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New Capabilities Advance Dremio’s Data Lakehouse

New product functionality and expanding ecosystem combine data warehouse functionality and performance with the scale and cost advantages of a data lake.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Dremio has included new features for writing and updating data, enhanced support for semistructured data, and expanded BI and data ecosystem integrations that mark a turning point in data lakehouse evolution. Dremio is ensuring easy, fast, self-service analytics -- with data warehouse functionality and data lake flexibility -- across customer data.

“Dremio’s Apache Arrow-based query engine and patent-pending query acceleration technology, Data Reflections, have enabled companies to achieve sub-second performance and 1/10 the cost of cloud data warehouses when querying data,” said Tomer Shiran, co-founder and CPO at Dremio.

Mature Product Functionality Is Powering Increased Data Lakehouse Adoption

With SQL improvements and new product capabilities added for performance, usability, security, and ecosystem connectivity, Dremio’s open data lakehouse is now even better positioned to be a core part of modern analytics architectures. Highlights include:

  • DML and time travel. General availability support for DML operations (INSERT, UPDATE, DELETE) on Apache Iceberg tables and time travel for in-place querying of historical data mean that Dremio has established a key pillar of data lakehouse operation and provided functionality previously only found in database and data warehouse technologies.
  • Additional new SQL functionality. Dremio software and Dremio Cloud now have a semistructured MAP data type that allows users to query map data from Apache Parquet files, Apache Iceberg, and Delta Lake. Other updates include MERGE statement and FROM clause improvements as well as improvements to scalar SQL UDFs, tabular UDFs, Listagg, QUALIFY clause, and LIKE ANY/ALL/SOME statements.
  • Security enhancements. These include row- and column-level policy-defined access control for users, new RBAC privileges for admin operations, and encryption for a project store (S3 buckets) with customer managed keys.
  • Performance improvements. Dremio is adding Graviton2 support as a new option for customers within AWS, and spillable hash join functionality, with which a join operator can spill to disk, when the build-side of a join operator does not fit in memory.
  • Usability updates. A functions list provides users with a searchable list of supported SQL functions and the syntax and description of each. Function syntax from this component can be added to the SQL runner with one click.

Commitment to Open Source and Community Remains Uncompromised

Dremio has always been deeply involved in open source projects that are powering data’s independence and use, and incorporates all of the latest functionality from Apache Iceberg, Apache Parquet, Apache Calcite, Apache Arrow, Apache Arrow Flight, and Gandiva. Dremio continues to be a key contributor to these projects.

Dremio co-created Apache Arrow, which has become the industry standard in-memory columnar format for analytics systems. Arrow is downloaded over 60 million times each month and is embedded in some of the world’s largest analytics projects.

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