January 2025 Summaries

Data velocity, a critical component of the data landscape, refers to the speed at which data is generated, updated, or deleted and is increasingly challenging traditional data management systems. With the rapid growth in data volume and velocity, existing systems like data warehouses often struggle to maintain performance, causing issues such as decaying query performance and increased computational costs. This challenge is exacerbated by the shift towards real-time data scenarios, which require systems capable of handling high-velocity, semi-structured, or unstructured data. Solutions like open data lakehouses, which integrate technologies such as Apache Iceberg and the Trino open query engine, offer a way to effectively manage high-velocity and real-time data by providing scalable, efficient, and flexible data handling capabilities. These lakehouses enable businesses to work with data at scale more effectively, providing a competitive advantage and facilitating the transition from outdated architectures. Starburst's open data lakehouse solution exemplifies this approach by combining these technologies to enhance data performance and support a range of data workloads, offering features like cluster autoscaling and integration with various data sources, including Hive.

Apache Hadoop, once a groundbreaking technology in the big data landscape, continues to be popular for processing vast amounts of data, thanks to its stability and active ecosystem. Its architecture combines compute and storage, making it ideal for on-premise, long-running batch analytics. However, as technology evolves, Hadoop's limitations, such as complexity, coupled compute-storage architecture, and security issues, have become more apparent, particularly in the cloud era. Many companies are now considering more modern solutions like data lakehouses, which offer the benefits of both data lakes and data warehouses, supporting a wide range of use cases with high performance and cost-efficiency. Apache Iceberg, an open table format for the cloud, is highlighted as a powerful option within the lakehouse architecture, providing enhanced features like updates, deletes, and schema evolution. Transitioning to a data lakehouse allows businesses to leverage legacy Hadoop systems while gradually adopting more versatile and efficient architectures, exemplified by solutions like Starburst's Icehouse, which combines the Iceberg format with the Trino SQL query engine for improved performance and cost savings.

Data centralization has historically been the default approach in big data projects, but it has faced challenges due to its monolithic nature, leading to inefficiencies and backlogs in data engineering teams. In contrast, data decentralization gained popularity as a way to alleviate these issues, although it also poses difficulties when implemented without the right tools. The article advocates for a balanced approach, termed "Centralization 2.0," which allows organizations to choose between centralization and decentralization on a case-by-case basis, leveraging modern data platforms like data lakehouses. These platforms enable a flexible architecture that supports both centralized and decentralized data scenarios, enhancing performance, governance, and analytics capabilities. By adopting an evolutionary approach to centralization, data teams can undertake smaller, iterative projects, thereby optimizing data management and ensuring the strategy aligns with business needs. Tools like Starburst's Open Hybrid Data Lakehouse offer a framework to manage data effectively, allowing organizations to mix and match centralized and decentralized workloads to achieve optimal performance and cost efficiency.

Columnar storage formats like Apache Parquet and ORC have revolutionized data management by offering significant performance improvements for traditional SQL workloads, but they face several challenges when applied to modern machine learning (ML) workloads. Originally designed for tasks involving large-scale data scanning, joining, and aggregation, these formats now struggle with the extreme characteristics of ML datasets, such as wide and sparse tables, vector handling, and compliance with data privacy regulations like GDPR and CCPA. The metadata read time for wide tables can dominate query execution, and existing compression techniques are not optimized for vectors, leading to inefficiencies. Additionally, the block-based compression complicates in-place deletions necessary for compliance, often requiring costly file rewrites. Although these formats remain valuable, their limitations indicate a need for new columnar formats specifically designed for ML workloads, which would incorporate optimizations like direct metadata access and improved in-place delete functions to better meet current data processing demands.