June 2024 Summaries

As organizations aim to maximize their data's potential, transitioning from Hadoop to modern lakehouses with Starburst addresses the limitations of legacy systems, such as performance bottlenecks and high operational overhead. Hadoop's traditional architecture, reliant on components like HDFS and SQL engines such as Hive and Impala, is robust for batch processing but struggles with real-time analytics and high-concurrency workloads. Starburst's integration introduces Trino as a powerful query engine and Apache Iceberg for advanced data management, transforming Hadoop into a modern, efficient lakehouse model. This shift supports real-time data ingestion, automated data management, and unified governance, offering improved performance, cost efficiency, and scalability. Organizations can choose from a phased approach, including SQL engine upgrades, on-premises modernization with Dell, or cloud-centric solutions with Starburst Galaxy, to meet specific needs and regulatory requirements. For instance, Optum's deployment of Starburst resulted in queries running ten times faster and significant cost savings, illustrating the tangible benefits of transitioning to a modern lakehouse architecture.

Starburst has introduced a new data quality dashboard in Starburst Galaxy that allows users to monitor data quality rules and trends in real-time, with capabilities for detailed issue troubleshooting. The dashboard supports custom quality checks using SQL, which can be scheduled to periodically assess data quality. Users can view the state of data quality rules, observe trends over the past 30 days, and drill down into specific datasets and rules to resolve issues. Initially, the dashboard will display pass, fail, and unprocessed states of checks, with future updates planned to include classifications of quality checks and visualizations of their importance to the business. The dashboard is in public preview and becomes available automatically in the Galaxy catalog explorer after executing a quality check, requiring no additional configuration.

Starburst has introduced a new feature in Starburst Galaxy that allows data owners to package SQL code samples with their data products, enhancing data consumption by providing users with a starting point for understanding and utilizing the data. This initiative addresses the challenge of underutilized data products by offering functional code snippets that users can easily modify to meet their needs, thereby saving time and reducing the risk of data misuse. The feature includes a dedicated section for these SQL samples, where users can execute and review the code within the query editor. Additionally, for organizations using Galaxy's generative AI capabilities, users can receive explanations of the sample code and ask questions to gain further technical and business insights. Currently in public preview, this feature is accessible to users who sign up and create a data product in their free Starburst Galaxy account.

Starburst Enterprise Platform's 443-e.1 LTS release introduces a range of enhancements designed to improve the open data lakehouse's capabilities, efficiency, and user experience. Key features now generally available include managed statistics for better data representation and query performance, SAML 2.0 for secure single sign-on authentication, and AWS Lake Formation integration for advanced data management and security. The release also enhances collaboration through shared queries, allowing users to easily share and manage SQL queries within the platform. Additionally, the update supports new technologies like Apache Ozone and integrates with external configuration providers, reinforcing the platform's focus on performance, governance, and security. These updates not only enhance the technical capabilities of Starburst Enterprise but also offer significant business benefits, aligning with the company's mission to deliver a state-of-the-art data analytics platform.

Starburst Galaxy has introduced several new features and enhancements aimed at improving data performance and user experience, including the Icehouse 101 Workshop, a data quality dashboard, and data product usage examples. The Icehouse 101 Workshop is designed to help users learn about building Icehouse architectures using Trino and Apache Iceberg, providing insights into creating, transforming, and optimizing data. The new data quality dashboard offers a comprehensive view of data quality rules and trends, with future updates planned for more detailed insights. Additionally, Starburst Galaxy now includes a feature that allows data owners to share SQL code samples for data products, facilitating easier understanding and usage of data. The platform also supports monitoring active queries across execution stages and offers dynamic catalog management, enabling seamless workflow without requiring cluster restarts. These updates aim to enhance data management and analytics capabilities for users.

Asurion, a technology care company, emphasizes the critical role of data quality in effective analytics and decision-making, especially given their vast processing of over 20 billion records daily. To address data quality challenges, Asurion has developed a proactive machine learning framework that automates data cleansing, predicts potential data issues, and improves data accuracy, completeness, and reliability. This approach has led to a twofold reduction in data quality issues, decreased reprocessing costs, and enhanced trust in enterprise data. Asurion's data governance policies and metrics ensure continuous improvement and alignment with business strategies. Additionally, Starburst Galaxy’s new data observability features, such as column lineage and SQL-based data quality checks, further enhance data integrity and facilitate efficient data management. These initiatives collectively enable Asurion and other organizations to maintain high data standards, optimize business outcomes, and achieve better alignment with key performance indicators and return on investment.

Apache Iceberg has emerged as the leading table format for the data lakehouse, overtaking Databricks' Delta Lake, due to its open data architecture and features that rival traditional data warehouses, such as ACID compliance and schema evolution. This shift towards Iceberg, embraced by industry giants like Snowflake and Databricks, has sparked a new competition over compute engines that can operate on open data stacks. The openness of Iceberg allows for a dynamic, interoperable data pipeline where various components, including compute engines, can be swapped as needed, encouraging a more competitive landscape. The integration of Iceberg with platforms like Starburst Galaxy, particularly through the use of the Trino engine, positions it well in the evolving data architecture space, suggesting that the industry's focus is shifting towards finding the best compute engines compatible with Iceberg's open framework. This development signifies a broader industry move towards more open, flexible data solutions, with Starburst's "Icehouse" architecture aiming to capture the new demand for scalable and efficient data processing on Iceberg.

As organizations increasingly migrate away from Apache Hadoop due to its performance limitations and architectural complexity, many are adopting modern data lakehouse architectures on platforms like Amazon Web Services (AWS) to improve scalability, cost-effectiveness, and performance. The transition from Hadoop involves leveraging AWS services such as S3 for data storage, Glue for ETL processes, and EMR for managing Hadoop infrastructures, while new tools like Apache Spark and Trino offer enhanced data processing and query capabilities. Modern file and table formats, including Parquet, Avro, Iceberg, and Delta Lake, accelerate query performance and support ACID transactions, making them well-suited for handling semi-structured and unstructured data from streaming sources. Enterprise solutions like Starburst extend the capabilities of open-source tools, providing federated data access, governance, and security features that facilitate compliance with international data regulations. Case studies illustrate how organizations like global investment banks and Israel's Bank Hapoalim have utilized these technologies to achieve efficient data management and rapid decision-making, ultimately streamlining their data architectures and enhancing their data-driven cultures.

The recent Snowflake Summit was marked by major announcements, including Databricks acquiring Tabular, the creator of Apache Iceberg, and Snowflake introducing Polaris, an open-source implementation of the Iceberg REST catalog. These moves highlight the growing importance of open table formats like Iceberg in the data warehousing industry, which has traditionally been dominated by proprietary systems. The acquisition of Tabular by Databricks underscores Iceberg's dominance in the lakehouse format wars and raises questions about the future of Databricks' own Delta Lake. Snowflake's Polaris announcement reflects a shift towards embracing open standards to reduce vendor lock-in, a common concern among its customers due to Snowflake's cost and migration challenges. These developments are seen as beneficial for customers of both Snowflake and Databricks, offering them greater flexibility and the potential for improved price-performance by allowing them to use the best compute engines for their needs. Starburst, a company advocating for open data access, supports these changes and offers integration with various catalogs, enabling customers to leverage modern table formats across different deployment models, including on-premises and hybrid environments, through partnerships like the one with Dell's Data Lakehouse.

The blog post explores the advantages and implementation of partitioning strategies in data lake tables, specifically using Apache Iceberg within Trino, to enhance query performance and scalability. It illustrates the concept of partitioning as a method to organize data into subdirectories, allowing queries to target specific subsets of data, thus reducing resource consumption and improving efficiency. The post emphasizes the importance of selecting an efficient partitioning strategy, particularly for large tables, and highlights Apache Iceberg's unique features like hidden partitioning and partition evolution, which allow dynamic adjustments without rewriting data. It also discusses the challenges of small file sizes in query performance and advocates for the use of compaction tools offered by modern table formats to address this issue. The text concludes by positioning Apache Iceberg as an optimal table format due to its advanced partitioning features and encourages the use of an "Icehouse" setup, combining Trino and Iceberg, for optimal data management and query performance in cloud data environments.

For nearly two decades, companies have relied on the Apache Hadoop ecosystem to manage large-scale data processing, but its complexity and performance limitations have led to the adoption of advanced tools like Trino and Starburst to enhance data management. While Hadoop's original framework, including MapReduce and HDFS, focuses on affordable big data analytics, it struggles with modern demands such as real-time ingestion and efficient data storage. Trino, a massively parallel processing SQL query engine, and Starburst, a platform enhancing Trino, bypass these limitations by allowing direct data querying from sources, reducing network traffic, and improving processing speeds through cost-based optimizations. Additionally, Starburst supports federated data architecture, enabling data storage in scalable cloud services, and integrates with existing security and governance frameworks, thus offering a comprehensive solution that blends the accessibility of SQL with the scalability of modern data architectures.