The Apache Iceberg table format has seen an impressive expansion in its compatibility with a vast spectrum of data platforms and tools. Among these, Dremio stands out as a pioneer, having embraced Apache Iceberg early on. In this article, we delve into the multitude of ways Dremio has seamlessly integrated Apache Iceberg, establishing itself as one of the most formidable platforms for Iceberg lakehouses available today.
Reason 1: Dataset Promotion
Dremio’s integration with Apache Iceberg began with its innovative dataset promotion feature, which significantly enhances how you interact with file-based datasets on your data lake. When you connect a data lake source containing a folder of Parquet data, Dremio allows you to elevate it to a table status. This capability is not just about organization; it’s about performance. Leveraging Dremio’s high-performance scan capability, driven by its Apache Arrow-based query engine, dataset promotion transcends to a new level of efficiency. Notably, Apache Arrow isn’t just an adopted technology for Dremio; it’s foundational, having originated as Dremio’s in-memory processing format. When a Parquet file table is promoted, Dremio discreetly crafts a layer of Apache Iceberg metadata. This layer is not merely a catalog; it’s a powerful index that enables swift and efficient querying, epitomizing how Dremio’s deep integration with Apache Iceberg makes it a powerhouse for managing Iceberg lakehouses.
Reason 2: Data Reflections
Dremio has a powerful feature known as data reflections, which effectively eliminates the need for traditional materialized views, BI extracts, and cubes. This feature allows users to activate reflections on any table or view within Dremio, which then creates an optimized Apache Iceberg table within your data lake. Dremio maintains a keen awareness of the materialized data’s relationship to the original table or view, allowing it to seamlessly substitute the optimized version during queries. This substitution process enhances query speed without burdening the end user with the complexity of juggling multiple namespaces across various materialized views.
Data reflections are not a one-size-fits-all solution; they are highly customizable. Data Engineers can easily by SQL or point-and-click specify which columns to track, define custom sorting and partitioning rules, and choose whether to reflect raw data or aggregated metrics, tailoring the reflection to provide the optimal query performance. These reflections are not static; they’re dynamically updated on a schedule that you control, with upcoming features allowing manual updates via a REST API. You can even create your own materialization externally and register them with Dremio so it can use them for acceleration.
Moreover, Dremio’s intelligent reflection recommender system advises on which reflections will maximize price performance based on your cluster’s query patterns. This feature not only accelerates query performance but does so in a manner that is straightforward for data engineers to implement and for data analysts to utilize, simplifying the entire data interaction process while delivering rapid results.
Reason 3: First-Class Apache Iceberg Support
Dremio elevates the utility of Apache Iceberg, offering its benefits to all users, regardless of whether they operate an Iceberg Lakehouse. Through features like dataset promotion and data reflections, users engage with Apache Iceberg’s advantages seamlessly, without needing to delve into its complexities. However, Dremio doesn’t stop at seamless integration; it offers comprehensive capabilities to interact directly with Apache Iceberg tables in your data lakehouse.
Dremio’s full DDL (Data Definition Language) support allows users to create and modify Iceberg tables, while its complete DML (Data Manipulation Language) capabilities enable inserting, upserting, deleting, and updating data within Apache Iceberg tables. The platform supports a variety of options for your Apache Iceberg catalog, enhancing flexibility and integration.
A standout feature of working directly with Iceberg tables in Dremio is the elimination of the need for setting a metadata refresh cadence, as required for promoted datasets. The catalog becomes the authoritative source for Apache Iceberg metadata, ensuring that users always access the most current metadata, especially when working with service catalogs like Nessie, AWS Glue, Hive, and others.
This capability transforms Dremio from a platform solely focused on read-only analytics to a comprehensive solution capable of handling data ingestion, curation, and analytics. Users can import data from various sources into their Iceberg tables, join Iceberg tables with data from other databases and data warehouses, and build a cohesive semantic layer—all within the Dremio platform.
Reason 4: Enhanced Catalog Versioning
Dremio not only supports a diverse array of Apache Iceberg catalog options but also continuously expands its offerings. A notable aspect of Dremio’s catalog capabilities is its integration with the open-source Nessie technology, alongside support for externally managed Nessie catalogs. Nessie introduces a robust versioning system for Apache Iceberg tables, enabling users to segregate work across different branches within the catalog. This feature is a game-changer, offering functionalities such as multi-table transactions, isolation for ingestion tasks—ideal for DataOps patterns, the creation of zero-copy environments, multi-table rollbacks, and tagging for simplified data replication.
The integration of Nessie within Dremio’s catalog doesn’t just enhance Dremio’s functionality; it extends its utility beyond its ecosystem. Since Dremio’s catalog leverages Nessie, it’s accessible read and write via other tools like Apache Spark and Apache Flink, broadening the scope of your Apache Iceberg workflows. This interoperability, empowered by catalog versioning, provides unparalleled flexibility and control, allowing you to streamline your data processes and collaborate more effectively across various platforms.
Reason 5: Streamlined Lakehouse Management
Dremio simplifies the intricacies of lakehouse management with its suite of built-in table optimization features. Users can effortlessly execute compaction or manage snapshot expiration across any supported Iceberg catalog, utilizing Dremio’s intuitive OPTIMIZE and VACUUM SQL commands. The convenience doesn’t stop there; for tables cataloged within Dremio’s integrated system, the platform offers automation for table management tasks. This means that Dremio can be configured to routinely manage table optimization on a set schedule, ensuring your tables are always operating efficiently without the need for constant oversight. This automation not only streamlines operations but also ensures that your data is always primed for optimal performance, allowing you to focus on deriving insights rather than managing the underlying infrastructure.
Conclusion
In conclusion, Dremio’s adept integration with Apache Iceberg provides a robust, feature-rich platform that significantly enhances lakehouse architecture and management. From advanced dataset promotion and revolutionary data reflections to first-class Apache Iceberg support, enhanced catalog versioning, and streamlined lakehouse management, Dremio offers an array of tools that empower users to optimize their data operations efficiently. Whether you’re looking to accelerate queries, manage complex data transformations, or ensure seamless data governance and versioning, Dremio’s innovative features and user-friendly approach make it an ideal choice for anyone looking to leverage the full potential of Apache Iceberg lakehouses.