The Dremio Blog

Nessie does this with its branching functionality to track changes to multiple copies of the same data, and version control to track these changes over time so they can be merged back into production safely, consistent and atomically.

Arrow Flight SQL JDBC driver increases performance, and reduces the technical installation burden on applications and users

Read this blog to learn about three major trends that have emerged in the world of data and analytics that IT leaders are actively working on today.

Today, we’re excited to announce our Dremio August 2021 release!

Empowering cloud, technology and SI partners with the best SQL lakehouse platform to achieve fast, frictionless, and self-service analytics.

In today’s world of unimaginable data explosion, the 5 severe limitations of data warehouses just do not meet the needs of modern-day apps and user expectations

Best practices to eliminate expensive data copies with a SQL lakehouse platform.

Today we announced the limited availability of Dremio Cloud, a SQL Lakehouse Platform that makes cloud data lakes easier than ever. The launch of Dremio Cloud is the culmination of hundreds of engineering years, and here are 10 reasons why I’m so excited

Data Engineering Challenges Despite the flexibility that data lakes offer, a couple key challenges are commonly encountered when deploying a data lake. The first is how to achieve the transactional consistency of traditional relational databases with an open architecture, and the second is how to address operational challenges to ensure users never see an inconsistent […]

A data lake is a centralized repository that allows you to store all of your structured and unstructured data at any scale. In the past, when disk storage was expensive, and data was costly and time-consuming to gather, enterprises needed to be discerning about what data to collect and store. Organizations would carefully design databases and data […]

While data lakes and data warehouses are conceptually different in terms of their design and implementation, they have at least a few things in common: However, this is usually where the similarities end. Before comparing data warehouses and data lakes, it is useful first to explain what we mean by data warehousing. Today, most organizations initially store data in […]