The Dremio Blog

Last Thursday Dremio hosted Subsurface Summer 2020, the industry’s first and only cloud data lake conference — and we did it all LIVE (and virtually, of course)!

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!

Learn about the modern cloud technology stack that data architects use in today's digital landscape.

As cloud data lake pioneers with our data lake engine, and as the original creators of Apache Arrow, we are excited to introduce and host Subsurface, the industry’s first cloud data lake conference.

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

Dremio's VP of Customer Success - Ohad Almog - shares the reason why he joined Dremio.

Dremio's VP of People - Colleen Blake - shares the reason why she joined Dremio.

I joined Dremio to become part of an amazing team that will empower enterprises to harness data for faster insights in a cost-effective and compliant way.

Dremio AWS Edition allows you to kick your data lake analytics initiatives into high gear, dramatically improve the productivity of your data engineering teams, and save money while you’re at it.

What’s the best measure of success for data pipeline efficiency? This blog charts the rise of business intelligence (BI) on the cloud data lake, explained its appeal from an architectural and performance perspective, and recommended ways to design effective data lakes for BI.

Surrendering data to a data warehouse creates big challenges— locking you into an expensive and proprietary platform and locking you out of innovations from other vendors and the open source community. It’s time to think about an open, modern data architecture based on a cloud data lake.

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.

Innovative data science, and the data volumes and varieties it requires, find a natural home in the data lake. But the latest generation of cloud-native data lakes also hosts a rising share of mainstream business intelligence (BI) projects.