December 2023 Summaries

The text explores the challenges associated with cloud data warehouses, particularly focusing on issues like total cost of ownership (TCO) and vendor lock-in, which arise when companies become overly reliant on a single vendor's proprietary technologies. Vendor lock-in occurs as vendors create friction that deters customers from switching providers, often through high costs and complex migrations. This dependency not only limits business agility and innovation but also inflates operational costs through data duplication and reliance on proprietary formats. The text suggests that federated data architectures, such as those offered by Starburst, provide a solution by enabling a unified, vendor-agnostic platform that abstracts storage and allows seamless data access across multiple sources. Starburst's approach, which decouples storage from compute and offers connectors to numerous data sources, is presented as a way to enhance data portability, reduce costs, and foster innovation by mitigating the constraints of traditional cloud data warehouses.

Data warehouses play a crucial role in the data landscape, offering structured and efficient access to data for users with varying technical skills, yet they come with several challenges. These include the need for data to be pre-structured, which can be time-consuming and resource-intensive, and the potential for high storage costs due to the accumulation of historical data. Additionally, data warehouses require pre-planned designs, limiting flexibility for new use cases, and struggle to maintain a single source of truth due to the effort needed to integrate new data sources. They are also not well-suited for unstructured data types like video or audio content, prompting some organizations to explore data lakes or lakehouses as complementary solutions to enhance their data lifecycle. While data warehouses effectively store transaction, operational, and customer relationship data, they may not encompass all organizational data, which can be limiting if new data needs arise, highlighting the importance of evaluating data needs and solutions on a case-by-case basis.

The post discusses the integration of Ibis with Trino as a backend, highlighting the flexibility and functionality of using the Ibis DataFrame API for SQL operations. It emphasizes that while the post revisits concepts from a previous PyStarburst-focused exploration, it now employs Ibis to showcase similar SQL tasks, such as selecting, projecting, filtering, joining, and sorting tables. The author shares personal insights, comparing Ibis to PyStarburst, and notes a preference for PyStarburst due to its alignment with PySpark, although acknowledging Ibis's advantage of running the same DataFrame program across multiple SQL engines. The post also touches on the potential for performance optimization and how Ibis and PyStarburst produce similar query plans, with differences in execution highlighted as areas for further research. Despite the technical depth and exploratory nature of the work, the author expresses satisfaction with the experience and encourages further experimentation and learning through resources like Starburst Academy.

Trino Summit 2023, hosted by Starburst, is set to take place virtually on December 13th and 14th, bringing together experts and enthusiasts from the Trino community to share insights and advancements in data performance. The event promises a lineup of knowledgeable speakers from companies like Pinterest, Airbnb, LinkedIn, and Quora, covering topics such as polymorphic table functions and data lakehouse efficiency. Highlighting contributions like the Trino Gateway, a collaboration between Bloomberg, Starburst, and others, the summit will also feature talks on caching with Guava in Trino by Piotr Findeisen. Attendees can look forward to special announcements and interactive sessions, with the event supported by sponsors including Monte Carlo, Coginiti, and Alluxio.

The unbundling of cloud data warehouses refers to the separation of storage from compute, a trend highlighted by Tanya Bragin in a discussed episode of the "Data Engineering Podcast," which is transforming how analytical workloads are managed. This approach allows data lakes to serve a variety of use cases by leveraging various engines with features such as ACID transactions, SSD caching, indexing, and enterprise-grade security through table formats like Iceberg, Delta Lake, and Hudi, avoiding storage lock-in with a single vendor. Companies are encouraged to initially store their data in a lake/object store, with the option to choose different technologies for specific needs, such as OLAP databases for real-time analytics and high-performance search solutions. As previously closed architectures like Snowflake and BigQuery begin to support external customer storage, the trend toward open analytical environments is becoming more pronounced, despite potential challenges like metadata lock-in. The year 2024 is anticipated to be pivotal for organizations embracing these open architectures, as they can harness the benefits without the constraints of vendor lock-in.

Isaac Obezo, a Staff Data Engineer at Starburst, discusses the limitations of traditional enterprise data warehouses (EDWs) and advocates for a decentralized approach to data management. He highlights several challenges of EDWs, including unpredictable costs, data complexity, and access control issues, which arise from the centralized nature of these systems. Obezo argues that centralizing data is increasingly unrealistic due to the diverse and decentralized nature of modern data sources, leading to inefficiencies and hidden costs. He emphasizes that a decentralized model, as exemplified by Starburst's federated approach, offers a more flexible and cost-effective solution. This model allows data to remain in its original locations, leveraging an abstraction layer to unify disparate data sources without the need for extensive pipeline development. This approach not only reduces costs by decoupling storage from compute but also democratizes data access, enabling users to efficiently retrieve and analyze data without being constrained by centralized architectures. Through Starburst, organizations can embrace the complexity of modern data ecosystems, enhancing data accessibility, compliance, and business insights while moving beyond the limitations of traditional data warehousing.

Data mesh and data lake are two distinct approaches to data management, with data mesh decentralizing data ownership and responsibilities across individual domains, while data lakes serve as centralized repositories for vast amounts of raw data. Data mesh promotes a collaborative and scalable environment by allowing domains to independently manage their data with federated computational governance, enhancing accessibility and compliance. In contrast, data lakes face challenges related to governance, scalability, and reliance on centralized teams, which can hinder timely responses to business needs. Although data lakes facilitate enterprise-grade analytics and support complex data science workflows, they often lead to unwieldy and costly pipelines as they grow. Starburst's implementation helps address these challenges by enabling efficient data querying and reducing ETL processing costs, thus empowering analysts and data scientists with quick access to valuable insights.

Data lakehouses offer significant advantages over traditional data lakes and data warehouses, becoming central to modern data workloads, including analytics, AI, and data applications. They provide enhanced performance, cost efficiency, flexibility, and compliance without sacrificing existing cloud-based storage solutions. The architecture of lakehouses, utilizing modern table formats like Iceberg and Delta Lake, allows for efficient workflows by enabling record-level updates and reducing reliance on outdated systems such as Hive. This modern approach not only improves query performance and reduces costs but also enhances flexibility with full CRUD capabilities and ensures better governance through metadata transaction logs, facilitating compliance with regulations like GDPR and CCPA.

Databases are structured collections of data organized for rapid retrieval, with schemas serving as blueprints that define data structure and relationships within the database. In transactional databases, normalized schemas are optimal for real-time data writing, while analytical systems like data warehouses use schemas optimized for reading large data volumes for analysis. Dimensional data warehouses implement a denormalized schema model, consisting of fact and dimension tables, to efficiently store and retrieve aggregated data for business analysis. Fact tables hold aggregated metrics, providing accessible business insights, while dimension tables offer context by organizing related information. This structure allows business analysts to quickly query relevant data and examine historical trends without frequent updates, as opposed to the normalized designs in transactional databases that avoid redundancy.

Data mesh and data warehouse represent contrasting approaches to enterprise data management, with data mesh focusing on decentralization and data warehouses emphasizing centralization. Data mesh, as defined by Zhamak Dehghani, is a sociotechnical method that enables organizations to handle analytical data in complex environments by connecting directly to data sources instead of centralizing them, promoting self-service and flexibility through logical domains and data products. In contrast, data warehouses offer a centralized repository for business intelligence via a single source of truth, relying on a fixed schema and often resulting in increased costs and inefficiencies due to reliance on a centralized team for changes. Starburst aids in both approaches by providing tools for connecting to various data sources and facilitating the creation of data products, allowing organizations to maintain data governance while accommodating ever-changing enterprise requirements.

Data mesh and data lakehouse are two distinct yet complementary approaches to managing data within organizations. While a data lakehouse serves as a centralized architecture that merges the flexibility of data lakes with the governance and reliability of data warehouses, providing a robust technical foundation for storage and processing, data mesh is an organizational methodology that decentralizes data ownership. This allows individual business domains to manage their data autonomously, promoting a more scalable and collaborative data environment. The data lakehouse excels in handling vast amounts of structured and unstructured data with features like ACID transactions and schema enforcement, whereas a data mesh addresses organizational challenges by distributing data responsibilities across different domains, thereby fostering agility and domain-specific innovation. Together, they can form a hybrid strategy where the technical strength of a lakehouse infrastructure supports the decentralized, domain-oriented principles of data mesh, enabling organizations to treat data as a product while maintaining high performance and governance standards.