DataDuck ETL is a straightforward, effective extract-transform-load framework for data warehousing. If you want to set up a data warehouse, DataDuck ETL makes it simple and straightforward to do.

Getting Started

Getting started with DataDuck ETL takes just a few minutes. For instructions, read the getting started page.

Why Use a Data Warehouse

If you already have your data in your main database, and probably use a web analytics product like Google Analytics, you may be wondering why you'd want a data warehouse anyway.

There's many advantages to using a data warehouse, including:

  • integrating multiple data sources so you can analyze them together
  • helping to ensure data quality by cleaning up the data and running data quality checks
  • having a single source of truth that the entire company trusts
  • connecting business intelligence products for reports and dashboards
  • using the data warehouse to build models, which may get incorporated back in the product, or used for predictions and company decision making
  • performance optimizations so your queries run fast
  • ensuring sensitive data doesn't end up in reports, by not passing it to the data warehouse (encrypted passwords, salts, etc have no practical analytics value, so they are not ETLed)
This page was autogenerated from README.md in the DataDuck project. Improvements? Let us know or open a pull request.