July 2024 Summaries

Organizations are increasingly seeking efficient, scalable, and secure methods to manage and analyze their data, and transitioning from Hive to Iceberg is one way to establish an open and interoperable data lakehouse. This modern approach, supported by platforms like Starburst Galaxy, enables cross-cloud and cross-region analytics at a petabyte scale while maintaining robust data governance and security. Iceberg provides scalable and flexible data handling, improved query performance, and unified governance, making it an attractive choice for large-scale data workloads. Starburst Galaxy, a fully managed platform, combines Trino and Apache Iceberg to deliver high-performance analytics with minimal administrative overhead, offering real-time data ingestion, automated management, and AI-driven optimizations. The platform emphasizes interoperability, allowing seamless data sharing across platforms and regions, and ensures data integrity and compliance through a centralized governance framework. Embracing this modern data architecture can significantly enhance an organization's analytics capabilities by providing scalable, high-performance solutions with robust governance and security measures.

Starburst and Trino have announced support for the open-source Polaris Catalog, a significant development for Apache Iceberg integration in data lakehouse environments. The Polaris Catalog, which implements the Iceberg REST Catalog specification, aims to facilitate multi-engine interoperability by maintaining metadata pointers for Iceberg tables, thus supporting engines like Trino, Apache Spark, and Snowflake. This advancement enables users to utilize open data formats and access control across various engines, reducing reliance on proprietary systems and enhancing data governance. By integrating with Polaris Catalog, Starburst and Trino provide flexibility in choosing data processing engines, allowing organizations to optimize performance and cost-efficiency by leveraging open architectures. As part of this integration, Starburst is enhancing its managed platform, Starburst Galaxy, to support Polaris, further empowering users to handle diverse workloads efficiently.

A recent discussion between Starburst CEO Justin Borgman and Murli Buluswar, Head of U.S. Consumer Analytics at Citigroup, highlighted the importance of building a robust data foundation and fostering a proactive data culture for maximizing data value. Buluswar emphasized the need for data professionals to transition from a reactionary mindset to one that is proactive and curiosity-driven, encouraging a deeper understanding of the business context. Additionally, he stressed the importance of balancing data infrastructure development with accountability to ensure alignment with organizational goals. The conversation also covered the strategic use of proofs of concept (POCs), advocating for well-defined objectives and success metrics to align POCs with long-term organizational visions. AI was discussed as a tool that should be integrated not just for mechanization but as an enabler of creative, strategic decision-making, promoting collaboration between humans and machines. Clear and comprehensive success metrics were deemed essential for measuring the impact of data initiatives, ensuring all stakeholders understand their business implications. The discussion underscored the evolving roles of humans and machines, with humans focusing more on creativity and interdisciplinary thinking, highlighting the need for continuous learning and adaptation in the data landscape.

The recent session on governing data products at scale emphasized the importance of collaboration between data product producers and consumers to effectively manage and scale data products. Laurent Dresse from DataGalaxy, a data catalog solution provider, highlighted that successful data governance hinges on understanding data products beyond technology, focusing instead on solving issues related to people, communication, and collaboration. This involves defining characteristics such as lifecycle status, quality expectations, security measures, and privacy dimensions and ensuring this information is accessible through a self-service platform. The session underscored the necessity of effective communication, where producers and consumers work together to document and define use cases, ultimately facilitating a comprehensive understanding of data flow from extraction to consumption. DataGalaxy's approach integrates a collaborative "WidiWig" strategy, where producers and consumers jointly identify data sources, build data product pipelines, and ensure real-time updates in their data catalog. This dynamic integration with platforms like Starburst allows for continuous evolution and governance of data products, making them visible and understandable across the organization and enhancing their scalability and adoption.

Automated table maintenance for Apache Iceberg tables is crucial in ensuring optimal performance and efficiency within cloud object storage systems, specifically when used with Trino. The process involves tasks such as optimizing to merge small files into larger ones, expiring outdated snapshots, and removing orphan files to prevent unnecessary data accumulation, which can lead to increased costs and decreased performance. The text outlines a manual approach to creating a maintenance routine using an Iceberg table to store parameters, a Python script to execute maintenance tasks, and a scheduling tool like Cronitor for automation. Additionally, it highlights an automated alternative provided by Starburst Galaxy, which simplifies the process by managing these tasks without requiring extensive engineering work. This automated solution offers a data warehouse-like experience on data lakes, optimizing data size, and improving performance through scheduled maintenance jobs.

Enterprises are increasingly moving away from traditional Hadoop architectures due to challenges with performance, maintenance, and scalability, opting instead for modern data lakehouses powered by SQL engines like Starburst's Trino. The Hadoop Distributed File System (HDFS) struggles with large-scale datasets, leading to inefficiencies and increased costs, compared to cloud object storage solutions that separate compute and storage for scalable, cost-effective data management. Hadoop's SQL-like querying tool, Hive, simplifies data processing but remains limited in speed and scalability, prompting organizations to explore alternatives like Starburst, which leverages Trino's high-performance, massively parallel processing capabilities. This transition enhances query performance, security, and governance while reducing operational costs, allowing seamless integration with existing data ecosystems and supporting a wide range of data sources. By adopting a Trino-based SQL engine, organizations like Optum have reported substantial improvements in query speed and infrastructure cost savings.

Data ingestion, the first stage of a data pipeline, plays a crucial role in establishing the flow of data from source systems to target systems, such as data lakes or data lakehouses, and can be executed through batch processing, streaming, or change data capture methods. Batch ingestion collects and transfers data at scheduled intervals, making it suitable for scenarios where real-time processing isn't needed, while streaming ingestion captures and transfers data continuously for real-time applications. Change data capture tracks dataset changes and updates the analytic system when a threshold is reached. Best practices for optimizing data ingestion include using Apache Iceberg for its cost-effective cloud storage and enhanced metadata capabilities, employing versatile workload configurations to accommodate various data velocities, and performing data quality checks to ensure accuracy and reliability. Starburst Icehouse architecture supports these practices by leveraging Apache Iceberg, data streaming, and quality checks, fostering an open data architecture that democratizes data access and prevents vendor lock-in.

Starburst Enterprise Platform 443-e has reintroduced advanced file caching through collaboration with Alluxio and Trino, which replaces the deprecated RubiX and Alluxio caching systems. This update, powered by Alluxio, enhances query performance and reduces data access costs, integrating seamlessly with Starburst’s Iceberg, Hive, and Delta Lake connectors, thus providing a more versatile caching solution. The reintroduction of file system caching underscores Starburst's commitment to delivering high-performance, enterprise-grade solutions while ensuring supportability for its users.

Trino, originally developed at Facebook to enhance performance and scalability in data processing, has become a prominent open-source query engine for data lakes and lakehouses, celebrating significant growth and innovation over the past decade. Recent updates have focused on modern table formats like Apache Iceberg, Delta Lake, and Hudi, enhancing Trino's role in the data lakehouse ecosystem. With ongoing contributions from its community and key figures at Starburst, Trino continues to evolve with new deployment options, performance improvements, and expanded connectivity, notably integrating with Kubernetes, Apache Iceberg, and Snowflake. As the data landscape shifts towards open and accessible data lakes, Trino is poised to gain further adoption due to its cost-performance advantages and ability to streamline data infrastructure operations. The collaborative nature of its community ensures a dynamic future for Trino, encouraging participation and contributions to shape its development.

Enterprise data architectures often face challenges due to disparate data systems and formats, which hinder strategic decision-making and operational efficiency. This issue is exacerbated by data silos, outdated data lakes, and rigid data warehouses that limit analytics capabilities. The Icehouse architecture, enabled by open-source technologies and tools like Starburst, addresses these challenges by federating disparate data sources into a unified consumption layer, decoupling storage from compute, and allowing organizations to access data where it resides. By leveraging open formats and query engines such as Apache Iceberg and Trino, companies can improve query performance and democratize data access. Additionally, adopting data products tailored to specific business needs enhances decision-making speed and accuracy. Rather than replacing existing investments, the Icehouse architecture integrates with legacy systems to optimize data management and operationalize data products at scale, supporting data-driven innovation.