March 2025 Summaries

In celebration of Women's History Month, Starburst highlights the significant contributions of women to the field of technology, starting with pioneers like Ada Lovelace, who envisioned machines capable of creative outputs in the 1800s, and Grace Hopper, whose development of the first compiler in 1952 revolutionized computer interaction. The initiative continues with modern efforts such as Reshma Saujani's Girls Who Code, which has introduced over 500,000 girls to programming, and Dr. Fei-Fei Li's AI4ALL, which promotes inclusion in AI education for underrepresented communities. The legacy of trailblazers like Katherine Johnson, whose work at NASA in the 1960s defied gender and racial barriers, underscores the narrative that diversity in technology is about empowering women to lead and innovate. Starburst Galaxy offers a platform to analyze data and explore the history and future trajectory of women in technology.

Starburst has announced its support for Confluent Tableflow, enhancing its capabilities in transforming streaming data into queryable datasets by integrating Apache Iceberg with Apache Kafka. This collaboration aims to address the challenges of managing streaming data by converting Kafka topics into structured tables stored in Iceberg or Delta Lake formats, facilitating analytics and AI workloads without the need for complex ETL pipelines. Tableflow, available on AWS and soon on Azure and GCP, streamlines the process of mapping, converting, and cleansing high-throughput data, while offering features like schema evolution and catalog syncing. This initiative strengthens Starburst's commitment to open data architecture and its Iceberg-based Icehouse framework, promoting seamless data access for analytics and AI applications.

Trino, often referred to as the "PostgreSQL of analytics," is a versatile SQL query engine that enables interactive querying of data across distributed data sources, mirroring the adaptability that has made PostgreSQL a staple in relational databases. It employs a Massively Parallel Processing (MPP) architecture to efficiently handle large-scale analytics workloads, supporting a wide range of use cases such as real-time streaming analytics, ETL/ELT processes, and machine learning applications. Like PostgreSQL, Trino is open-source and low-cost, with a robust set of connectors that allow integration with various data storage solutions like Snowflake, Clickhouse, and Amazon Redshift, among others. It was originally developed as Presto at Meta to facilitate querying of their extensive data lake and has since evolved to outperform other analytics solutions such as Apache Spark due to its real-time processing capabilities and advanced performance techniques. Trino's governance features, fault-tolerant architecture, and community-driven development have contributed to its widespread adoption in modern analytics environments, offering a flexible and scalable solution for organizations seeking to eliminate data silos and improve data governance.

Enterprise adoption of AI is rapidly scaling, requiring significant technological and organizational changes, with data architecture at the core of this transformation. Starburst, built on Trino, aims to address these challenges by providing a platform for data analytics and AI architecture that facilitates access, collaboration, and governance of diverse datasets. In partnership with Dell Technologies and NVIDIA, Starburst has introduced the Dell AI Data Platform with NVIDIA, a comprehensive solution designed to accelerate AI adoption across enterprises. This platform leverages Dell's hardware and NVIDIA's AI processing capabilities to deliver a high-performance architecture that supports various AI workloads, including Retrieval Augmented Generation (RAG) and Agentic AI, while ensuring data security and governance. By integrating NVIDIA RAPIDS Accelerator for Apache Spark, the platform enhances GPU acceleration, thus reducing bottlenecks and enabling faster insights and decision-making. The collaboration promises to transform industries by improving processes such as inventory forecasting in retail, predictive maintenance in manufacturing, and fraud detection in finance, thereby providing a robust foundation for future AI challenges.

The introduction of the Trino spooling protocol aims to enhance the efficiency of handling large query result sets by distributing the load from the coordinator to the workers, contrasting with the direct protocol which has served for over a decade. While the direct protocol excels in low-latency delivery for smaller datasets, it struggles with large data volumes, leading to potential bottlenecks. The spooling protocol addresses this by allowing data to be retrieved in parallel from external storage, thus offering higher throughput and supporting more space and CPU-efficient formats. This new protocol maintains backward and forward compatibility with existing clients and can automatically switch between direct and spooled data transmission based on the result set size. Initial tests, such as those conducted in the Trino Community Broadcast, have shown significant performance improvements, reducing query completion times from 35 seconds to 9 seconds for large datasets. Adoption of the spooling protocol requires configuration changes and client updates, but it promises to optimize data processing in active clusters by freeing up coordinator resources and supporting extensible encoding schemes.

Apache Iceberg is a tool used to manage metadata for datasets stored in open file formats like Parquet, ORC, and Avro, enabling features such as schema evolution, time travel, and concurrent data access by tools like Trino, Starburst, and Spark. However, its robust capabilities can lead to a proliferation of small metadata files, causing performance issues as the number of files increases, which affects query speed and system scalability. This problem, commonly referred to as the "file explosion problem," arises because Iceberg generates new metadata files for each data modification to maintain transactional integrity and historical snapshots. Solutions such as deleting old snapshots and file compaction are employed to mitigate these issues, with file compaction being a key strategy because it reduces the number of small files by merging them into larger ones without losing data integrity or historical capability. Effective compaction requires strategic prioritization of resources, potentially involving separate computing clusters, to ensure that high-priority tables are targeted for compaction, thereby maintaining efficient query performance.

Frequent data architecture audits are crucial for modernizing analytics and AI workflows, allowing organizations to adapt older systems for contemporary needs and improving overall data performance. These audits help identify gaps between current use cases and existing architecture, enabling teams to address issues such as outdated data pipelines, scalability challenges, and cost inefficiencies. By adopting an Agile mindset and utilizing flexible technologies like open data lakehouses, companies can incrementally upgrade their data systems without resorting to full-scale overhauls. Open data lakehouses, such as those offered by Starburst, facilitate scalable, efficient, and secure data management, supporting diverse data formats and modern workloads. This approach not only enhances performance and reduces costs but also ensures compliance with evolving data security regulations, ultimately positioning businesses for success in the rapidly changing landscape of data-driven decision-making.

Apache Iceberg is gaining traction in Japan due to its ability to address modern data architecture challenges, such as data analytics, AI, and data applications, while offering a flexible solution for managing hybrid data lakehouse architectures. The Japanese market, characterized by the importance of on-premises data for security and compliance and a cautious adoption of cloud computing, finds Iceberg appealing due to its capacity to integrate seamlessly with multiple cloud services and avoid vendor lock-in. Two primary use cases driving Iceberg's popularity in Japan include migrating from Hive table formats, which are limited by the absence of ACID transactions and schema evolution, and preventing vendor lock-in through Iceberg's self-descriptive table format that supports interoperability across different query engines. The recent Iceberg Japan community meetup and Starburst's commitment to supporting the Iceberg community highlight the growing importance of Iceberg in Japan, as it not only solves data silos and maintenance issues but also facilitates seamless analytics across diverse data formats and storage systems.

Hewlett Packard Enterprise (HPE) and Starburst have introduced the HPE Active Data Lakehouse, a revolutionary solution in the modern data architecture landscape, designed to integrate HPE's Alletra Storage MP X10000 with Starburst's analytics engine. This collaboration aims to address the growing needs for data management and analytics, particularly for AI-driven enterprises, by providing unified data access across hybrid, on-premises, and cloud environments. By leveraging open table formats such as Apache Iceberg and Delta Lake, the solution promises significant cost reductions and enhanced query performance, making it ideal for real-time analytics and AI workloads. The HPE Active Data Lakehouse enables organizations to solve business problems by unlocking real-time customer analytics, serving AI/ML models at scale, and ensuring secure data governance across multiple environments. This partnership highlights the industry's shift towards open, hybrid data management systems that offer both flexibility and performance, positioning Starburst as a central player in the evolving data landscape.