deequ VS enso

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.

> At least I have yet to see one that is actually useful in the sense of a generic (or even a single-purpose-built) language

Yeah as said, https://github.com/enso-org/enso seems to be a general purpose functional programming language with visual editor, but otherwise I haven't really seen any no-code solutions worth their salt. I'm not particularly a fan of enso either, but it's the best I've seen.

Although it's not quite the same, I do like what Enso[0] is bringing to the table, especially the 1:1 visual node/language interop. Whether this is generalisable to a fully decoupled interface remains to be seen, but there's definitely potential.

[0]: https://enso.org/

I think Enso is already taken by a YC company [0]. Could get confusing.

[0] https://enso.org

COMPANY: Enso Inc. TYPE: Full time LOCATION: Europe and United States of America – fully distributed company REMOTE: Only remote VISA: No VISA required DESCRIPTION: Hi, we are Enso (enso.org, Y Combinator S21)! We are looking for an amazing Cloud engineer to join our core team. We are a remote first company, working in Europe and the USA.

Friends of mine are developing Enso (https://enso.org/), an interactive programming language with dual visual and textual representations.

Even well before Bret Victor's time, there were tools for visual programming. I have been using LabView to maintain data processing in an optical laboratory.

> I'm convinced this entire space should be visual.

At my last 2 jobs I spent entirely too much time debugging Matillion jobs, which are visual. I have my doubts that it’s the panacea that it appears to be.

That said, you may find Enso particularly interesting: https://github.com/enso-org/enso