rudderstack-docs VS superset

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.

Exactly! Use spark only prepare data for dashboards. Then you can use any visualisation tool like https://superset.apache.org/ which is free.

I also like to do some data analysis on the side and recently ran across Apache Superset which describes itself as a "modern data exploration and data visualization platform". Coincidentally, Superset has a lot of Python code and can be deployed in containers (nine of them at current count!)

- Apache Superset / Preset: https://superset.apache.org (hosted version with free tier of 5 people at www.preset.io/product)

- Open source software projects that actually reward designers and product folks and include them in the development process: https://www.blender.org/ is my favorite example here! I'll also shamelessly plug the open source project I'm involved with (https://superset.apache.org/ )

Within minutes, you can find conversations on Superset’s Slack or GitHub revolving around identifying broken dashboards and finding ways to reduce the impact on users:

Supercharging Apache Superset Getting Started with Superset

I'm trying to install Apache Superset "from scratch" on AlmaLinux, but it requires a newer version of Python than is in the repos.

- Apache Superset: https://superset.apache.org/ (open source), https://preset.io/pricing/ (cloud hosting for $20 user/month or less)

To analyze the data stored in Snowflake and Postgres, we use Metabase. We chose Metabase because of it’s open source offering and easy to use interface. Other open source tools like Lightdash and Superset exist which we may add to the stack as our data team grows.

(Dashboards in Metabase and in Apache Superset)