Build your workflows with the right tools for the job.
Deploy automation and AI to build your data workflows on a unified data engineering platform
Data Ingestion
- Accesses a library of ready to use connectors.
- Setup standard batch loading intervals or code your own.
- Ingest data incrementally.
- Eliminate the need for full reloads from the source.
- Leverage a full-featured Python framework to write and deploy your own API connectors on the fly.
- Apply data quality rules during loading.
Data Transformation
- Develop via the command line, see results in the visual DAG.
- Code just your transformation logic, leave the rest to automation.
- Mix and match SQL and Python in the same pipeline.
- Use native Snowflake, Databricks, and BigQuery functions.
- See your entire pipeline in one view.
- Make updates without hopping between tools.
Data Orchestration
- Go beyond orchestration with DataAware Automation.
- Enjoy seamless propagation of changes across entire networks of pipelines.
- Detect and debug pipeline issues in realtime as changes are propagated.
- Cut the amount of wasteful data processing by up to 63%.
- Never reprocess data that is already up to date.
Data Activation
- Put clean data back into operational systems with reverse ETL.
- Subscribe to datasets generated in other pipelines and pick up where they end.
- Eliminate dataset duplication with a built-in cataloging system.
- Connect datasets across domains and even clouds.
Data Quality
- Access a library of common data quality tests.
- Build and reuse custom data quality checks.
- Apply data quality rules during loading.
- Differentiate between data quality errors and everything else to raise smart alerts.
Data Observability
- Get end-to-end observability on your pipelines with unprecedented levels of metadata.
- Trace all the data in your system to its source.
- Drill down to the code level on high consumption areas.
- Recieve real-time insights into the health and performance of your pipelines.
