Øyvind Strøm from Nykredit continued and showed everyone how AI can assist you in building your Data Warehouse with the help of data examples. Finally, we welcomed the TKH Luchse - 1st DBBL (1st Women's Basketball Bundesliga). First, the secrets of successful professional basketball players were presented and then all participants were able to get active and shoot a few hoops with the players outside on the outdoor court. This evening, all participants have the opportunity to take part in an evening program. There will be a city tour through Hanover, a wine tasting or dinner in a restaurant. Tomorrow we continue with the next keynote from ProSiebenSat.1.

As the first day of Data Dreamland progresses, here's a little update: When @Christof Wenzeritt and @Marc Winkelmann had finished, @Glen Dawson and @Mark van der Heijden from Coalesce took over the stage with their presentation "From Zero to Data Vault 2.0". They gave insights on how to build fast, scalable and extensible data pipelines with Coalesce. Then it was time for the lunch break - sponsored by Coalesce. All participants had the opportunity to gather together and network over delicious food and discuss the presentations and topics from today. DataVault Builder also had their time to shine. Beat Bannwart shared information on AI-driven management, explored the latest innovations and their impact on solutions.

The countdown is over, and the day is finally here: Data Dreamland 2024 kicked off this morning in Hanover. Michael Olschimke and @Christof Wenzeritt welcomed everyone to two days full of new ideas, insightful presentations and networking. Right after, Lukas Mazanek and Martin Safranek (Raiffeisenbank) got right into it with their keynote presentation "Data Mesh and Semantic Layer in mid-size Bank and how Data Vault can help". They talked about how the Data Vault 2.0 methodology contributes to building a robust semantic layer within a bank. Following this presentation, the Scalefree Duo @Marc Winkelmann and @Christof Wenzeritt delved into the Wild West of Data Mesh and how to handle data ownership with a centralized governance. Stay tuned for more updates!

Data modeling is crucial due to the increased number of data sources, diverse data consumers, and the need for optimization (both speed and cost). Mike Kahan briefly showed different modeling approaches, e.g., Star Schema and Data Vault 2.0. At the end, he delves into hybrid approaches. Have you used hybrid approaches before? If so, which modeling approaches did you mix together and how?

There are so many tools out there, but I found this video on YouTube. It did a good job of breaking down the essentials. Here's a quick list of the tools mentioned: - Basics: SQL, Python, Linux, bash scripting, network understanding - Technical Basics: Git, SFTP, PGP - Databases: PostgreSQL, MySQL, MongoDB - Data Platforms: Snowflake, Databricks, BigQuery, Redshift, Azure Synapse Analytics - Orchestration, ETL & Data Pipelines: Airflow, Dagster, SSAS, Azure Data Factory - Cloud: AWS, Azure, GCP - Others: Docker, Kubernetes, Terraform If you had to give advice on where to start, what would that be? What are your favored tools?