Quill VS deequ

https://github.com/zio/zio-quill

This library does exactly what you prescribe. Pretty sure under the hood it's using macros with string templates

But I can connect to Postgress with something like Quill and run sophisticated queries to fetch data. Which then got me thinking, what is the difference between using Spark to connect to the database and using something like Quill or your normal pure JDBC driver?

Just as an example, say you are using quill ( https://getquill.io/ ) to query your database.

I think Quill is the closest to your request: https://github.com/zio/zio-quill

If this still looks like too much hassle, you can always go a bit higher-level and use something like Quill, which is also a powerful approach that uses a different, more ORM-like style.

I'm using it mostly for full-stack web development with ScalaJS (https://www.scala-js.org) in the frontend (https://outwatch.github.io/docs/readme.html) and in the backend with AWS lambdas.

The ecosystem is currently in the process of porting all the libraries to Scala 3. So if you're new to Scala, I'd recommend to start with Scala 2, which is rock-solid and already very powerful.

I never worked with SQLAlchemy. But on the scala database side, popular libraries are Doobie (https://tpolecat.github.io/doobie) and Quill (https://getquill.io). Keep in mind that these are for Scala on the JVM. On the ScalaJS side I'm using the javascript library pg. But I'd like to try if it works well with Prisma soon.

The nice thing about ScalaJS is, that you can use Javascript libraries. And if there are typescript facades, then you can transpile these to Scala and use them in a type safe way (https://scalablytyped.org).

I know that libraries like Scodec and shapeless were rewritten practically from scratch for Scala 3, taking advantage of the next syntax and internals, as well as protoquill - a Scala 3 implementation of Quill.

Akka HTTP, Cats, Quill, ninny, Monix Observable, mill.

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.