Why Dremio is a must for Apache Iceberg Data Lakehouses

Alex Merced - Tech Evangelist
5 min readNov 30, 2023

When crafting an Apache Iceberg-based Data Lakehouse, there are many things you need to concern yourself with:

  • Portability of your tables
  • Being able to update your tables
  • Governing your tables
  • Modeling your warehouse
  • Making your warehouse fast and self-serve
  • Documenting your tables

The Dremio Lakehouse platform makes achieving all these things using Apache Iceberg tables easy, fast, and open. Let’s talk about it.

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What is Dremio?

Dremio is a cutting-edge data lakehouse platform that equips you with all the tools to efficiently transform your data lake into a powerful resource, offering self-service, performance, and data governance capabilities akin to a data warehouse. This is achieved through Dremio’s advanced query engine, which swiftly accesses various data sources, including databases, data lakes, and data warehouses. Dremio empowers you to create virtual data marts or data products across these diverse data repositories, implement precise access controls, and seamlessly deliver data for various purposes such as BI Dashboards, reporting, ad hoc analytics, AI/ML, and data applications.

Dremio simplifies the data modeling process, eliminating the need for complex management of materialized views, BI extracts, and cubes. Its unique and innovative data reflections feature accomplishes this, making data acceleration as straightforward as flipping a switch.

Apache Iceberg DDL and DML

Dremio offers robust support for Apache Iceberg tables, seamlessly integrating with Hadoop, Hive, Object Storage, AWS Glue, Nessie, and other Iceberg catalogs. The catalog support within Dremio is continually expanding to accommodate diverse data environments.

With Dremio, you can harness various transaction capabilities when working with Apache Iceberg tables. This includes comprehensive support for Data Manipulation Language (DML) and Data Definition Language (DDL) operations. Additionally, Dremio provides convenient commands like “COPY INTO” for effortless data migration from individual or group files into your tables. You can also utilize “OPTIMIZE” to run compaction processes on your Iceberg tables, optimizing their performance. Furthermore, Dremio offers the “VACUUM” command, which allows you to expire snapshots and perform cleanup tasks on your Apache Iceberg tables.

Data Lakehouse Management

Dremio offers a comprehensive set of Data Lakehouse Management features through its “Arctic” catalog. Arctic is a cloud-managed Apache Iceberg catalog built on the Nessie framework, serving as the internal catalog for the Dremio Sonar query engine on Dremio Cloud. With Arctic, you can harness catalog-level versioning capabilities, enabling you to isolate data ingestion processes, create zero-copy clone environments, facilitate reproducibility by tagging your catalog, and ensure disaster recovery readiness through rollbacks.

Tables under Dremio’s management also benefit from automated optimization and maintenance, reducing the burden of data management tasks and allowing you to concentrate on your analytics efforts. At the same time, Dremio takes care of the rest. The Dremio Arctic catalog includes an intuitive web-based user interface, making monitoring catalog changes, creating branches, merging branches, performing rollbacks, and more effortless.

Fine-grained data Governance and Documentation

Dremio simplifies data governance, offering a robust solution encompassing access control and data documentation. Effective governance involves a combination of explicit access rules to proactively prevent unauthorized data usage and comprehensive documentation to aid users in discovering and optimizing their access to available data. Dremio excels in delivering robust capabilities for both aspects.

Dremio empowers you with fine-grained access controls, allowing you to implement role-based access controls, column-based access controls, and row-based access controls — all easily configurable through SQL. Additionally, Dremio’s semantic layer incorporates an integrated wiki feature, enabling you to provide detailed documentation for each dataset. To enhance user-friendliness, Dremio even offers generative AI capabilities for automating the documentation process, making it more convenient.

Acceleration without Sacrificing Self-Service

Creating a layer of logical views to build your data marts or products is ideal, as it provides more intuitive self-service access to your data. However, when virtualizing your modeling over federated data sources in this manner, you can often encounter performance bottlenecks. This may lead to the need for complex solutions such as creating a web of materialized views, BI extracts, and cubes. These solutions can burden data engineers with additional work for maintenance and introduce complexity for analysts who must manage a growing number of namespaces and determine when to use them.

Dremio simplifies this process by introducing Data Reflections, which manage the materialization of raw or aggregated data based on Apache Iceberg tables. Dremio handles this under the hood, eliminating the creation of new namespaces. Instead, Dremio intelligently swaps out these reflections when it deems appropriate. Data engineers don’t need to worry about maintaining such abstractions, and data analysts can focus solely on analytics. Dremio also offers the flexibility to provide custom materializations through its external reflections feature.

Moreover, when your raw data sources are Apache Iceberg tables, Dremio’s reflections can be incrementally updated, ensuring maximum data freshness while minimizing compute resources required for curating these reflections.

While Dremio is already a fast query engine, it includes a reflection recommendation feature that suggests when to use reflections based on your query patterns. This streamlines the optimization process and makes it easier to leverage these performance-enhancing features effectively.

With reflections, Dremio offers an easy-to-use self-service approach to virtual data warehousing through its semantic layer features without compromising performance. Dremio enables high-performance virtualization at scale.

Conclusion

crafting an Apache Iceberg-based Data Lakehouse can be a complex endeavor with various considerations, including portability, updates, governance, modeling, performance, and documentation. However, the Dremio Lakehouse platform offers a streamlined and efficient solution to these challenges.

Dremio provides a cutting-edge data lakehouse platform that combines self-service capabilities, high performance, and robust data governance, rivaling traditional data warehouses. With its advanced query engine, support for Apache Iceberg tables, and a robust semantic layer, Dremio simplifies managing your data lakehouse while maintaining top-tier performance.

The platform’s support for Apache Iceberg Data Definition Language (DDL) and Data Manipulation Language (DML) operations and its intelligent data reflections feature ensures you can efficiently manage and optimize your data without complex abstractions. Dremio’s Arctic catalog further enhances versioning and disaster recovery capabilities, making data management a breeze.

Dremio’s fine-grained access controls and integrated documentation features make data governance straightforward, allowing you to proactively prevent unauthorized data usage and provide comprehensive documentation for your datasets. Additionally, the platform’s reflection recommendation feature simplifies optimization, ensuring you can easily achieve high-performance virtualization.

In essence, Dremio empowers you to harness the full potential of your Apache Iceberg-based Data Lakehouse, offering an easy-to-use, self-service solution without sacrificing performance. With Dremio, you can confidently unlock the value of your data and drive actionable insights at scale.

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Alex Merced - Tech Evangelist

Alex Merced is a Developer Advocate for Dremio and host of the Web Dev 101 and Datanation Podcasts.