deequ VS circe

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.

Using Circe so I define some classes that contain my custom Encoder[BusinessObject] in a file and I use that whenever I want to save/store a record, or handle a web request or respose. I also represent my mongo queries as JSON objects that I can freely build then pass to the driver.

Circe adopters should be using Scala https://github.com/circe/circe

If you look at Circe's github repo you will see a very large list of very recognizable companies, that should give you some idea. Circe isn't the ONLY Json parsing library, but it is probably the most popular, so - should give you a rough idea of the types and variety of companies using Scala.

Has the worst error messages I've ever seen for a parser. "Attempt to decode value on failed cursor" is not helpful when all you have is missing fields. Has been an issue for 5 years.

Circe tries to drop Scala 2.12 support in retaliation for not enough users paying them.

Circe: https://circe.github.io/circe/

PR for the same functionality in Scala3: https://github.com/circe/circe/pull/1800

Otherwise I tend to just use ZIO-JSON or Circe both of which have been updated for Scala 3.

I've updated results of benchmarks of 12 JSON parsers for Scala: - AVSystem's scala-commons - Borer - Circe - DSL-JSON - Jackson - jsoniter-scala - Play-JSON, - play-json-jsoniter - Spray-JSON - uPickle - weePickle - zio-json