rudderstack-docs VS dbt-spark

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.

Spark + dbt => https://github.com/dbt-labs/dbt-spark/blob/main/docker-compose.yml

So the idea is you land your data raw in a Delta bronze layer, and then use dbt models to propagate that data forward to silver, gold, do all of your data quality, etc. and all of the actual execution is happening on a Databricks SQL endpoint (or you can use the dbt-spark adapter and run your transfors as Spark on a cluster)

Neat. Congratulations on the launch!

Apart from the fact that it could deploy to both GCP and AWS, what does it do differently than AWS Batch [0]?

When we had a similar problem, we ran jobs on spots with AWS Batch and it worked nicely enough.

Some suggestions (for a later date):

1. Add built-in support for Ray [1] (you'd essentially be then competing with Anyscale, which is a VC funded startup, just to contrast it with another comment on this thread) and dbt [2].

2. Support deploying coin miners (might be good to widen the product's reach; and stand it up against the likes of consensys).

3. Get in front of many cost optimisation consultants out there, like the Duckbill Group.

If I may, where are you building this product from? And how many are on the team?

Thanks.

[0] https://aws.amazon.com/batch/use-cases/

[1] https://ray.io/

[2] https://getdbt.com/

It will be helpful to set the stage, as no two technical stacks are the same and not all data warehouse platforms provide the same functionality. It's for the latter that we really like tools like dbt, and the sample files provided here should provide a good starting point for your specific use case. Our instance leverages the cloud version of dbt and connects to our Snowflake data warehouse, where models output tables in a designated dbt schema.

T: SQL - views and scheduled queries in BigQuery; planning to go hard with dbt as soon as I can find some breathing room)

Since all of the data is often already in the data warehouse, the logical choice is to simply just use it as a CDP. A modern data stack should consist of an end-to-end flow from data acquisition, collection, and transformation. In most cases, the easiest way to enable this goal is by leveraging tools that are purposely designed to handle a single task. Fivetran, Snowflake, and dbt are great examples of this. In fact, this is the core technology stack that every data-driven company is adopting. Fivetran handles the entire data integration aspect providing a simple SaaS solution that helps businesses quickly move data out of their SaaS tools and into their data warehouse. Snowflake provides an easy way for organizations to consolidate their data into one location for analytics purposes. Lastly, dbt provides a simple transformation tool that is SQL-based, enabling users to create data models that can be reused. These three solutions combined create an effective data management platform.

This is the issue: https://github.com/dbt-labs/dbt-spark/issues/161. Too bad they still haven't fixed it!