The Dremio Blog

Agentic AI offers several compelling benefits that are transforming how we work and solve problems. The key difference from traditional AI is agency, the agent’s ability to plan, use tools, take actions in sequence, and work toward goals rather than just responding to individual prompts. There are many upsides to embracing AI agents in your […]

Dremio's integrated platform transforms the agentic analytics challenge from a complex systems integration problem into a straightforward data conversation. By unifying federation, autonomous performance, a business-centric semantic layer, and conversational interfaces, Dremio eliminates the friction that has historically kept valuable insights locked away in data silos.

Success depends on three non-negotiable pillars: deep business context from a semantic layer, universal access through live data federation, and interactive speed delivered by autonomous performance features.

Dremio's power can be fully leveraged within the Python ecosystem thanks to these versatile community-driven libraries.

Dremio's approach reveals a clear vision for the future of the data stack: AI is not a bolt-on feature but a foundational capability woven throughout the entire data lakehouse platform.

Dremio’s Open Catalog delivers a free, interoperable, and flexible foundation for the modern data lakehouse. By embracing open standards and a "bring your own" philosophy for compute, storage, and AI, Dremio provides a platform that prioritizes user choice and cost control over vendor lock-in. This commitment to openness gives you the flexibility to select the best tools to achieve your data analytics goals.

Achieving elite Dremio performance is a blend of smart architecture, understanding the platform's autonomous capabilities, and applying targeted, tool-assisted tuning. By moving beyond the basics, you can create a truly interactive experience for your data consumers.

Dremio is the engine that finally transforms the lakehouse into a high-performance BI reality. By unifying namespaces, automating acceleration via Reflections, and utilizing the power of Apache Iceberg, it moves the organization away from the "data moving" era.

The Dremio performance story is a narrative of engineering excellence meeting operational simplicity. By combining the efficiency of Apache Arrow, the "magic" of Iceberg-backed Reflections, and the intelligence of an AI-driven optimizer, Dremio has built a system where performance is an automated byproduct of the architecture.

By combining open standards, autonomous lakehouse operations, and agentic interaction, Dremio turns the journey from raw data to trusted insight into an incremental, flexible process, not a long, disruptive platform migration.

Large Language Models (LLMs) are capable of understanding and generating language. However, they do not understand or process language in the same way that a person like you or I does.  When reading text, humans construct meaning by processing the syntax of each sentence as it unfolds. This involves combining word definitions, using context from […]

The traditional barriers between complex, distributed data and clear, actionable answers have been removed. With an intelligent, AI-native data lakehouse, you no longer need to be a data engineer to explore your data; you just need to be curious.

Discover the top AI tools for data engineers. Compare leading platforms and see how Dremio automates pipelines and boosts performance at scale.

Explore 18 strategies for optimizing SQL queries and learn how enterprises improve speed, reduce compute costs, and maintain efficient workloads with Dremio.

Dremio Reflections don't just accelerate queries; they accelerate the entire analytics lifecycle by removing performance tuning from the critical path. By reclaiming the time and cognitive overhead traditionally spent on manual materialization management, Reflections empower data teams to shift their focus from infrastructure management to insight delivery.