spark-fast-tests VS Metabase

I was a Databricks user for 5 years and spent 95% of my time developing Spark code in IDEs. See the spark-daria and spark-fast-tests projects as Scala examples. I developed internal libraries with all the business logic. The Databricks notebooks would consist of a few lines of code that would invoke a function in the proprietary Spark codebase. The proprietary Spark codebase would depend on the OSS libraries I developed in parallel.

https://github.com/MrPowers/spark-fast-tests https://github.com/97arushisharma/Scala_Practice/tree/master/BigData_Analysis_with_Scala_and_Spark/wikipedia

If the majority of your stuff is not UDF-based there is an OS solution to run assertion tests against full data frames called spark-fast-tests. The idea here is similar in that you have a it notebook that calls your actual notebook against a staged input reads the output and compares it to a prefabed expected output. This does take a bit of setup and trial and error but it’s the closest I’ve been able to get to proper automated regression testing in databricks

I am the author of spark-fast-tests and chispa, libraries for unit testing Scala Spark / PySpark code.

I built daria (https://github.com/MrPowers/spark-daria) to make it easier to write Spark and spark-fast-tests (https://github.com/MrPowers/spark-fast-tests) to provide a good testing workflow.

quinn (https://github.com/MrPowers/quinn) and chispa (https://github.com/MrPowers/chispa) are the PySpark equivalents.

Built bebe (https://github.com/MrPowers/bebe) to expose the Spark Catalyst expressions that aren't exposed to the Scala / Python APIs.

Also build spark-sbt.g8 to create a Spark project with a single command: https://github.com/MrPowers/spark-sbt.g8

I've built popular PySpark (quinn, chispa) and Scala Spark (spark-daria, spark-fast-tests) libraries.

Remote Code Execution via H2

We've used it for about a year - Blazer is okay if you need a quick SQL query console, but we found it lacking as an actual business intelligence tool. The support for graphs and dashboards is limited, for graphs it requires you to structure the query in an exact way as you can see in the Blazer readme.

After some research on available alternatives that don't break the bank, we decided to deploy a self-hosted instance of Metabase[0]. This took only a few minutes to set up using their Docker image[1] and it has much better graphing capabilities and you can easily put a custom layout together for dashboards. Upgrading is similarly easy (just redeploy). Also easy to configure: data sources, hiding or changing the data type of a column, G Suite sign-in for our domain. Highly recommend it if you need anything more than Blazer's table output.

[0]: https://github.com/metabase/metabase

I've never used Tableau, but heard a lot of hate about it. However, in my previous role, we were big fans of Metabase (https://metabase.com). You can also self-host it, which was a huge win for us.

It seems folks want a working example. Here's one in prod:

Metabase is a BI tool, backend written mostly in Clojure. Like basically all BI tools they have this intermediate representation language thing so you write the same thing in "MBQL (metabase query language)" and it theoretically becomes same query in like, Postgres and Mongo and whatever. End user does not usually write MBQL, it's a service for the frontend querybuilding UI thing and lots of other frontend UI stuff mainly in usage.

Whole processing from MBQL -> your SQL or whatever is done via a buncha big-ass transducers. Metabase is not materially faster than other BI tools (because all the other BI tools do something vaguely similar in their langs) but it's pretty comparable speed and the whole thing was materially written by like 5 peeps

https://github.com/metabase/metabase/blob/master/src/metabas...

(nb: I used to work for Metabase but currently do not. but open core is open core)

They haven't released the source, and the compiled versions are non-trivial to diff (e.g. there are nondeterministic numbers from the clojure compiler that seem to have changed from one to the other, and .clj files have been removed from the jar).

The old version has `hash=1bb88f5`, which is a public commit: https://github.com/metabase/metabase/commit/1bb88f5

We are unsure about the right license to use, so this is a great feedback. We had a MIT license one week ago that we know that we cannot hold on long term and we felt we were lying to the community by keeping an MIT license and changing it in one year.

By using AGPL, we feel it's the right level of restriction. It's the license used by Metabase for example (https://github.com/metabase/metabase) that many companies use internally.

The solution really depends on what sort of problems you are trying to solve and who your customers are.

There are a fair few low-code solutions out there for reporting and data visualisation that are great for finance and marketing teams for example. e.g. https://metabase.com/ , https://evidence.dev/

For multipurpose SMB workflows and organisational processes, I have used n8n in the recent past and found it was quite good and incredibly easy to maintain. https://n8n.io/engineering-resources/

For enterprise processes I'd go with Camunda (solely based on recommendations and not first hand experience). Although only parts of their platform are OSS https://github.com/camunda

Bear in mind that some of these are not suitable if you want to build something that competes with them while taking their OSS code. But are perfectly fine otherwise.

Anyway, I have a collection of scripts that merge ActivityWatch data from all my machines and WakaTime exports to a PostgreSQL database which I then query with a project called Metabase. If you're curious, the scripts are in a repository called sqrt-data. I've been playing with this for ~4-5 years already I think.

Metabase | https://metabase.com | REMOTE | Full-time | Backend, Frontend, Full Stack, and DevOps engineers

Metabase is open source analytics software that lets anyone in your company rummage around in the databases you have. It connects to a number of databases / data warehouses (BigQuery, Redshift, Snowflake, Postgres, MySQL, etc).