The Dremio Blog

Apache Iceberg's open and extensible design empowers users to achieve optimized query performance while maintaining flexibility and compatibility with a wide range of tools and platforms. Iceberg is indispensable in modern data architectures, driving efficiency, scalability, and cost-effectiveness for data-driven organizations.

Dremio's approach removes primary roadblocks to virtualization at scale while maintaining all the governance, agility, and integration benefits.

The recent adoption of the Apache Iceberg REST catalog specification by Nessie not only broadens its accessibility and usability across different programming environments but also cements its position as a cornerstone in the data architecture landscape.

Central to the functionality of Apache Iceberg tables is their catalog mechanism, which plays a crucial role in the evolution of how these tables are used and their features are developed. In this article, we will take a deep dive into the topic of Apache Iceberg catalogs.

This exercise hopefully illustrates that setting up a data pipeline from Kafka to Iceberg and then analyzing that data with Dremio is feasible, straightforward, and highly effective. It showcases how these tools can work in concert to streamline data workflows, reduce the complexity of data systems, and deliver actionable insights directly into the hands of users through reports and dashboards.

By leveraging a lakehouse architecture, organizations can achieve significant savings on storage and compute costs, streamline transformations with virtual modeling, and enhance data accessibility for analysts and scientists.

Dremio's unwavering commitment to Apache Iceberg is not merely a strategic choice but a reflection of our vision to create an open, flexible, and high-performing data ecosystem. Our deep integration with Apache Iceberg throughout the entire stack complements Dremio's extensive functionality, empowering users to document, organize, and govern their data across diverse sources, including data lakes,  data warehouses, relational databases and NoSQL tables. This synergy forms the bedrock of our open platform philosophy, facilitating seamless data accessibility and distribution across the organization.

The allure of the data lakehouse architecture, particularly with the Apache Iceberg table format, lies in its ability to be utilized across various systems, eliminating the need for expensive data movement and migration planning. In this article, we will explore how Apache Iceberg tables are employed within Dremio—a data lakehouse platform that serves as a […]

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.

Choosing the right versioning solution involves considering your organization's specific data management needs, existing infrastructure, and the desired level of granularity for version control. Whether you prioritize the flexibility of file-level versioning with LakeFS, the seamless table-level versioning of Apache Iceberg, or the comprehensive catalog-level versioning offered by Nessie, each system presents a pathway to more efficient, reliable, and manageable data operations.

DataOps represents a paradigm shift in managing and utilizing data across organizations. By adopting DataOps principles, companies can ensure their data lakehouse architecture is not just a repository of information but a dynamic, efficient engine for innovation and growth.

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.

Data lakehouse, data virtualization, and data mesh trends significantly shift how we approach data management, addressing today's growing scale, speed, and complexity.

Dremio also facilitates a gradual and flexible adoption process. Organizations can start small, using only the necessary components, and scale up as their requirements grow. This approach reduces the initial investment and complexity, making it easier for businesses to transition to a data lakehouse architecture at their own pace.

The synergy of Apache Arrow, Apache Iceberg, and Nessie within Dremio simplifies complex data management tasks and democratizes access to data analytics, enabling a more data-driven approach in organizations.