Data Engineering Meetup Berlin @Zalando HQ

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🚀 Welcome to Data Engineering Berlin Meetup! 🚀

Get ready for an exciting evening packed with enlightening talks, delicious food (pizza), refreshing drinks, and fantastic networking opportunities!

Agenda

18:00 Networking, Entrance
18:30 Talk DBT & Python - How to write reusable and testable pipelines by Florian Stefan (Flatiron Health)
19:00 Talk Digital Twins: Data Engineering in the Context of Product Lifecycle Management by Georg Hildeb. (GROPYUS, ex-Zalando)
19:30 Talk First experiences with AI-assisted Analytics@Zalando by Sebastian Herold (Zalando)
20:00 Networking, Food & Drinks

Detailed Agenda

Florian Stefan (Staff Software Engineer, Flatiron Health): dbt & Python - How to write reusable and testable pipelines:
The "data build tool" (DBT) was designed to unlock software engineering best practices for SQL-based data pipelines: pipelines as version controlled directed acyclic graphs (DAGs) consisting of testable and reusable nodes. With the increasing number of cloud data warehouses and data lakehouses that allow the native execution of Python code, DBT also added support for Python models.
In this talk, I will explain how Flatiron Health uses DBT to improve and extend lives by learning from the experience of every person with cancer. We will discuss an example project setup that uses SQL as well as Python models. I will share our experiences with unit and data testing as well as with writing a reusable variable library.
The talk is well-suited for anyone with prior data warehouse or data lakehouse experience who is curious how they can leverage DBT to write test-driven and reusable data pipelines. The example project will use SQL, Python and Snowflake.

Georg Hildeb. (Principal Engineer, GROPYUS): Digital Twins: Data engineering in the Context of Product Lifecycle Management:
In today's world, computing systems form the backbone of our infrastructure, supporting everything from energy grids to military operations. These increasingly interconnected systems achieve complexities that challenge human management capabilities. Digital twins offer an advanced data engineering solution, effectively managing these complexities. In this talk, I will discuss how digital twins play a pivotal role in addressing real-world challenges in areas like product lifecycle management and discuss relevant patterns of implementation.