The Dremio Blog

Why SQL Must Evolve in the Era of Agentic Apps and Data-Aware AI SQL has long been the universal language of data. But with the rise of Generative AI and agentic applications, a major shift is underway. We're entering an era where natural language is the interface, and agents are the client. There are two […]

By leveraging the dbt-dremio adaptor, Analytics Engineers can seamlessly sync model descriptions and tags from dbt projects with Dremio to generate wikis and labels for Business Users and Data Analysts.

Today, we're excited to announce the general availability of Dremio's latest release, delivering accelerated AI and analytics through intelligent automation. Marking the next generation of Dremio, this release represents a significant milestone in our mission to eliminate technical complexity and resource waste through autonomous capabilities, empowering teams to innovate rather than maintain. In today's economic […]

Is Query Performance Slowing Down Your AI and Analytics Initiatives? Slow analytics and AI workloads frustrate users and delay critical insights, draining productivity. If waiting for queries to load feels like the norm, you're not alone. But what if query performance could be accelerated—automatically, without requiring any specialized expertise or manual intervention? Enter Autonomous Reflections. […]

Companies of all sizes now use lakehouse architectures to power their analytics and AI workloads. Lakehouses give companies a single, trusted source of data for analytics and AI tools to access, and eliminate the need for data duplication and vendor lock-in. The catalog, or metastore, is an integral part of the lakehouse that enables tools […]

Simplifying Kubernetes Management for the Modern Data Team Enterprise customers leveraging Dremio on Kubernetes (K8s) have long valued the ability to scale engines according to their performance and cost requirements. However, until now, this capability required specialized Kubernetes expertise and manual configuration processes that added complexity to your data infrastructure management. Today, we're excited to […]

Organizations generate vast amounts of data, spanning multiple sources, tables, views, and scripts. Traditional keyword-based search methods often fall short, returning irrelevant results and making data discovery and exploration difficult and time consuming. With AI-Enabled Semantic Search, discovering relevant data to help answer questions and solve business problems becomes intuitive, simple, and quick. AI-Enabled Search […]

Unlocking Effortless Data Organization with Dremio’s Iceberg Clustering Organizations today face significant challenges optimizing their data lakes for performance while minimizing engineering overhead. That's why Dremio is excited to introduce Iceberg Clustering, a powerful capability that intelligently optimizes the data layout in your Apache Iceberg lakehouse.. With Iceberg Clustering, Dremio automatically reorganizes data within partitions, […]

You're not alone if you're currently stretching SQL Server—or any OLTP database—beyond its intended purpose to keep up with analytics demand. This pain point is shared by countless organizations as data volumes grow, dashboards become more complex, and business expectations rise.

By leveraging the Dremio and dbt 's capabilities and adhering to these best practices, business units can build a strong CI/CD pipeline that improves both the quality and speed of data transformation projects.

With Dremio + LangChain + Flask/FastAPI, you can build the next generation of AI-driven applications that provide real-time, data-powered insights.

Organizations can create intelligent, real-time, data-driven applications with minimal overhead by leveraging Dremio’s ability to unify data across sources, LangChain’s agent-based AI capabilities, and Iceberg’s scalable table format.

With Dremio, enterprises can achieve a unified approach to data modeling that simplifies workflows, reduces costs, and improves collaboration across teams. The result is a streamlined, high-performing data environment where analysts and scientists can focus on delivering insights rather than untangling complexity.

Dremio also shines in unifying data across multiple sources, allowing you to query seamlessly without managing dozens of backend connections. With its semantic layer, you can define business logic once and reuse it consistently across your applications, making your backend code leaner and your APIs more reliable.

To conclude, deploying Dremio Pipes within your organisation offers significant enhancements to your data workflows. Auto-ingest Pipes provide a robust and automated solution that overcomes the limitations of traditional data ingestion methods, ensuring reliability and minimising disruptions. By streamlining ingestion processes, guaranteeing data integrity, and eliminating errors, Dremio Pipes maximise data team productivity and free up valuable resources for revenue-generating activities. Furthermore, Dremio Pipes offer effortless scalability, enabling seamless adaption to the ever-growing volumes and complexity of your data. This agility and responsiveness ensure your organisation can leverage valuable insights and make more informed decisions with unprecedented speed and efficiency.