spark-excel VS deequ

Not natively. But the com.crealytics.spark.excel library has had great results for us. There are still some cases where pandas manipulation is needed with Excel files that have weird header setups.

Spark 3.2 has a to_excel() method, but not Spark 3.1, so you'll need to use an external library such as https://github.com/crealytics/spark-excel

Have you checked spark-excel's documentation? The dataAddress option seems to be what you're looking for.

Are you using this library to load xlsx files? https://github.com/crealytics/spark-excel

I found similiar issues on their github: https://github.com/crealytics/spark-excel/issues/227

Plug-in in question is GitHub - crealytics/spark-excel: A Spark plugin for reading Excel files via Apache POI , but I guess it could be any. Assuming that I can read the plain code in an individual .scala file how do I learn to understand how it all pieces together and what the underlying code being run is?

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.