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?

Hey all, Bit of a generic question here but I'm curious about people's social media habits. I'm looking at doing some research into whether those involved in data management are more or less likely to use social media regularly knowing about the way they collect and store data. So keen to know which platforms people are on but I'd love to know in the comments if there's a reason why you're on them or if there's any you actively avoid and why.

The question arose as to the correct way to model address data. Michael Olschimke explained his point of view and shared his idea on how to proceed in the latest session of Data Vault Friday. You can find the video either here in the classroom or below. How would you model address data? Would you do it differently to Michael?

Just finished watching a breakdown of Snowflake and Databricks. While the comparison itself isn't new, the focus on their origins and how that shaped their current offerings was insightful. Key takeaways: - Foundational Philosophies: Snowflake's roots in traditional data warehousing vs. Databricks' academic, notebook-centric beginnings are evident in their core strengths even today. - Architectural Choices: Snowflake's virtual warehouses & micro-partitions vs. Databricks' Spark clusters & Delta Lake highlight differing approaches to storage, compute & scalability. - The paths of the two platforms, Snowpark and the data lakehouse concept, reflect their initial goals. Share your thoughts! Which platform's philosophy resonates more with your data workflows?

If you have not signed up for tomorrows Webinar already: Register now! The topic is: "Choosing the right Tech Stack for an Open Source powered EDW" and it will take place on August 27th at 2pm. In this session, I'll walk you through the critical process of selecting the perfect tech stack for your open-source Enterprise Data Warehouse (EDW) project. Key takeaways include: - Key considerations when evaluating vendors and leveraging open-source products - Essential components like databases, automation tools, DevOps, infrastructure, and visualization - The benefits of combining Data Vault 2.0 with an open-source tech stack Don't miss this opportunity to gain valuable insights and set your EDW project up for success! If you can't make it to the live Webinar you can still sign up and will receive an e-mail with the recording afterwards! Feel free to reach out if you have any questions.