Visual ETL
DataQloo: A Visual ETL Tool for Enterprise Data
Connect your databases and files, prepare the data on a visual canvas, and save the result as a reusable dataset — no scripts, no SQL required.
Connect, prepare, and reuse — without writing scripts
A DataQloo workflow makes a pipeline's transformation logic visible: connect a database or file, filter and summarize it into shape, and save the result with an Output node as a dataset you reuse instead of rebuild. No separate diagram, no script to read line by line to understand what happens to your data. Once it's saved, rerunning it against new data is one click — the same logic, not a rebuild.
Data stays where it lives
Instead of copying your data into another system before you can work with it, DataQloo processes it where it already lives — your connected database or file. That means less data movement, one less system to keep in sync, and results that reflect your source data directly.
Connector-agnostic by design
DataQloo isn't built around one database or file format. PostgreSQL is live today, and the same visual workflow works with any connector as it ships — see Integrations for the full, current breadth across databases, files, cloud storage, and business apps.
Learn more
- Visual ETL for PostgreSQL — a full worked example, node by node.
- How to Stop Rebuilding the Same Report Every Week — why the "reuse instead of rebuild" idea matters more than any single node.
- What Is ETL?, What Is a Data Pipeline?, DataQloo vs. Apache Airflow
Frequently asked questions
Each stage of ETL is a node on a canvas: an Input node connects to a source, Filter and Summarize nodes transform it, and an Output node loads the result into a reusable dataset. The workflow reads top to bottom — no script to trace line by line.
See DataQloo’s Visual Designer for yourself.