Drools VS deequ

I've met a few young programmers who heard somewhere that object-oriented programming was bad and they want to get the enlightenment of functional programming that they've heard about. Frequently they travel from job to job like itinerant martial artists always looking for somewhere where they practice the true technique but they always seem disappointed as it is just as easy if not easier to screw up handling errors with monads than it is with exceptions and they find analogies like "a monad is like a burrito" just get them more confused.

As for something profound I'd point you to

https://github.com/cerner/clara-rules

which many people will struggle with because like many other production rules engines in LISP (and many other examples of simple compilers), there is hardly any code! Contrast that to the orders of magnitude larger rules engine Drools

https://github.com/kiegroup/drools

which is so crazy-complicated primarily because the Drools language is Java-based so you need all sorts of things that Clara or CLIPS don't need.

IMHO you're better off using something like https://www.drools.org/ for this. Non-devs writing code is a pipe-dream. It never works out.

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

Hi all, I am doing a project in rust right now (a web server with axum, postgres, redis), and am in need of a good rule engine like Drools in java (https://www.drools.org/). From what I have searched, I couldn't find any that are well maintained or provide similar levels of functionality.

Also ... https://www.drools.org/

Drools - Drools - Business Rules Management System (Java™, Open Source)

Drools (a Business Rule Engine),

Hopefully you already know that Drools is a business rules management system. You write rules in either "drl" syntax, in spreadsheets, or in glorified flowcharts, and then let your application throw data at it.

There's no straightforward way to drop and rerun a metric collection. For example, say you detect a problem in your data. You fix it, rerun the pipeline, and replace the bad data with the good. You'd want your metrics history to reflect the true state of your data. But the "bad run" cannot be dropped. Issue

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).

Hey all! At the companies where I've worked as a developer, I've found that business stakeholders typically want a concrete way to check and assure the quality of data that pipelines are producing, before other downstream systems and users get impacted. I've tested solutions like Deequ, but I found that it made building compliance and data rules a bit more complicated and put a greater emphasis on developers to get the rules right that business was expecting. I also experienced issues with running checks in parallel and getting row level details about the failures.

GE is arguably the most well known OSS alternative to Soda Core. The third option is deequ, originally developed and released in OSS by AWS. Our community has told us that Soda Core is different because it’s easy to get going and embed into data pipelines. And it also allows some of the check authoring work to be moved to other members of the data team. I'm sure there are also scenarios where Soda Core is not the best option. For example, when you only use Pandas dataframes or develop in Scala.

I wonder how this compares with tools like DeeQu (https://github.com/awslabs/python-deequ - requires Spark) or Pandas Profiling? One plus side I can see is that it doesn't require Apache Spark to run profiling (though a quick look at the code indicates that they are working on Spark support) and can work with real time systems.

There are so many of them in big data, e.g. Kafka, Spark, Flink, Delta, Snowplow, Finagle, Deequ, CMAK, OpenWhisk, Snowflake, TheHive, TVM-VTA, etc.