The Dremio Blog

We'll see that because of Iceberg's metadata, we can efficiently derive table changes, and due to its efficient transaction and tool support, we can process those changes effectively. Although, there are different CDC scenarios so let's cover them.

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.

By embracing both data unification and decentralization, organizations can achieve a harmonious balance that leverages the strengths of each approach. Centralized access ensures consistency, security, and ease of governance, while decentralized management allows for agility, domain-specific optimization, and innovation.

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, […]

Integrating Monte Carlo with Dremio’s Unified Lakehouse Platform provides a robust framework for implementing advanced data observability practices in a data lakehouse environment. By leveraging this powerful combination, organizations can significantly improve their data reliability, catch issues early, and maintain the integrity of their data assets.

We'll delve into how querying Iceberg metadata tables in Dremio can provide invaluable insights for table auditing, ensuring data integrity and facilitating compliance.

By leveraging Dremio's unified analytics capabilities and Streamlit's simplicity in app development, we can overcome the challenges of data unification.

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 […]

Iceberg catalogs are essential in the Iceberg lakehouse ecosystem, enabling core features such as table portability, concurrency control, governance, and versioning. As data lakehouse adoption grows, solutions like Nessie, and Apache Polaris (incubating) provide the necessary tools to streamline data management across diverse environments. With innovations like catalog versioning and centralized governance, these catalogs ensure consistency and reliability and empower organizations to manage their data more efficiently.

I hope this guide has helped you on your journey in exploring whether Dremio is the right solution to eliminating data silos, reducing costs and overall improving your organizations data outcomes.

Choosing an Iceberg Lakehouse for your business means investing in a data architecture that meets your current needs and scales and evolves with your organization while delivering significant cost savings and enhanced analytics capabilities. As you consider the next steps for your data strategy, the Iceberg Lakehouse offers a compelling, forward-looking solution that will drive your business's success in the data-driven future.

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? […]