October 2022 Summaries

Data Mesh, a decentralized data management strategy, integrates risk management and compliance best practices, which are now essential rather than optional features for businesses. This approach allows for decentralized data ownership, accelerating time-to-value but posing challenges for compliance teams used to centralized systems. By employing flexible compliance controls and ongoing education, organizations can maintain robust security protocols while adopting Data Mesh. Compliance can be reinforced through globally recognized certifications like ISO 27001, which align well with Data Mesh's federated governance model. Least-privilege policies are essential for mitigating access risks, and documenting compliance procedures ensures consistency and reduces knowledge loss. Data Mesh allows domain-specific data classification, empowering data product developers to integrate compliance and data privacy at foundational levels. While shifting to this new data management approach may seem daunting, Data Mesh's inherent design supports security and compliance at every level, ensuring that businesses do not have to compromise on these critical aspects during digital transformation.

Cloud data lakes have become integral to modern data infrastructure, offering the ability to manage vast, complex datasets in their native format and support high-performance queries. They consist of three essential layers: scalable object storage, a distributed query engine, and a big data catalog. Major cloud platforms like AWS, Azure, and Google Cloud provide robust solutions for building data lakes, each with unique storage and service offerings. Open-source query engines such as Trino, Apache Drill, and Apache Spark facilitate querying across diverse data sources. While managed solutions offer quick deployment and scalability, they can lead to potential cost increases and data silos. A cloud data lake stack can serve diverse use cases, from ad hoc querying to real-time IoT data analysis. Optimal performance and cost balance are crucial, with advanced query acceleration techniques enhancing efficiency. Starburst's platform exemplifies how to optimize cloud data lakes by autonomously accelerating queries, ensuring high ROI, and maintaining agility without performance compromise. As cloud adoption grows, organizations are urged to prioritize transitioning analytics workloads to data lakes to maintain competitive advantage.

Apache Iceberg is an open-source table format, originally developed by Netflix and now under the Apache Software Foundation, that provides advanced database functionality on object stores such as AWS S3, Azure ADLS, and Google Cloud Storage. It enables the construction of data lakehouses with reliable ACID transactions, avoiding vendor lock-in and offering significant flexibility in data management. With its ability to support schema evolution, time travel, and efficient partitioning, Iceberg allows for high-performance data queries and modification, making it a popular choice among companies looking to migrate from Apache Hive. The format has seen widespread adoption by various data engines, enhancing its reputation as a robust, community-driven solution for managing large-scale analytics workloads. Apache Iceberg integrates seamlessly with Trino, offering features such as snapshot management and metadata queries, and is strongly endorsed by platforms like Starburst for its ability to deliver exceptional performance without the need for proprietary cloud data warehouses.

Monica Miller details her journey from skepticism to advocacy in integrating dbt with Starburst Galaxy to create a federated data lakehouse reporting structure. Initially hesitant about dbt due to the complexities of centralizing data from multiple locations, Miller eventually embraced its transformative power when combined with Trino, allowing her to federate data from various sources like S3, Snowflake, and the TPC-H dataset into one cohesive system. This integration not only addresses the challenges of accessing disparate data sources but also facilitates communication between different data platforms, enhancing data analysis and insights. Through a tutorial, Miller demonstrates how to build a data lakehouse architecture using Iceberg table formats on S3, highlighting the practicality and efficiency of using dbt and Starburst Galaxy together. By offering pre-built models and detailed instructions, the tutorial aims to help users quickly understand and implement this innovative solution in their own environments.

The Accenture Master Class explores the adoption of a data product mindset, emphasizing the importance of integrating data products into business strategies to enhance value and drive innovation. It highlights the necessity for executive understanding and involvement in defining the value of data products, which can begin with creating marquee products in areas like finance and HR due to their universal relevance within a company. The class calls for building, maintaining, and updating initial data products while setting standards for onboarding new developments and supporting various business groups across different levels of tech maturity. To ensure scalability, the introduction of frameworks such as metadata hubs, data virtualization, and knowledge graphs is recommended. The role of the Chief Data Officer is expected to evolve from tactical data management to focusing on strategic growth, aligning with revenue-focused objectives. The class underscores the iterative process of strategy and governance, involving user feedback and collaboration among data teams, to successfully launch and sustain data products, while also considering aspects like architecture, regulation, and user support.

Data lakes have emerged as a crucial tool for big data analytics, offering unprecedented agility by enabling organizations to access and utilize diverse data types without predefined schemas, unlike traditional databases and data warehouses. The architecture of a data lake emphasizes data collection, transformation, and access, with cloud-based systems providing flexibility through scalable storage options like object stores. Modern table formats such as Apache Iceberg and Delta Lake enhance performance by handling large data volumes and supporting features like ACID compliance. Data virtualization is becoming essential in data lake architectures, allowing direct access to data and acting as a single source of truth, thus facilitating innovation and efficiency by eliminating the need for separate analytics systems. Starburst exemplifies this approach by enabling data virtualization, which allows organizations to perform efficient queries across various data sources, thereby maximizing the potential of data-driven processes for business intelligence and operational impact.

Starburst, co-founded by Justin Borgman and the creators of Trino, is a company that has evolved from an open-source project into a venture-backed startup challenging traditional data warehouse models. Originally developed as Trino, a massively parallel processing analytic database, the company aimed to provide a versatile solution for running fast SQL, BI, and batch transformation workloads on distributed data. Starburst initially bootstrapped its operations, resisting venture capital until it partnered with Index Ventures, allowing it to expand its customer base to include major companies like Comcast and DoorDash. The company distinguishes itself by offering users the flexibility to query and analyze data across various sources, thereby challenging the industry norm of vendor lock-in and centralized data storage. By advocating for data decentralization, Starburst seeks to empower businesses to access and leverage diverse datasets, disrupting the traditional monopoly model of data management and improving decision-making capabilities.

Starburst has been a significant contributor to the Trino community, assisting with everything from simple queries to complex code reviews, and has played a key role in the continued development of the Trino ecosystem. The company has collaborated with authors Matt, Martin, and Manfred to produce "Trino: The Definitive Guide," a resource designed for both beginners and advanced users, which is now available in its second edition. The book, available for free as a digital copy, has been updated to reflect advancements in the Trino ecosystem, including the update to Java 17. It also serves as a practical guide for managing Starburst Enterprise clusters, configuring catalogs, and writing SQL queries, facilitated by additional Starburst contributions like dbt-trino and the Metabase driver. The company continues to support the community through events such as the upcoming Trino Summit 2022, which will offer further learning opportunities both in-person and online.

Starburst Galaxy, in conjunction with Apache Iceberg and AWS services like S3 and Glue, offers a streamlined approach for managing a data lakehouse architecture that eliminates the need for costly data migrations by keeping data stored on S3 while enabling efficient querying and reporting. By landing raw data in formats such as JSON on S3, users can leverage Starburst's Great Lakes connector to create structured and reporting tables with improved query performance through formats like Apache Iceberg and Delta Lake. This architecture supports both SQL BI reporting and ad hoc querying, allowing companies to build their data infrastructure with flexibility in processing engines and storage options, while maintaining performance gains and ease of data management. The tutorial also highlights the use of Trino, an open-source SQL query engine managed by Starburst Galaxy, to facilitate the easy handling of data and analytics on cloud storage platforms like S3, Azure ADLS, and GCP, making it a versatile solution for modern data challenges.

Data virtualization, a technology that serves data consumers directly on top of data stores without moving data, is evolving to address the challenges of enterprise performance, federated governance, and unpredictable cost structures. Initially designed for flexibility and quick time-to-market, data virtualization has struggled with performance consistency and complex data operations, often leading enterprises to rely on isolated data silos for speedy queries. Trino, an advanced query engine, optimizes data virtualization by offering high performance without data duplication, using in-memory pipelined distributed execution and a cost-based optimizer to enhance query efficiency. As data management trends shift towards decentralized data management and data mesh architectures, data virtualization is becoming an essential component of modern data lakehouses, providing agility, ease-of-use, and enhanced performance for a broad range of use cases without relying on traditional data warehousing.