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Dremio Offers New Ingestion Automation, Optimization Features for Apache Iceberg Data Lakehouse

Reduces barriers to Iceberg lakehouse adoption while improving time to insight.

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, the unified lakehouse platform for self-service analytics and AI, has unveiled new capabilities that simplify the process of building and managing an Apache Iceberg data lakehouse. Available immediately, the latest version reduces tedious, manual tasks critical to data management with new capabilities that include ingestion, processing, and migration. By automating Iceberg management processes, Dremio reduces total cost of ownership (TCO) and enhances data team productivity and improves overall time-to-insight.

Dremio has expanded the functionality and capability of its solution in three key areas:

  • Ingestion. With support for high-speed streaming from Kafka into Iceberg, organizations can now effortlessly ingest data in real time and enable near-real-time analytics on the data lakehouse.
  • Migration. New streamlined migration from legacy data lake formats such as Apache Parquet to Apache Iceberg makes it easier for companies to transition from a traditional data lake to a modern lakehouse. The platform can seamlessly convert raw data from data lakes, data warehouses, relational databases, and NoSQL databases into Apache Iceberg, both in the cloud and on premises, making Iceberg adoption rapid, easy, and error-free.
  • Optimization. Dremio’s query acceleration technology (called Reflectionsnow allows users to achieve sub-second BI performance on Iceberg tables of any size without managing BI extracts/imports or aggregation tables in the lakehouse/warehouse. Reflections are updated incrementally and transactionally based on the updates to the Iceberg tables, thereby drastically reducing the time and cost to update Reflections with new data.

In addition, SQL engine enhancements provide real-time memory management that dynamically manages memory and optimizes allocation. This reduces memory use; heightens performance, scalability, and stability; and ensures successful analytical query operations even with vast data sets. Companies can be assured that their queries will run to completion regardless of other workloads and available resources. 

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