The Dremio Blog

Companies are racing to operationalize agentic AI, but the path from raw unstructured data to an informed decision often breaks down in the final mile. Teams juggle schema knowledge, joins, query tuning, visualization tools, and unified governance checks before they can answer even a simple business question. With Dremio’s Agentic Lakehouse, we remove that friction […]

The rapid growth of the use of AI throughout the modern data stack has transformed how organizations extract insights from their data. With our latest release, we're excited to announce the general availability of AI Functions — a capability that brings the power of Large Language Models (LLMs) directly into SQL execution, making Dremio’s Agentic […]

Discover how Dremio’s Next Generation Cloud and enterprise MCP Server simplify data exploration with AI-driven queries, governance, and natural-language SQL.

Performance management for Apache Iceberg tables isn’t just about cleaning up small files, it’s about ensuring your data layout evolves in step with your ingestion patterns and query workloads. Dremio’s OPTIMIZE command provides the precision engineers need: merging, splitting, and reclustering data into efficient layouts while keeping metadata lean. With its flexible parameters, you can tailor compaction jobs to strike the right balance between optimization depth, runtime, and cost. At the same time, Dremio’s auto-optimization features mean you don’t always have to run these jobs manually. By letting Dremio continuously monitor and optimize Iceberg tables in the background, your most critical datasets stay query-ready without the overhead of constant maintenance.

The real secret to minimizing Iceberg table maintenance isn’t running more optimization jobs, it’s writing smarter data from the very beginning. By combining batch and streaming ingestion best practices, designing thoughtful partitioning and clustering strategies, tuning table properties, and monitoring file health, you can dramatically reduce the frequency and cost of downstream operations like OPTIMIZE.

Apache Iceberg’s snapshot model is a game-changer for time travel, auditing, and recovery, but it comes with a responsibility: old data must be managed carefully. Without proactive cleanup, tables can accumulate unnecessary files, driving up storage costs, slowing queries, and even creating compliance risks. Dremio’s VACUUM TABLE command provides the control data engineers and architects need to: Expire outdated snapshots, keeping only the versions that align with retention policies. Permanently remove deleted data to meet GDPR and CCPA requirements. Clean up orphan files to ensure storage remains lean and predictable.

This is exactly where MCP and A2A come together. MCP ensures that agents can securely interact with enterprise tools like Dremio, accessing trusted data through well-defined interfaces. A2A, in turn, provides the framework for those agents to collaborate, delegating tasks, exchanging results, and orchestrating end-to-end workflows.

Regulatory change in the food and agriculture supply chain is no longer about periodic paperwork, it’s about maintaining continuous, trustworthy, and accessible data. Whether it’s proving product lineage within 24 hours for the FDA, reporting Scope 3 emissions under the EU’s CSRD, or ensuring supplier compliance through due diligence laws, the common denominator is data complexity.

The transportation and automotive industry sits at a unique inflection point: vehicles are now data platforms as much as they are machines. With each car, truck, or bus generating gigabytes of telemetry, location, and sensor data every hour, the potential for innovation is enormous. Yet the regulatory landscape, from U.S. privacy laws to EU mandates on data portability and global cybersecurity standards, makes it clear that this data must be handled with care. Non-compliance isn’t just a legal risk; it undermines consumer trust and can derail new business models before they gain traction.

Telecom companies cannot afford to rely on platforms that “might” meet regulatory standards ,  they need proven, auditable compliance. Dremio is designed with security and certification at its core, giving providers confidence that their data practices align with industry and legal obligations.

The Energy and Utilities industry faces some of the most demanding regulatory and operational pressures in the world. From NERC CIP requirements in the power grid, to TSA directives in pipelines, to the EU’s NIS2 obligations, the stakes are high: protect sensitive data, demonstrate compliance, and maintain uninterrupted service to millions of customers.

For financial and insurance companies, compliance is not just a legal requirement, it’s a strategic imperative. The overlapping patchwork of regulations, from NAIC Model Laws and HIPAA to Basel III, MiFID II, GDPR, and PCI DSS, makes it clear that data governance and security must be woven into the very fabric of an organization’s data strategy. At the same time, customers expect real-time experiences, transparent policies, and the assurance that their most sensitive information is being handled with care.

Healthcare organizations face a unique challenge: they must manage some of the world’s most sensitive data while also unlocking its potential to improve patient outcomes, accelerate research, and streamline operations. Regulations like HIPAA, HITECH, GDPR, and the 21st Century Cures Act ensure accountability, but they also raise the bar for data security, governance, and interoperability.

AI without governance is a liability. Governance without speed is a bottleneck. Dremio’s semantic layer eliminates this trade-off by providing governance without friction. With a single, governed interface exposed to AI agents via the MCP server, organizations can unlock AI-powered analytics that are fast, secure, and business-friendly.

In the AI era, speed is the differentiator. Dremio’s autonomous reflections make it possible to achieve sub-second responses without endless tuning or fragile performance layers.