The Dremio Blog

Compare 19 Databricks alternatives for data analytics, AI and lakehouse workloads, built to accelerate SQL and simplify operations.

The main differences come down to architecture, cost and AI readiness. Snowflake requires copying data into its warehouse and charges per compute credit consumed. Dremio queries data in place with Zero-ETL federation and includes an AI semantic layer, a built-in AI agent,and autonomous optimization. For a full breakdown, see the Dremio vs Snowflake comparison.

Accelerating the Agentic Lakehouse Today, we’re thrilled to announce that Dremio has agreed to join forces with SAP, pending regulatory approval. Together, we will be able to deliver one open platform where agents reason over all enterprise data, decide, and act. This acquisition will give us the scale and backing to accelerate our agentic vision, […]

The semantic layer is not a one-time project. It is a living system that grows with your organization's data needs. Start small, prove value on the metrics that matter most, and expand from there.

Microsoft Fabric now includes the Mirrored Dremio catalog, a new item type that brings Dremio-managed Iceberg tables into OneLake without copying data or building pipelines. If your organization runs Dremio as its lakehouse platform, your Fabric users can now query that data from Power BI, the SQL analytics endpoint, and other Fabric experiences, while the […]

For all the many improvements data lakehouses bring to analytics, there's one uncomfortable trade-off: deleting rows is expensive. In a system built around immutable Parquet files, a delete is actually a rewrite. You read the file, filter out the rows you don't want, and write a new file. At scale those I/O costs mount up […]

Dremio eliminates that choice. Connect your sources, build your semantic layer, enable AI access, and start migrating to Iceberg when you are ready.

Winning in financial services and insurance now depends on how well you understand each customer or policyholder and turn that understanding into timely, relevant, and trusted actions. Customer 360 and hyper‑personalization, powered by an agentic data and AI foundation, are now essential for banks, insurers, wealth and asset managers, and financial technology firms that want […]

It is a great thing that so many platforms now support Apache Iceberg. More support means more flexibility for everyone. But if your intention is to make Iceberg your primary analytics format, then "supports Iceberg" and "built for Iceberg" lead to very different outcomes.

Most data teams think about lineage at the table or column level. Which pipeline wrote to this table? Which upstream source feeds this column? Those are useful questions, but they stop short of what actually matters in an audit or incident investigation: which specific rows were affected, by which operation, and when. Apache Iceberg v3 […]

Dremio offers three distinct integration points to the data in your lakehouse. This gives users the freedom to pick the interface, models, and tools that are right for them. Whether you're a business user, a seasoned data analyst, or a developer, we have an integration that will suit how you like to work. The built-in […]

Agentic AI is how leading financial institutions turn risk aggregation and regulatory reporting from a slow, manual burden into a real‑time, always on advantage, boosting accuracy, slashing costs, and accelerating insight. Dremio’s Agentic Lakehouse gives financial institutions the data foundation and AI agents they need to industrialize risk aggregation and regulatory reporting, with higher accuracy, […]

Why Dremio built a command-line tool designed to be introspected by machines. When GitHub launched gh in 2020, they framed the problem as context switching: developers losing flow by bouncing between terminal and browser. When Stripe shipped their CLI, the pain was webhook testing. When Fly.io built flyctl, the argument was philosophical: web apps aren't […]

This post walks through the four capabilities that make Dremio the easy button for building a unified, governed, AI-native data platform.

The data lakehouse takes a different approach. It deconstructs these components into modular, interchangeable layers, each built on open-source standards. This post walks through the Apache Software Foundation projects that form the core of the open lakehouse stack, what each one does, and how Dremio integrates them into a production-ready platform with built-in AI capabilities.