Thoughts on a business rules engine

This page summarizes the projects mentioned and recommended in the original post on /r/dataengineering

Our great sponsors
  • WorkOS - The modern identity platform for B2B SaaS
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • SaaSHub - Software Alternatives and Reviews
  • Drools

    This repository is a fork of apache/incubator-kie-drools. Please use upstream repository for development.

    https://www.drools.org/ an open source solution that allows you to use the UI to define rules. You can even import excel files.

  • deequ

    Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.

    I had similar requirements for QA reporting on large and diverse data sets. I implemented data check pipelines, with rules in AWS Deequ (https://github.com/awslabs/deequ) running on an Apache Spark cluster. The Deequ worked well for me, but there were a few cases where I opted to write the rule checks in the data store to improve throughput (i.e. SQL checks on critical data elements on the database).

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

Suggest a related project

Related posts