NL dbt meetup: 2nd Edition

Get ready for the 2nd dbt Meetup in the Netherlands!
This time brought to you by Picnic!

Schedule:
17:30 – 🍕 Welcome.
18:00 – 🎤 CI for dbt: Beyond the basics!
18:30 – 🎤 Programmable dbt models: how to automate GDPR deletion requests
19:00 – 🎤 Data alerts are hard
19:30 –🥤 Drinks

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🎤 CI for dbt: Beyond the basics!
Pádraic Slattery | Analytics Engineer at Xebia Data

As organizations continue to generate massive amounts of data, the challenges of scaling data management have become increasingly complex. To remain competitive, data teams must find ways to scale their operations and deliver high-quality data quickly. Continuous Integration (CI) has emerged as a best practice that is gaining popularity with dbt users. By automatically building and testing code changes before deployment, CI helps data teams streamline their development workflows, reduce errors, and deliver high-quality data.

In this talk, we will explore the benefits of using CI with dbt and how it can help data teams scale their operations. We will discuss how to set up a CI pipeline with dbt and cover best practices for testing data transformations. We will also share real-world examples that can be applied to any CI setup including; building models in a dedicated schema, reducing CI data scans using jinja filters and running pytests against the JSON artifacts produced in the CI run. We have seen great success with these CI configurations at our clients as they allow organisations to enforce their conventions and standards automatically, therefore allowing developers to concentrate on delivering new features rather than spending large quantities of time reviewing pull requests.

Attendees will leave this talk with a clear understanding of how CI with dbt can help data teams scale their development and deliver high-quality data faster. They will also gain practical knowledge of how to set up a CI pipeline with dbt including some tests that will benefit any dbt codebase.

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🎤 Programmable dbt models: how to automate GDPR deletion requests
Maxime Casara & Matthieu Caneill | Data Engineers at Picnic

dbt allows data engineers to reason about models as first-class citizens of the ecosystem. Most dbt entities like models, sources, exposures, tests and seeds are created and configured declaratively in YAML. This opens the door for programmable dbt models. Materializations and other macros can exploit model metadata and derive side-effects from them. In this talk, we will go over the concept of programmable dbt models, how we use it at Picnic, and illustrate it with the example of GDPR deletions.

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🎤 Data alerts are hard
Mikkel Dengsøe | Co-founder of Synq

Managing the alerting workflow is no easy task as you scale. On one side you want to know about issues as they happen but at the same time you don’t want to end up flooded with data alerts that don’t get actioned.

When data teams don’t find the right balance and end up having too many alerts that are unaddressed it can lead to negative consequences such as:

• Important issues get overlooked
• Triaging and monitoring alerts become a time sink
• The data team loses confidence in the impact of writing tests

In this talk I’ll dive into what a better alerting workflow can look like and some steps you can take to bring this into your alerts. The talk will cover:

• Different type of metadata you can surface in alerts such as ownership, importance and root cause analysis that can help you prioritise, resolve and debug issues
• Ways in which you can use alerts to bring relevant information to people outside the data team such as upstream engineering or operations teams
• Specific steps you can take to get this metadata and build it into your workflow

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Join the dbt Slack community: https://community.getdbt.com/
Join the conversation in the #local-netherlands channel in dbt Slack to connect with other data practitioners locally.

To attend, please read the Required Participation Language for In-Person Events with dbt Labs: https://bit.ly/3QIJXFb