The Dremio Blog

By enabling efficient, real-time analytics directly from data lakes, Dremio provides organizations with the tools they need to navigate the complexities of big data, derive actionable insights, and maintain a competitive edge in the digital age.

Learn how to take advantage of Dremio Arctic’s capability to help monitor data quality, recover from mistakes, and audit data.

Dremio's approach to lakehouse management embodies a forward-thinking solution to the challenges of modern data architecture. By integrating Git-for-Data concepts, automating Apache Iceberg table maintenance, and providing an easy-to-use UI for monitoring data catalogs, Dremio not only simplifies data management but also empowers organizations to harness their data for strategic advantage.

Nessie's integration with platforms like Dremio demonstrates the significant value that version control brings to the data lakehouse architecture. Whether through the cloud-based ease of Dremio Cloud or the flexible, self-managed approach with Dremio software, Nessie is set to redefine how organizations manage, collaborate on, and deploy their data assets.

By unifying data from diverse sources, simplifying data operations, and providing powerful tools for data management, Dremio stands out as a comprehensive solution for modern data needs. Whether you are a data engineer, business analyst, or data scientist, harnessing the combined power of Dremio and Apache Iceberg will undoubtedly be a valuable asset in your data management toolkit.

Integrating technologies like Apache Arrow, reflections, and the Columnar Cloud Cache (C3) in Dremio's platform brings a new era in query performance on the data lake. The benefits of these technologies extend beyond just improved query performance; they contribute to a more cost-effective and efficient data management strategy.

mplementing a data mesh with Dremio can significantly enhance an organization’s data management capabilities. Dremio’s alignment with data mesh principles and powerful features make it an excellent tool for this modern data architecture.

As we conclude, remember that the world of data is ever-evolving. The combination of Dremio and dbt isn’t just a solution; it's a continuously advancing pathway to data excellence, unlocking potential and opportunities for businesses ready to embrace the future of data management.

Whether through direct PyArrow library usage or leveraging the dremio-simple-query library for simplified querying and data manipulation, the synergy of these tools opens up new possibilities for data analysis and processing. The ability to convert data streams into different formats ensures compatibility with a wide array of data processing and analytics tools, making this approach highly versatile.

With the release of Dremio Software Enterprise Edition 24.3, we’ve added Kubernetes autoscaling to Dremio. Kubernetes autoscaling for Dremio streamlines resource management with an emphasis on memory and CPU utilization in Dremio workloads.  Now, Dremio on Kubernetes scales automatically, reducing time spent in administration and R&D to forensically size clusters. Understanding Kubernetes Autoscaling Kubernetes autoscaling […]

This month, we’re excited to announce automated table cleanup!

Dremio has released a new version of the Dremio vectorized Parquet reader that will improve query performance on Parquet datasets encoded with the Parquet V2 encodings by up to 75%

This blog helps you learn about array functions in Dremio Cloud and Dremio Software v24.3+.

This blog helps you learn about table-driven access policies in Dremio Cloud and Dremio Software v24.1+.

This blog helps you learn about tabular UDFs in Dremio Cloud and Dremio Software v24.1+.