The Dremio Blog

With the introduction of REST catalog , managing and interacting with Apache Iceberg catalogs has been greatly simplified. This shift from client-side configurations to server-side management offers many benefits, including better security, easier maintenance, and improved scalability.

The scale, speed, and variety of data are growing exponentially. Organizations are inundated with vast amounts of information from an ever-increasing number of sources, ranging from traditional databases to cloud-based systems and real-time data streams. This data deluge presents significant challenges for traditional data architectures, which often rely on extensive data pipelines and centralized storage […]

Organizations are constantly seeking ways to optimize data management and analytics. The Dremio and NetApp Hybrid Iceberg Lakehouse Reference Architecture brings together Dremio’s Unified Lakehouse Platform and NetApp’s advanced data storage solutions to create a high-performance, scalable, and cost-efficient data lakehouse platform. With this solution combining NetApp’s advanced storage technologies with Dremio’s high-performance lakehouse platform, […]

A Powerful Partnership Data reliability, quality, and observability are crucial for organizations to make informed decisions. Integrating Monte Carlo, a leading data observability platform, and Dremio’s Unified Lakehouse Platform, brings powerful data observability capabilities to your lakehouse. Connecting the platforms is straightforward and easy to implement, offering tangible benefits to data-driven enterprises. Monte Carlo: Advanced […]

Organizations are inundated with vast amounts of data generated from diverse sources. Managing, processing, and extracting meaningful insights from this data is a significant challenge. The Data Lakehouse architecture has become the next evolution in overcoming these challenges, combining the best features of data lakes and data warehouses to deliver a unified platform for both […]

Businesses often face the daunting task of unifying data scattered across multiple platforms. Whether it's transactional data stored in PostgreSQL, customer preferences housed in MongoDB, or analytics data in a data warehouse like Snowflake, integrating these disparate sources into a cohesive dataset is a complex challenge. This fragmentation not only complicates data analytics but also […]

In this blog, we’ve explored the technologies that enable the lakehouse paradigm, such as Minio for object storage, Apache Iceberg for ACID-compliant table formats, Nessie for catalog versioning, Apache Spark for distributed data processing, and Dremio for fast, SQL-based analytics.

Several of the world's largest data-driven organizations use Dremio to facilitate rapid analytics and achieve sub-second query response times directly on the lakehouse.  Reflections are one of the primary technologies in Dremio's query acceleration toolkit. Reflections are materializations that are aggregated, sorted, and partitioned in a variety of ways, and transparently accelerate queries irrespective of […]

Learn more about Apache Polaris by downloading a free early release copy of Apache Polaris: The Definitive Guide along with learning about Dremio's Enterprise Catalog powered by Apache Polaris. The Data Lakehouse pattern, which involves building data warehouse-like functionality on top of a data lake, is rapidly becoming a popular data architecture trend. This approach […]

If you are reading this, you are probably looking at Dremio as a potential solution to many different problems: If any of the above propositions would add value to your organization, then it would be advisable to evaluate Dremio as a solution, and this guide can hopefully help you learn how to assess Dremio. Individual […]

Introduction to Iceberg Lakehouse Apache Iceberg has been transforming the data industry as a pillar of the open lakehouse architecture. It enables you to maintain a single copy of your data that can be accessed by a vast ecosystem of tools and platforms, providing flexibility and efficiency in data management. Definition and Concept of Data […]

Dremio's latest version sets a new standard in the overall performance for lakehouse platforms. This release underscores Dremio's commitment to providing the most high performance Iceberg lakehouse platform, positioning it as the market's premier lakehouse analytics platform. Reflection Enhancements  A Reflection In Dremio, is an optimized relational cache that takes advantage of the platform's advanced […]

In today's data-driven world, organizations are increasingly dealing with diverse data environments, encompassing cloud, multi-cloud, on-premises, and hybrid. Efficiently managing and querying data across these varied landscapes can be challenging, particularly when it comes to access control and workload management. Dremio has introduced significant improvements in query federation capabilities, simplifying data access and ensuring robust […]

Dremio continues to innovate and enhance the capabilities of Data Lakehouse environments with its latest feature, Auto Ingest Pipelines for Iceberg tables. This cutting-edge functionality for both Dremio Enterprise Software and Dremio Cloud changes the way organizations handle data ingestion from Amazon S3 into Iceberg tables in  Lakehouse environments. What is Automatic Iceberg Data Ingestion? […]

In today’s data-driven world, businesses face the constant challenge of managing and analyzing data across various environments—cloud, on-premises, and hybrid. With our latest release of Dremio 25.1, we continue to innovate and deliver features that enhance performance, streamline data ingestion, and improve federated query access. This release introduces improvements that collectively drive better performance, efficiency, […]