Metabase VS Druid

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).

Apache Druid: Focused on real-time analytics and interactive queries on large datasets. Druid is well-suited for high-performance applications in user-facing analytics, network monitoring, and business intelligence.

Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in maintaining the freshness of results. The query in the streaming database focuses on recent data, making it suitable for continuous monitoring. Using streaming databases, you can run queries like finding the top 10 sold products where the “top 10 product list” might change in real-time.

https://github.com/apache/druid

It's always a question of tradeoffs.

The awesome-selfhosted project has a nice list of open-source analytics projects. It's really good inspiration to dig into these projects and find out about the technology choices that other open-source tools in the space have made.

Spencer Kimball (now CEO at CockroachDB) wrote an interesting article on this topic in 2021 where they created spencerkimball/stargazers based on a Python script. So I started thinking: could I create a data pipeline using Nifi and Kafka (two OSS tools often used with Druid) to get the API data into Druid - and then use SQL to do the analytics? The answer was yes! And I have documented the outcome below. Here’s my analytical pipeline for Github stars data using Nifi, Kafka and Druid.

Apache Druid is part of the modern data architecture. It uses a special data format designed for analytical workloads, using extreme parallelisation to get data in and get data out. A shared-nothing, microservices architecture helps you to build highly-available, extreme scale analytics features into your applications.

Apache Druid

But then you realize there are other databases out there focused specifically on analytical use cases with lots of data and complex queries. Newcomers like ClickHouse, Pinot, and Druid (all open source) respond to a new class of problem: The need to develop applications using endpoints published on analytical queries that were previously confined only to the data warehouse and BI tools.

Datadog's product is a bit too close to Apache Druid to have named their design system so similarly.

From https://druid.apache.org/ :

> Druid unlocks new types of queries and workflows for clickstream, APM, supply chain, network telemetry, digital marketing, risk/fraud, and many other types of data. Druid is purpose built for rapid, ad-hoc queries on both real-time and historical data.

Maybe rare for someone to be seeking their first coding job at that age. But plenty of us are in our 50s or older and still coding up a storm. And not necessarily ancient tech or anything. My current project exposes analytics data from Apache Druid and Cassandra via Go microservices hosted in K8s.

Fortunately, it is super easy to build your own leveraging the binary distribution and existing docker.sh.