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Our friends at Floryn are hosting the upcoming event at their office in Den Bosch (5 min walk from train station).

17:30 – 🍕 Welcome

18:00 – 🎤 Lights, dbt, Action: Making Analytics Engineering Visible (and Fast) Annebelle Olminkhof (Data Analyst) & Tijs Bronnenberg (Business Analyst) @ Floryn

18:30 – 🎤 Data analyst AI Agent powered by dbt Metadata - Daniel Herrera (Analytics Engineer & Developer Advocate) @ Teradata

19:00 –🥤 Drinks & Snacks

--- About the talks

🎤 Lights, dbt, Action: Making Analytics Engineering Visible (and Fast)

In 2022, our team of three data analysts at Floryn implemented dbt to build a more scalable and structured analytics workflow. At the time, most of our business logic was embedded in LookML within Looker, and dbt was more of a “nice to have” than a core component of our workflow. That changed last year when we migrated to a new BI tool, forcing us to extract all our LookML-based transformations into dbt.

This transition made us realize how much of our logic had been siloed within Looker, and it became the catalyst for fully centralizing our data models in dbt. By making dbt the foundation of our data analytics products, we standardized data transformation, improved data quality, and created a more scalable approach to managing our data.

Beyond improving our data models, dbt has enabled us to develop entirely new analytics products that wouldn’t have been possible before. With dbt as our single source of truth, our analytics engineers can now build cleaner, more reliable models while ensuring consistency across all reporting and analysis. We’ve leveraged dbt to develop metric trees that provide deeper insights into business performance as well as a data-driven warning system. By making dbt central to our analytics strategy, we’ve enhanced trust in our data and unlocked new opportunities for delivering meaningful insights.

In this talk, we'll share our journey from LookML-dependent modeling to a fully dbt-driven analytics framework, the challenges we faced, and the lessons we learned along the way. Whether you're considering dbt for your organization or looking to scale your analytics capabilities, our story highlights the power of a well-structured, centralized data strategy

🎤 Data analyst AI Agent powered by dbt Metadata

Generative AI adjacent concepts terms like "Agentic AI" or "vibe coding," are frequently used or misused as marketing hooks rather than as practical frameworks for understanding the technical reality of generative AI.

In this talk, we aim to cut through the noise by building a data analyst AI agent completely from scratch. We will not rely on any libraries or frameworks. Instead, we will focus on what it actually takes to create an agent that can generate insights from data.

One of the biggest challenges when working with large language models for sql query generation is providing the right context. Helping the model understand the structure and meaning of your data — including databases, tables, and columns and what they contain — is often the hardest part. However, if you are using a dbt, you already have access to rich metadata that can be passed to the model. This gives it the necessary understanding of the available data structures to generate accurate queries.

In this session, we will walk through how to use that metadata effectively and how to connect an LLM to your database. By the end, you’ll have a clear understanding of what it takes to build a functioning data analyst agent from the ground up.

--- 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://www.getdbt.com/legal/health-and-safety-policy

Get ready for the 3rd dbt Meetup in the Netherlands!

This time brought to you by https://vandebron.nl/Vandebron!

Schedule:

17:30 – 🍕 Welcome.

18:00 – 🎤 Utilizing Temporal Tables in dbt Incremental Models

18:30 – 🎤 Reigniting Data Modeling: Sensible Strategies for the 2020s and Beyond

19:00 – 🎤 Building on top of dbt

19:30 –🥤 Drinks

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🎤 Utilizing Temporal Tables in dbt Incremental Models

Daniel Herrera \| Principal Developer Advocate at Teradata

This session provides an in-depth examination of the integration of temporal tables within a dbt project. We'll elucidate how temporal tables serve as an efficient method for tracking newly added or updated records, thereby enhancing the efficiency of incremental models. Additionally, we'll highlight alternative strategies, including dbt snapshots, that can achieve comparable outcomes.

Discussion Points:

• Review of Incremental Models: Understanding incremental models and their runs.

• Insights into Temporal Tables: Dive into the functionalities of the TRANSACTIONTIME PERIOD fields and comprehend their significance in monitoring record additions and updates.

• Harnessing Temporal Data in DBT: An extensive analysis on how dbt models can leverage temporal markers to filter and ingest only the newly added or updated data.

• DBT Snapshots: An exploration of snapshots as an alternative approach to temporal tables.

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🎤 Reigniting Data Modeling: Sensible Strategies for the 2020s and Beyond

Niko Korvenlaita \| CTO at reconfigured

This talk covers theoretical and practical learnings from Niko's journey to productize data modeling best practices into performant dbt code, across major data warehouses. In this talk Niko will cover long forgotten data modeling best practices, as well as design you can apply to your own dbt projects. Be prepared: This is not going to be jaffle shop but instead we’ll dive to the deep end with incrementality, temporality and event ledgers. All whilst not blowing up your compute costs. Niko will also share practical learnings on how to build agile data processes in DWH centric setup.

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🎤 Building on top of dbt

Mats Stijlaart \| Analytics Engineer at Vandebron

Pieter Custers \| Tech Lead Data Analytics at Vandebron

dbt takes a central position in the data stack, but is never the start or the end of any data pipeline. In this talk Vandebron will show how they use Dagster — a data orchestration tool — and Lightdash — a self-service BI-tool — to seamlessly integrate Dbt in their stack.

Dagster and Dbt have in common that they recognize data assets as the first class citizens of a data pipeline. This common characteristic makes that they integrate very well with each other. The benefit of using Dagster is that its orchestration capabilities go beyond the data warehouse. This makes it possible to integrate dbt in a larger context, from ingestion to consumption of the data. Located at the consumption end, Lightdash is designed specifically to operate on top of dbt, and thereby lets you leverage the full potential of your data marts.

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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