rudderstack-docs VS Metabase

2/ Featured tools this week - Transform and RudderStack

RudderStack is an open-source Customer Data Pipeline that allows you to track and send real-time events from your web, mobile, and server-side sources to your entire customer data stack. Our primary repository - rudder-server - is open-sourced on GitHub.

This post will explain how your customer data pipeline can help improve your data privacy and how to ensure your data privacy with RudderStack.

However, limitations to traditional CDPs, especially around connecting to best-of-breed customer tooling and exposing data for use across an organization have driven a new generation of non-CDPs. Solutions like Snowplow's (website, GitHub) data delivery platform and RudderStack's (website, GitHub) customer data platform for developers ingest data from a multitude of sources, apply in-stream transformations, and route data to your data warehouse, like Snowplow, or your warehouse plus your preferred customer tooling destinations for activation, like RudderStack.

I learned many lessons from this journey - lessons that deserve a post of their own - but there's one lesson that I learned early on that stands out. In this blog, I talk about why we merged Blendo with RudderStack, building the team and working together to build a great product.

In fact, developers have already started contributing to our documentation. Recently, Benedikt from the Userlist team created the docs for the Userlist destination for RudderStack (see the pull request here). They also built the Userlist integration, submitted a pull request, and it is now live on our platform! This is the beauty of open source!

TLDR: general steps/starting point for setting up an app with Rudderstack ( or Segment) to track customer events

As a part of our SDK roadmap at RudderStack, we wanted to develop a Flutter SDK. Our existing SDKs include features such as storing event details and persisting user details on the database, and much more. However, these features are already implemented in our Android and iOS SDKs.

To load Stripe data into Postgres, you can use platforms such as Stitch Data and Rudderstack. In this guide, we will use Stitch Data because it is a cheap and a fast solution.

With your Tracking Plans in place, you can use the existing Data Governance API's to evaluate your inbound events, payload samples and metadata to compare them against your plans. You can also use the RudderTyper tool we're releasing alongside Tracking Plans. RudderTyper is a tool for generating strongly-typed RudderStack analytics library wrappers based on your published tracking plan specs, meaning your data will conform to your defined schema upon capture.

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).