rudderstack-docs VS ClickHouse

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.

Yes, we are working on it! :) Taking some of the learnings from current experimental JSON Object datatype, we are now working on what will become the production-ready implementation. Details here: https://github.com/ClickHouse/ClickHouse/issues/54864

Variant datatype is already available as experimental in 24.1, Dynamic datatype is WIP (PR almost ready), and JSON datatype is next up. Check out the latest comment on that issue with how the Dynamic datatype will work: https://github.com/ClickHouse/ClickHouse/issues/54864#issuec...

In our repository, I've set up a few hard limits: each translation unit cannot spend more than a certain amount of memory for compilation and a certain amount of CPU time, and the compiled binary has to be not larger than a certain size.

When these limits are reached, the CI stops working, and we have to remove the bloat: https://github.com/ClickHouse/ClickHouse/issues/61121

Although these limits are too generous as of today: for example, the maximum CPU time to compile a translation unit is set to 1000 seconds, and the memory limit is 5 GB, which is ridiculously high.

I have a project dedicated to this topic: https://github.com/ClickHouse/ClickBench

It is important to explain the limitations of a benchmark, provide a methodology, and make it reproducible. It also has to be simple enough, otherwise it will not be realistic to include a large number of participants.

I'm also collecting all database benchmarks I could find: https://github.com/ClickHouse/ClickHouse/issues/22398

ClickHouse: A fast open-source column-oriented database management system. ClickHouse is designed for real-time analytics on large datasets and excels in high-speed data insertion and querying, making it ideal for real-time monitoring and reporting.

Today we're going to create an UDF (User-defined Function) in Golang that can be run inside Clickhouse query, this function will parse uuid v1 and return timestamp of it since Clickhouse doesn't have this function for now. Inspired from the python version with TabSeparated delimiter (since it's easiest to parse), UDF in Clickhouse will read line by line (each row is each line, and each text separated with tab is each column/cell value):

For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules.

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.

Proton is a lightweight streaming processing "add-on" for ClickHouse, and we are making these delta parts as standalone as possible. Meanwhile contributing back to the ClickHouse community can also help a lot.

Please check this PR from the proton team: https://github.com/ClickHouse/ClickHouse/pull/54870

curl https://clickhouse.com/ | sh

But I continue to find garbage in some of our CI scripts.

Here is an example: https://github.com/ClickHouse/ClickHouse/pull/58794/files

The right way is to:

- always pin versions of all packages;