The Dremio Blog

Learn how to leverage the integration of Superset and Dremio to visualize your data lake.

This month’s release delivers very useful features like Apache Arrow Flight with Python, security enhancements for Oracle connections, a new support bundle and much more.

This month’s release delivers multiple performance improvements, AWS Edition enhancements and more.

This month’s release delivers multiple performance improvements, a new Arrow flight server endpoint, AWS Edition enhancements and more.

The innovative new features in the Fall 2020 Release deliver sub-second query response times directly on cloud data lakes as well as support for thousands of concurrent users and queries.

This month’s release delivers multiple features such as external query, a new authorization service API, AWS Edition enhancements and more.

This blog post explains the architectural differences between Dremio and Presto and provides details on why Dremio achieves a high level of performance and infrastructure cost savings at any scale.

This blog post describes the methodology used to execute performance and efficiency benchmarks of Dremio versus Presto distributions, shares the tabular results of the benchmark tests, and provides guidance and tools to conduct your own benchmark.

This month’s release delivers multiple performance-oriented features such as Arrow caching, the ability to scale out coordinator nodes, runtime filtering, AWS Edition improvements, and more!

The Apache Arrow team today announced the 1.0.0 release. This covers over 3 months of development work and includes 810 resolved issues from 100 distinct contributors.

Dremio 4.6 delivers enhanced BI tool integrations, hourly paid enterprise edition features and much more!

Dremio 4.5 delivers faster metadata performance, simplified query troubleshooting and much more!

In this article, we explore how Kafka can be used for the storing of data and as a data loss prevention tool for streaming applications.

In this article, we will demonstrate how Kafka can be used to collect metrics on data lake storage like Amazon S3 from a web application.

Cloud data lakes are much more scalable and cost-efficient than data warehouses, but to become pervasive and applicable to both technical and non-technical users, new approaches are required.