Databricks, for data engineering and AI at real scale.
Databricks is where our biggest data engineering and machine learning workloads run — one platform for the pipeline, the warehouse, and the model training that sits on top, instead of stitching several specialist tools together.
Where Databricks earns its place in the stack.
Large-scale data pipelines
Apache Spark under the hood means transformations that would choke a single machine run comfortably distributed across a cluster.
Lakehouse architecture
One copy of the data serves BI and machine learning both — no separate warehouse and data lake quietly drifting out of sync with each other.
ML and AI model training at scale
From experimentation to production training, Databricks handles the compute without a separate platform for "the AI part."
Collaborative notebooks for data teams
Engineers, analysts and data scientists work from the same environment, on the same data, without exporting to a dozen local copies.
One platform for data engineering and AI.
Databricks earns its place on the biggest, most demanding data workloads we build — where the volume, the governance requirements, or the need to train models on the same data genuinely justifies a dedicated platform.
It's not the right tool for every job. For a smaller product, PostgreSQL usually does the job with far less operational overhead. Databricks is what we reach for once the scale or the ML requirements make that trade-off worth it.
One platform, not two
Data engineering and machine learning happen in the same place, on the same governed data — not a pipeline team and an ML team working from different copies.
Built on open standards
Delta Lake and Spark underneath mean the data isn't locked into a proprietary format — a real consideration when a client wants to avoid vendor lock-in.
Scales from a laptop-sized job to genuinely huge
The same code that runs a quick exploration scales up to a production pipeline processing far more data, without a rewrite.
Real governance and lineage
Unity Catalog gives a clear picture of where data came from and who can access it — not an afterthought bolted on once something goes wrong.
Not sure Databricks is the right fit? Ask the engineer who'd build it.
Book a 30-minute working session with a senior engineer — a real conversation about your data, not a sales call.
What a Databricks build usually includes.
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Read moreThe stack we ship on.
Pragmatic, mostly boring, and chosen because it works in production — not because it's on the front page of Hacker News.
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- You're building a data product and need a team that can deliver.
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- Your reporting is a mess and you need a real platform underneath it.