faust VS Druid

We have many similar ideas and paradigms, as one would expect from modern frameworks tackling the same problem. However, Faust has been deprecated and no longer managed or supported (there is a fork being maintained by the community at https://github.com/faust-streaming/faust). I guess Robinhood at some point decided not to spend any more resources on it, an often destiny of such side projects by large corporations. On the other hand, we hope to stay around for a long time and build to a framework and a community that loves it. This initial version was built using many libraries in order to have a working system and to gather feedback from the large community. We plan to replace eventually all, or at least most of dependancies, and replace then with Rust lib with Python bindings.

Just wanted to add that there is an actively maintained fork called faust-streaming, you can find it here: https://github.com/faust-streaming/faust

That's the basics yes. You have a pletora of things coming next. One is "Windowing" mentioned in the article, it's well explained and maybe it looks simple, but when you start with it, takes some time to wrap your mind around it.

The other things in kafka world are stateful transformations, which you would normally do using Java's Flink. The closest in python is Faust (the fork) [0]. What are stateful aggregations? something like doing SQL on top of a topic: group_by, count, reduce, and joins. So similar to SQL that you have kSQL [1].

Consumer groups IMO falls under basic usage, if you need to scale, take a look at it, and what are partitions and replicas, with that in mind, you'll be ok.

[0]: https://github.com/faust-streaming/faust

[1]: https://www.confluent.io/blog/ksql-streaming-sql-for-apache-...

The forked one, https://github.com/faust-streaming/faust, has been updated but still doesn’t seem to support joins.

Faust: Python framework

I’d like to use Python for this so I came across Faust but I’m not sure if it’s possible to create HTTP requests through Faust or if there are better alternatives.

There is a community fork which was stale for a while but got a new commit a couple days ago, this one might be usable, but is still quite risky (70 open issues etc.): https://github.com/faust-streaming/faust

There is a community which forked and actively develops faust, here’s the https://github.com/faust-streaming/faust

Hi, thanks for the answer, I'm actually using virtualenv with the requirements of the file associated with the example. However I wanted to tell you that due to covid the project is suspended but only temporarily, but there is an active fork https://github.com/faust-streaming/faust. But the example in the fork doesn't work either.

Apache Druid

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.