Databricks
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Databricks: lakehouse data engineering, ML and AI agents billed by the second
Databricks is a data and AI platform built on the lakehouse architecture, covering data engineering, SQL analytics, machine learning and AI agents. Founded in 2013 by the creators of Apache Spark, the company charges by consumption, down to the second, on AWS, Azure and Google Cloud. A permanent Free Edition has passed 500,000 sign-ups. Large data teams treat it as standard equipment.
Pros
- Full data lifecycle under one governance layer
- Runs on AWS, Azure and Google Cloud
- Permanent Free Edition for learning and prototyping
- Open source foundations: Spark, Delta Lake, MLflow
- Agent Bricks takes AI agents into production
Cons
- Steep ramp-up without Spark or distributed computing experience
- Bills get hard to predict at scale
- Built for technical teams, not casual use
Delta Lake, Unity Catalog, Genie: the anatomy of Databricks
Databricks rests on the lakehouse, an architecture that combines the looseness of data lakes with the discipline of a warehouse. Apache Spark and the Photon engine run the compute, Delta Lake keeps storage reliable, Unity Catalog handles permissions and audit trails.
Day to day, work happens in notebooks written in SQL, Python, R or Scala, plus Genie for less technical colleagues: you type a plain-language question, the query runs against governed data and a chart comes back.
| Component | What it does |
|---|---|
| Delta Lake | Reliable transactional data storage |
| Unity Catalog | Governance, permissions and audit |
| Databricks SQL | Warehouse and queries for BI |
| Lakeflow | Ingestion and data pipelines |
| Mosaic AI and Agent Bricks | Models, serving and AI agents |
Agent Bricks, the in-house agent workshop at Databricks
Agent Bricks builds, evaluates and deploys AI agents on governed company data. Launched at the 2025 Data + AI Summit, it claimed more than 100,000 agents built and over a quadrillion tokens processed per year twelve months later (the name is literal: you snap bricks together).
Better still, model choice stays open: OpenAI, Anthropic, Gemini and Qwen all plug into the same security boundary. AstraZeneca, 7-Eleven and Block have shipped production agents on it, a strong signal in the race between AI agent platforms.
Per-second billing, free to learn
Databricks charges by consumption, measured in DBUs and billed per second, with no flat subscription. Expect $0.15 per DBU for production jobs and $0.40 for interactive compute on AWS, while the cloud provider invoices machines and storage separately.
Good news if you just want to poke around: the Free Edition, which replaced the old Community Edition, stays free indefinitely and includes Agent Bricks, Lakebase and serverless GPUs. A 14-day trial opens up the full platform. Rates shift often in this market, so treat the official pricing page as the only number that counts.
Frequently asked questions
Is Databricks free?
Partly. The Free Edition costs nothing, has no time limit, and covers learning, prototyping and even testing Agent Bricks or Lakebase. A 14-day trial then opens the full platform. Production workloads switch to consumption billing in DBUs, with a separate invoice from your cloud provider for machines and storage.
Databricks or Snowflake, which should you pick?
They play different games. Snowflake shines at turnkey SQL warehousing and BI, while Databricks leads on pipelines, machine learning and AI agents, with open source foundations. Many companies run both side by side, and heavy AI projects lean clearly toward Databricks.
What is a DBU in Databricks?
A DBU (Databricks Unit) is the metering unit for compute: each workload burns a certain number per hour, billed per second. The rate per DBU depends on the workload type and service tier, and the underlying virtual machines and storage are invoiced separately by AWS, Azure or Google Cloud.
Is Databricks hard to learn?
There is a real learning curve, especially without Spark or distributed computing experience, a point reviewers raise often. To get going, the free Databricks Fundamentals course requires no prerequisites, and the Free Edition lets you practice in an actual workspace without a credit card.
Verdict: Heavy machinery for heavy data: data engineers, data scientists and platform leads who want pipelines, models and agents under one governance roof will settle in for the long haul.
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