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Bijection vs Databricks
Databricks is the lakehouse company: data engineering, analytics, machine learning and AI on open formats, governed by Unity Catalog, with compute that can run in your own cloud account on AWS, Azure or Google Cloud. It has moved toward applications, with Lakebase for operational Postgres, Databricks Apps for hosting, and Agent Bricks for building agents. More than 20,000 organizations use it, according to Databricks.
The usual question is why not build operations on the lakehouse you already have. Bijection is a narrower product for that one job: a transactional database for your ontology and operations, defined in TypeScript, where each change people and agents make is approved, recorded and sent back to the systems that own the data. It sits next to your lakehouse rather than replacing it, and it is far smaller than Databricks.
- What it is
- BijectionA transactional database and runtime for your ontology, operations and applications.
- DatabricksA lakehouse platform for data engineering, analytics, machine learning and AI, now with operational Postgres, hosted apps and agents.
- Where you start
- BijectionAn operational model: objects, operations and approval rules.
- DatabricksYour data: pipelines, tables and models on open formats.
- How you build
- BijectionTypeScript definitions in your Git repository, written by developers or coding agents. There are no visual builders yet.
- DatabricksNotebooks in Python, SQL, Scala and R, apps in Streamlit, Dash, React or Node, and no-code pipelines in Lakeflow Designer, in preview.
- Where it runs
- BijectionA dedicated single-tenant environment per customer, operated by us. Not in your cloud account or on premises today.
- DatabricksA Databricks-managed control plane on AWS, Azure or Google Cloud, with compute in your own cloud account or serverless, and US government clouds.
- Operational data
- BijectionThe ontology and its operations commit together in one transactional database, with approvals and a record of every change.
- DatabricksLakebase, a serverless Postgres with branching, generally available on AWS since February 2026.
- Changes to other systems
- BijectionDeclared operations. The intent is recorded before the call goes out, and an unanswered call stays unknown until it is reconciled.
- DatabricksWritten as your own code, in jobs, apps or agents.
- AI
- BijectionModel calls are bounded steps with typed output. Agents reach data through scoped MCP endpoints and go through the same approvals as people.
- DatabricksAgent Bricks for building and evaluating agents, Genie for questions in plain language, and an AI gateway.
- Maturity
- BijectionEarly. A small team and a focused core.
- DatabricksFounded in 2013 by the creators of Apache Spark, and used by more than 20,000 organizations according to Databricks.
Choose Databricks if
- Your main workload is large-scale data engineering, analytics or machine learning.
- You want compute to run in your own cloud account.
- You want data, Postgres, apps and agents under the one governance model you already run.
Choose Bijection if
- You need the operational layer on top of your data: approvals, a record of every change and reliable writes back to your systems, without building it yourself.
- You would rather define the business in code, reviewed and versioned like the rest of your software, and let coding agents write most of it.
- You want a dedicated environment and a small team that builds your first process with you.
Sources
What this page says about Databricks comes from these public pages, last read on 28 September 2026. Products change; if something here is out of date, tell us and we will correct it.