An open lakehouse, automated end to end.
DataJoinery pairs an Apache Iceberg data lake with a Snowflake data warehouse, then generates the pipelines, tests, lineage and documentation that connect them.
- BI tools and AI agentsDashboards, reports and MCP clients
- Presentation layerFacts, dimensions and bridges
- Snowflake data warehouseA Data Vault of hubs, links and satellites
- DataJoinery pipelinesGenerated, tested SQL tasks
- Apache Iceberg data lakeOpen tables for your raw data
- Source systemsApplications, databases and files
Open storage underneath. Snowflake performance on top.
Apache Iceberg data lake
Raw data from your source systems lands in open Apache Iceberg tables. Your data lake stays in an open table format rather than locked into a single engine, and Iceberg’s schema evolution absorbs changing source structures.
- Open table format
- Schema evolution
- Your cloud storage
Snowflake data warehouse
DataJoinery pushes its SQL tasks down onto Snowflake to run, so you get near-unlimited performance and scale for data volumes of all sizes. Snowflake’s variant data type is handled natively, so changing data structures load seamlessly.
- SQL push-down
- Data sharing with row-level security
- Streaming
Pipelines that write themselves.
Map your sources and business rules once. DataJoinery generates, orchestrates, tests and documents every pipeline from the lake to the warehouse.
Generated SQL tasks
Insert-only SQL tasks are generated for Snowflake, with modelling best practice applied for rapid development and quick results.
Orchestrated on Google Cloud
DataJoinery is built on Google Cloud and uses its services to scale and orchestrate data pipelines securely. No servers for you to run.
Automated tests
Unit tests run after every pipeline run, and notifications go out if issues arise, so you can resolve them proactively.
Lineage and history
Data lineage and history are tracked implicitly as you add data sources, down to field level.
Version control
Built-in versioning auto-commits every change to a git repository, and project versions keep work on the next release separate.
Living documentation
Download Markdown for an overview of entities and transformation rules, or YAML for entity definitions and data feeds.
Secure and governed by design.
Governance is built into the model itself, so every audience sees exactly the data it should.
Role-based and row-level security
Applied as data is shared, with data reaching defined stakeholders through secure views.
Personal data kept apart
Satellites separate personal from non-personal data, which helps with GDPR and POPIA compliance and makes it easy to forget a party.
Attribute security levels
Classify attributes as top secret, public or internal only, for better governance over who can see each type of data.
Authenticated AI access
AI agents reach your model through the DataJoinery MCP server, signed in with OAuth 2.1 access and refresh tokens.
Works with the cloud you already use.
Keep files, pipeline SQL tasks and dependency scripts where you want them, and reach them from within DataJoinery.
Google Cloud Storage
Every customer gets an internal DataJoinery bucket to upload files and download pipeline SQL tasks and dependencies. Connect your existing buckets securely too.
AWS S3
Connect your own S3 bucket securely to browse files in your account from within DataJoinery, and have pipeline SQL tasks delivered there.
Azure Blob Storage
Connect external Blob Storage securely and choose where files, pipeline SQL tasks and dependency scripts are stored.
Start with a small use case.
Speak to a DataJoinery data specialist today.