December 2022 Summaries

Organizations are increasingly recognizing the importance of data analytics in driving business innovation and competitiveness, yet many still struggle with effectively leveraging the vast amounts of unstructured data they collect, often referred to as "dark data." A strong data culture, characterized by managing data as a product and executing a data-driven strategy, can significantly enhance business success, as demonstrated by organizations achieving higher returns from AI investments and delivering new business use cases more rapidly. Despite the potential, challenges persist, including cultural barriers to becoming data-driven, issues with data access and quality, and the complexity of managing data systems, which often involve multiple platforms and cloud environments. The role of Chief Data Officers (CDOs) is becoming increasingly critical, with a focus on aligning data strategies with business goals and overcoming these challenges to harness data's full potential. As the demand for data analytics skills grows, so does the need for effective data management strategies, such as Data Mesh, which aims to democratize data access and foster innovation across industries.

In 2023, data management trends emphasize agility, simplicity, and environmental responsibility, reflecting companies' desire to optimize existing infrastructures without extensive overhauls. Organizations are gravitating towards hybrid architectures to leverage both on-premise and cloud solutions, valuing SQL for its universal applicability and ease of use. Data Lakes remain popular for their scalable storage capabilities, with innovations like Delta Lake and Apache Iceberg transforming them into flexible Data Warehouses. The Data Mesh concept is gaining traction as businesses seek democratized access to data, viewing it as a product managed by relevant business entities to enhance autonomy. As data volumes continue to grow, companies are increasingly focused on controlling data movement to curb energy consumption and costs, while integrating environmental data into decision-making processes to align with broader sustainability goals. Starburst advocates for leveraging existing data infrastructures to streamline access and analysis, facilitating efficient decision-making without complex migrations.

Starburst had a remarkable year in 2022, achieving significant growth and milestones under the leadership of Co-Founder and CEO Justin Borgman. The company was recognized on the Inc. 5000 list as one of the fastest-growing private companies in the U.S., with notable rankings in software and within Boston. Starburst nearly doubled its workforce, bolstered by strategic hires including its first-ever Chief Financial Officer, David Freeman, and Senior Vice President of Product Management, Alison Huselid. The company secured $250 million in Series D funding, tripling its valuation to $3.35 billion, and acquired Varada to enhance its data lake analytics capabilities. Starburst's innovations included new product features and compliance advancements, reflecting its commitment to data mesh and lakehouse architectures. The company celebrated the 10th anniversary of Trino, its open-source SQL query engine, with a hybrid summit. Recognized for its culture and product excellence, Starburst received accolades such as being named on Forbes' list of Best Startup Employers and winning the Datanami Readers' and Editors' Choice Award. The company is poised for continued growth, with plans for the 2023 Datanova conference aimed at advancing data and analytics discussions.

The announcement of a new native connector in Tableau Cloud marks a significant development in the collaboration between Tableau and Starburst, facilitating a seamless shift to cloud-based software-as-a-service platforms. This integration allows Tableau Cloud users to access and query data from various sources, including cloud data lakes, traditional warehouses, and relational databases, without additional configurations. The connector enhances the ability of business intelligence analysts to generate insights from distributed datasets by leveraging Starburst's high-performance connectors and query processing capabilities. With features like LDAP authentication and multi-cloud capabilities, the partnership between Tableau and Starburst aims to empower enterprises with faster insights and secure data access, reflecting a broader trend towards managed cloud services and the growing adoption of platforms like Starburst Galaxy.

The text explores the concept and importance of data products within the framework of Data Mesh, emphasizing their role in bridging the gap between operational and analytical data planes. It outlines three types of data products: source-aligned, consumer-aligned, and aggregate data products, each serving distinct organizational purposes. The source-aligned products represent raw data from operational systems, while consumer-aligned products are created by domain experts to add business value. Aggregate data products are used at a corporate level to drive global KPIs. The discussion highlights the significance of data governance, including usage metrics that inform the value and trustworthiness of data products, allowing organizations to transition from reactive to proactive data management. By utilizing tools like Starburst, organizations can streamline their Data Mesh implementations, reducing technological complexity and enhancing agility in data product creation and management.

Optimizing a data lakehouse architecture with Starburst Galaxy on AWS enhances data management by combining the strengths of data lakes and data warehouses, ensuring efficient data handling and improved performance. Starburst Galaxy provides flexibility in query execution, supports modern data formats like Apache Iceberg, Delta Lake, and Hudi, and integrates seamlessly with AWS services, which helps address the challenges of managing raw data in traditional data lakes. By utilizing open table formats, implementing detailed security controls, and building a structured reporting system, organizations can efficiently manage the data lifecycle from raw input to analysis-ready information. This approach allows for more effective data access and role customization, and facilitates the migration of queries from Amazon Athena to Starburst Galaxy, ultimately maximizing the potential of an AWS data lakehouse.

Apache Iceberg in Trino offers robust time travel and rollback features, allowing users to view and revert to previous table states through snapshots, enhancing data management capabilities on data lakehouses. Each change made to an Iceberg table generates a new snapshot, which can be accessed using SQL queries to review or revert operations like creation, insertion, and updates. The "for version as of" syntax enables users to select specific snapshots, while timeframes can retrieve older data states. Rollbacks permit reverting to previous table states if a snapshot hasn't been cleaned up, useful for correcting accidental data modifications. These capabilities bring database-like functionalities to object stores, significantly enhancing data management and recovery processes.

Data lakes and data warehouses serve as repositories for data storage but differ fundamentally in their architecture and functionality, particularly in how they utilize data catalogs. Data warehouses are characterized by their structured, predefined schemas that dictate how data is loaded and managed, offering speed and optimized query performance, but at the cost of flexibility. Conversely, data lakes are known for their flexibility, accepting data in any format and using catalogs to help users identify and manage data types, though traditionally, they lagged in query performance compared to data warehouses. However, advancements in data lake technology have enhanced their query capabilities, making them comparable to data warehouses while maintaining flexibility and cost efficiency. The complexity of managing data for insights and governance remains a challenge when using both systems, leading to the consideration of solutions like Starburst, which provides fast data lake query engines that optimize data access and reduce management costs, enhancing time-to-insight for business decisions.