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On September 12th, 2023, we will host an interactive, hands-on online hackathon on writing good pipelines and (dbt) tests. This after-office event will start from 18:30 hr until 22:00 hr.

  • The remote session link will be sent to you by a follow-up Meetup email prior to the session. Please remember to check your inbox.

During this event, you will be getting started on writing good data pipeline tests, and at the end of the event, you will have a clearer idea of what to do, what to avoid, and what tools to use to test the pipelines to make them as efficient as possible.

The event is free to join!

What is this hackathon all about?

Ever had an end-user come to you because the data in their dashboards was wrong? Or, because some important dataset still needs to be updated in at least 3 weeks? Serving faulty data is one of the biggest reasons for losing trust in your data program from the rest of the organisation.

This reputational damage is hard to repair. Yet, at the same time, testing of data pipelines is something that is often done either too late or not at all. We have written a blog about it and recently hosted a webinar on this topic and we realised it is a common topic that many data engineers are struggling with.

Why join this hackathon?

With this hackathon, we want to get you started on writing good tests; so that you can avoid this reputational damage. You will be deep-diving into the best practices of data quality around dbt testing, and deploying that with some efficient tools.

Who is this hackathon for?

This hackathon is for anyone who wants to improve the quality of their dbt data pipelines. Basic technical capabilities in dbt is a prerequisite. Some knowledge of Python is nice to have.

Practical information:

  • Date: September 12th
  • Time: 18:30 hr until 22:00 hr.
  • Hackathon link: The remote session link will be sent to you by a follow-up Meetup email prior to the session. Please make sure to check your inbox before the event.

Are you interested? Then wait no more and register now!

Related topics

Big Data
Data Analytics
Data Center and Operations Automation
Data Management
Database Development

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