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

Over the past few decades, databases and data analysis have changed dramatically. With these trends in mind, a clear opportunity emerged for a standard in-memory representation that every engine can use—one that’s modern, takes advantage of all the new performance strategies that are available, and makes sharing of data across platforms seamless and efficient. This […]

A cloud data lake is a cloud-hosted centralized repository that allows you to store all your structured and unstructured data at any scale, typically using an object store such as Amazon S3 or Microsoft Azure Data Lake Storage (ADLS). Its placement in the cloud means it can be interacted with as needed, whether it’s for […]

Azure Storage Types Azure Storage is a Microsoft-managed cloud service that provides storage that is highly available, secure, durable, scalable and redundant. Whether it is images, audio, video, logs, configuration files, or sensor data from an IoT array, data needs to be stored in a way that can be easily accessible for analysis purposes, and […]

Background on Data Within Data Lake Storage Data lakes are large repositories that store all structured and unstructured data at any scale. They are used to simplify data management by centralizing data and enabling all applications throughout an organization to interact on a shared data repository for all processing, analytics and reporting, significantly improving upon […]

The Rise of Data Lake Storage For decades organizations relied on relational databases, and later enterprise data warehouses, to organize and store corporate data. These systems provided a strong structural model to organize data as well as data consistency and reliability guarantees. However, these aspects were achieved by vertically integrated technology designs that were isolated […]

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. What Is a Data Warehouse? Data warehouses […]