The Dremio Blog

Incorporating the Polaris Data Catalog into your Data Lakehouse architecture offers a powerful way to enhance data management, improve performance, and streamline data governance. The combination of Polaris's robust metadata management and Iceberg's scalable, efficient table format makes it an ideal solution for organizations looking to optimize their data lakehouse environments.

The combination of dbt and Dremio creates a powerful, agile data transformation pipeline. With dbt’s ability to standardize and automate transformations, and Dremio’s unified data platform optimizing and accelerating queries, organizations can unlock the full potential of their data.

Tools like Dremio are critical for breaking down data silos and providing real-time access to valuable insights. By simplifying data integration and making it actionable, these capabilities empower teams to make data-driven decisions and collaborate more effectively, ultimately delivering superior customer experiences and driving growth.

By integrating GitHub Actions into your dbt and Dremio workflows, you’ve unlocked a powerful, automated CI/CD pipeline for managing and version-controlling your semantic layer.

By embracing the right orchestration tools, you can automate your data workflows, save time, reduce errors, and scale your data platform with ease. So, whether you're managing daily queries or orchestrating complex data pipelines, Airflow combined with Dremio is the way forward for efficient and reliable orchestration.

The hybrid data lakehouse represents a significant evolution in data architecture. It combines the strengths of cloud and on-premises environments to deliver a versatile, scalable, and efficient solution for modern data management. Throughout this article, we've explored the key features, benefits, and best practices for implementing a hybrid data lakehouse, highlighting Dremio's role as a central component of this architecture.

In this tutorial, we demonstrated how to set up Dremio, promote and format a dataset, create a complex query, and then use an Aggregate Reflection to optimize that query for better performance. With this approach, you can easily scale your data analytics workload while keeping query times low.

With Dremio and Apache Iceberg, managing partitioning and optimizing queries becomes far simpler and more effective. By leveraging Reflections, Incremental Reflections, and Live Reflections, you can maintain fresh data, reduce the complexity of partitioning strategies, and optimize for different query plans without sacrificing performance. Using Dremio’s flexible approach, you can balance keeping raw tables simple and ensuring that frequently run queries are fully optimized.

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.