infrastructure but involving complex responsibilities like managing the cloud environment and security. Fully managed services like Tinybird and ClickHouse® Cloud streamline deployment by handling infrastructure, allowing users to focus on data and API development. While BYOC can be beneficial for organizations with stringent data residency and compliance needs, it demands significant operational resources and lacks formal SLA guarantees. Fully managed solutions offer faster, simpler deployments with predictable costs and are ideal for teams prioritizing rapid feature development over infrastructure management. Tinybird, in particular, provides a complete real-time data platform that abstracts database management, enabling quick creation of APIs with minimal setup time, appealing to teams needing fast analytics without infrastructure complexity.

Wrapped features have become a ubiquitous year-end trend, popularized by companies like Spotify, GitHub, and Strava, and have now been implemented by Tinybird to provide a yearly recap for their customers. Built on Tinybird's managed ClickHouse infrastructure, the Tinybird Wrapped feature efficiently aggregates and processes vast amounts of data across multiple regions using a lambda architecture, which combines real-time and historical data, ensuring subsecond query responses and data deduplication. This architecture leverages S3 and Parquet as a data interoperability layer, allowing for seamless data collection and aggregation without significant operational overhead. The implementation includes robust privacy measures, such as JWT-based security and the use of public-safe identifiers, to protect customer data. The frontend application, crafted with Next.js and enhanced by GSAP animations, presents the data in an engaging and visually dynamic manner. The project showcases how Tinybird's existing infrastructure can be utilized to quickly develop a complex analytical feature by writing a few queries and building a straightforward, visually appealing interface.

AI agents with large language models (LLMs) have the potential to transform data analytics by providing intelligent responses to business queries, but they face significant challenges in handling analytical data. LLMs, trained to predict sequences in narrative texts, struggle with interpreting the multidimensional relationships in tabular data, leading to difficulties in generating accurate SQL queries and managing complex datasets. These models often underperform with long-running queries, exhausting context windows, and failing to grasp the specific data architectures of large organizations. Tinybird's implementation of an MCP server and the development of the Explorations UI demonstrate a solution by emphasizing the importance of context—both static and dynamic—to help LLMs map user intent to data more effectively. By documenting resources and employing semantic models, organizations can improve LLMs' understanding of data structures, allowing for more meaningful insights rather than just syntactically correct queries. The future of agentic analytics will belong to companies that can bridge the gap between human language and the intricate data environments, thus democratizing analytical thinking and making it quickly accessible to all users.

Managed data platforms offer comprehensive services that handle infrastructure, scaling, and operations, allowing teams to concentrate on data modeling and consumption. However, these platforms often optimize for batch analytics and BI rather than real-time analytics, which many businesses actually need. Tinybird, for example, provides a real-time analytics platform designed for streaming data ingestion and fast API serving, addressing the gap left by batch-focused platforms. Alternatives like Snowflake, Databricks, and Confluent Cloud cater to specific needs—such as data warehousing, lakehouse flexibility, or streaming-first architectures—while hyperscaler native platforms from AWS, Azure, and GCP offer integrated cloud services. Composable stacks allow for best-of-breed tools with greater flexibility but require more integration work. Governance platforms like Collibra and Alation focus on metadata management across systems rather than replacing data infrastructure. Ultimately, the choice of a managed data platform should align with the organization's actual data consumption patterns and operational capabilities, emphasizing the importance of understanding whether the primary requirement is batch analytics, real-time data serving, or event-driven processes.

Deciding whether to self-host ClickHouse® on Hetzner or utilize a managed service is crucial for deploying low-latency real-time analytics solutions. Hetzner is favored for its cost-effective infrastructure, being 50-70% cheaper than major cloud providers like AWS, while offering powerful bare-metal servers. However, this comes with the responsibility of handling all aspects of database management, from installation to incident response. Conversely, a variety of managed ClickHouse® services, such as Tinybird, ClickHouse® Cloud, Aiven, Altinity.Cloud, ByteHouse, AWS Marketplace options, and CelerData, provide alternatives that eliminate operational burdens by offering automatic scaling, integration ease, and expert support. These services vary in pricing models, scalability, and support, catering to different needs such as real-time data products, multi-cloud deployments, and complex query handling. Tinybird, for instance, offers a comprehensive real-time data platform that abstracts database management and focuses on data results, making it suitable for rapid deployment of user-facing analytics. The choice between Hetzner and managed services hinges on factors like technical expertise, cost considerations, compliance requirements, and the need for infrastructure control versus operational simplicity.

Apache Pinot, a distributed OLAP datastore, is designed for ultra-low latency user-facing analytics and high concurrency, but its operational complexity often prompts teams to seek alternatives. These alternatives include Tinybird, which simplifies real-time analytics with API publication without the need for complex infrastructure, and Apache Druid, which offers time-series OLAP with bitmap indexes for time-window queries. ClickHouse® provides a versatile columnar OLAP approach with a focus on physical data layout, while StarRocks offers an MPP warehouse solution optimized for complex SQL queries and joins. Google BigQuery and Snowflake represent traditional data warehouse options that trade real-time capabilities for operational simplicity. Trino addresses federated SQL requirements, allowing for cross-source queries, whereas OpenSearch and Elasticsearch cater to combined search and analytics needs, particularly for log data. The decision on which alternative to choose should be driven by specific workload requirements, operational complexity, and whether the focus is on real-time analytics delivery or specialized indexing strategies.

StarRocks is a Massively Parallel Processing (MPP) analytical database designed for real-time analytics with sub-second query latency, featuring complex SQL joins support and upsert capabilities for live data. It is known for its operational simplicity and support for both shared-nothing and shared-data deployments. While StarRocks is efficient for product analytics and dashboards, it may not suit every use case, prompting evaluations of alternatives based on specific requirements such as real-time analytics, operational simplicity, or complex joins. Among the alternatives, Tinybird offers a managed real-time data platform built on ClickHouse, abstracting database management to focus on data and APIs, while Apache Doris, ClickHouse, and Apache Druid provide various strengths in analytics workloads. Each alternative presents trade-offs in performance, complexity, and fit for use case, with decisions often influenced by factors like query latency, operational complexity, and time-to-value.

In a detailed exploration of high-speed data ingestion, the text describes the process of achieving Tesla's reported 1 billion rows per second ingestion rate using ClickHouse®, a columnar database management system. The author tests this claim on a MacBook M4 Pro, demonstrating that scaling ClickHouse® clusters by adding shards can linearly increase ingestion capacity. The setup involves creating a ClickHouse® cluster on Google Cloud Platform, configuring nodes, and optimizing data insertion techniques to achieve high throughput. The text also discusses the challenges of real-time ingestion, including handling failures, retries, and database upgrades, as well as balancing throughput, part size, and merges. It suggests that Tesla likely uses a combination of Kafka for data buffering and a real-time ETL process to manage data before ingestion into ClickHouse®, which is likely divided into multiple shards with various optimizations for efficient data processing. The author concludes that achieving such high ingestion rates requires careful architecture planning and possibly additional resources, indicating that this complex setup is more feasible for companies needing high-insert rate architectures.

Amazon S3, the most widely adopted data storage solution globally, has been integrated with Tinybird through the introduction of the S3 Sink, a feature that simplifies exporting analytical data products back into Amazon S3 without the need for external ETL processes. This integration allows Data Engineers to maintain consistency across user-facing dashboards, machine learning models, and internal reports by using Tinybird as the Source of Truth, facilitating seamless data transfer to Amazon S3 for further processing or sharing with other data systems. The S3 Sink has been extensively tested in production, enabling users to build real-time APIs and hydrate data lakes from a single source, while also offering a user-friendly setup process that includes creating Sink Jobs and connecting Tinybird to S3. Available for Tinybird users on Pro or Enterprise plans, the S3 Sink provides a comprehensive solution for managing and exporting data efficiently, with plans to expand to new Sink destinations in the future.

Building real-time applications involves selecting the right architecture and tools to ensure low latency, consistency, and scalability. Multiple approaches exist, each catering to different needs, such as Tinybird for real-time analytics, Socket.io and WebSocket frameworks for connection management, Apache Kafka for event-driven architectures, Firebase and Supabase for rapid prototyping with real-time capabilities, and managed services like Ably and Pusher for simplified messaging infrastructure. Choosing between these options depends on the specific real-time pattern being implemented, whether it's analytics serving, messaging, event processing, or data synchronization. Real-time analytics requires platforms optimized for data aggregation and serving, while messaging applications focus on connection infrastructure and coordination. It's crucial to separate concerns across transport, event backbone, and data serving layers to avoid a one-size-fits-all approach and ensure that the chosen architecture aligns with the application's requirements and the team's operational capabilities.

Real-time dashboards that perform well in demos often falter under production conditions due to architectural bottlenecks rather than issues with the charting library itself. The problem typically arises from sending excessive data points to the browser, leading to performance degradation in terms of data handling and rendering. The solution involves understanding end-to-end latency budgets and optimizing across four key layers: data volume from the backend, serialization and network transfer, client-side parsing and memory, and rendering and interaction. Techniques such as perceptually lossless downsampling, particularly using the Largest Triangle Three Buckets (LTTB) algorithm, adaptive resolution queries, and efficient payload strategies like binary formats and incremental updates, are crucial for maintaining performance. Additionally, selecting the appropriate rendering technology—Canvas for simplicity or WebGL for scale—and leveraging Web Workers for off-main-thread processing are essential. Platforms like Tinybird offer backend infrastructure designed for real-time analytics, enabling sub-100ms queries and efficient data handling, thus eliminating the need for complex custom solutions and ensuring fast charts that maintain their performance as data scales.

ClickHouse® is a powerful columnar OLAP database that excels in delivering high performance for analytical queries through its unique features, such as sparse primary indexes and vectorized execution. Beyond basic OLAP queries, ClickHouse® offers advanced functionalities like Projections, Materialized Views, and Dictionaries, which enhance query acceleration, pre-aggregation, and in-memory lookups, respectively. It supports operational features like Lightweight Deletes for fast logical deletion and ReplacingMergeTree for upsert patterns without manual deduplication. ClickHouse® Cloud offers managed services, eliminating the need for cluster administration, while ClickHouse® Keeper provides essential coordination for replication and distributed DDL operations. Tinybird, built on ClickHouse®, transforms SQL queries into low-latency APIs, facilitating the creation of data-driven applications. Many teams may underutilize ClickHouse®'s built-in capabilities, which can address common analytics challenges without resorting to alternative databases. Teams can optimize performance and simplify operations by strategically leveraging ClickHouse®'s extensive feature set tailored to their specific needs.

The text explores the challenges and solutions associated with scaling analytics systems, emphasizing the limitations of traditional databases and architectures when handling large datasets and high concurrency. It highlights the "analytics scale trap," where initial successes with small data volumes lead to performance issues as data grows exponentially. Traditional systems, often built for transactional workloads, struggle with analytical queries due to inefficient row-oriented storage, leading to performance degradation and increased costs. The text advocates for columnar databases like ClickHouse, which are optimized for analytical workloads, offering efficient query processing and storage compression. Tinybird is presented as a platform that leverages ClickHouse to provide a managed infrastructure that scales automatically, supports real-time analytics, and reduces operational complexity. By focusing on a streaming-first architecture, Tinybird promises real-time data processing without the overhead of complex batch systems, offering a cost-effective and efficient solution for businesses looking to scale their analytics capabilities.

In-product dashboards, which integrate analytics directly into applications, offer a variety of options for displaying metrics and visualizations without requiring external business intelligence tools. While embedded BI tools like Looker, Power BI, and Tableau provide easy visualization integration, they often face challenges such as performance unpredictability, multi-tenant complexity, and limited customization. These tools are most effective for quick prototyping or when leveraging existing BI investments. However, for applications demanding real-time data and sub-second latency, platforms like Tinybird offer a more suitable solution by providing real-time analytics APIs that serve pre-aggregated data, allowing for complete UI control and scalability. Custom dashboard development, although resource-intensive, provides maximum flexibility in UI and architecture but requires significant engineering investment. The decision on which approach to use—embedded BI, semantic layers like Cube, AWS-integrated solutions like QuickSight, low-code platforms like Retool, or custom-built dashboards—should be based on specific product requirements, including real-time data needs, user scale, and desired customization level.

Materialized views in ClickHouse can effectively process new data but leave historical data unprocessed, presenting challenges for large-scale backfilling due to high resource consumption, time constraints, and data consistency risks. The text discusses best practices for safe backfills, such as query optimization, settings tuning, and intelligent partitioning, while emphasizing the operational burdens of a DIY backfill approach compared to managed solutions like Tinybird. These managed solutions offer benefits like atomic backfills, automatic settings tuning, and on-demand compute isolation, which significantly reduce engineering time and risk. The text highlights the importance of balancing speed, safety, and resource impact during backfills, with real-world examples demonstrating substantial time reductions through optimization and parallelization. It outlines the complexities of backfilling, including resource exhaustion, potential data inconsistencies, and impacts on production infrastructure, and suggests that managed backfills are often justified for large-scale operations due to the significant time savings and reduced risk they offer.