Composable data systems are changing how organizations manage and analyze data by providing them with a flexible and efficient data infrastructure. Julien Le Dem, co-designer of the Parquet file format and Apache Arrow and now Principal Engineer at Datadog, shared how these systems enable organizations to build customized data infrastructure. This post will explore the main parts of composable data systems, with a focus on Parquet for data storage, and looks at how composable data systems are influencing modern data architectures.
Evolution from Monolithic Databases
Traditional databases often came as monolithic, vertically integrated solutions. It provided a one-stop-shop for data management, but as organizations' data volumes grew and use cases became more complex and diverse, the limitations of these rigid systems became apparent. Julien noted that in a proprietary system "everything's tightly integrated and very opaque... you have very little ability to customize certain aspects."
This realization led to the emergence of composable data systems, which break down database functionalities into modular, interoperable components. Each component specializes in specific tasks such as storage, query optimization, or execution. This modular approach enables organizations to tailor their data infrastructure to their unique requirements, whether prioritizing query performance, storage efficiency, or real-time processing capabilities. By allowing the mix-and-match of different components, composable systems provide the flexibility to optimize for volume, latency, or precision without being constrained by the one-size-fits-all model of traditional databases.
Key Components of Composable Data Systems
At the heart of composable data systems lie several crucial components, each serving a specific function in the data processing pipeline:
- Storage Layer: Formats like Parquet and Apache Arrow provide efficient data storage and retrieval mechanisms. Parquet, in particular, offers columnar storage optimized for analytical workloads, enabling faster query performance and better compression.
- Query Optimization: Tools like Apache Calcite serve as the "brain" of the query engine, transforming logical plans into optimized physical execution plans.
- Execution Engines: Technologies such as Presto, Spark, and newer projects like Velox and DataFusion offer powerful, flexible query processing capabilities.
Standardization plays a crucial role in facilitating seamless interoperability between the various components of composable data systems. Julien underscores the significance of Apache Arrow as a key standardization effort in this space. Arrow provides a common in-memory format for columnar data, enabling efficient data exchange between different systems and components without the need for serialization or deserialization. This standardization is particularly valuable when dealing with multi-tiered database architectures or systems with transactional abstractions. By adopting Arrow as a common interface, developers can create more modular and interoperable data systems, allowing for greater flexibility in combining different storage layers, query engines, and processing frameworks. This standardization not only improves performance by reducing data conversion overhead but also simplifies the development process, enabling data practitioners to focus on building more sophisticated data pipelines and analytics workflows rather than grappling with compatibility issues between different components.
Parquet: A Foundation for Analytical Workloads
The Parquet file format, co-designed by Julien, has become a standard of how raw data gets stored for enterprises. At its core, Parquet's columnar structure provides a solution for the challenges posed by analytical workloads. This design allows for significant improvements in both data compression and query performance, addressing two critical aspects of data management in today's data-intensive environments.
One of the key advantages of Parquet's columnar storage is its ability to optimize data access patterns typical in analytical queries. Unlike traditional row-based storage, where entire rows must be read even when only a few columns are needed, Parquet allows systems to efficiently retrieve only the specific columns required for a given query. This targeted data access dramatically reduces I/O operations and memory usage, leading to substantial performance gains, especially for queries that operate on a subset of columns in large tables.
Overall, columnar format enables more effective data compression. By storing data of the same type together, Parquet can apply type-specific compression algorithms that yield better compression ratios compared to row-based storage. This not only reduces storage costs but also further enhances query performance by minimizing the amount of data that needs to be read from disk or transferred over networks.
The efficiency gains provided by Parquet have made it an essential component in modern data lakes and warehouses. Its versatility allows it to support a wide range of analytical workloads, from simple aggregations to complex machine learning pipelines. Parquet's widespread adoption across various computing environments, including on-premises clusters, cloud platforms, and hybrid setups, underscores its importance in the evolving landscape of data analytics.
As organizations continue to grapple with ever-increasing data volumes and more sophisticated analytical workloads, Parquet's role in enabling fast, efficient data processing becomes even more critical. Its ability to seamlessly integrate with various query engines and data processing frameworks further cements its position as a foundational technology for modern data architectures, empowering businesses to derive insights from their data more quickly and cost-effectively than ever before.
Future Trends in Composable Data Systems
Looking ahead, Julien anticipates continued evolution in several key areas:
- Enhanced Parquet capabilities, including support for wider schemas and new encodings for improved performance.
- Growing adoption of Open Lineage by major cloud providers and data platform vendors.
- Increased focus on data mesh architectures, leveraging lineage for understanding dependencies between data products.
- Further development of efficient execution engines like DataFusion, enabling faster and more flexible data processing across various use cases.
As organizations continue to grapple with ever-increasing volumes of data and more complex analytical requirements, the composable data systems approach offers a promising path forward. Developments in Parquet, Arrow, and Open Lineage, along with a growing ecosystem of tools, indicate a shift towards modular, composable data management and analytics. This approach allows organizations to tailor their data infrastructure to specific requirements, balancing power and customization.
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