Anyone working with business intelligence, data science, data analysis, or cloud computing will have come across SQL at some point. We can use it to extract, manipulate and analyze data — in relational databases as well as in modern cloud environments. For a Salesforce Data Cloud implementation, I had to brush up on my SQL and data modeling knowledge.

Fun Fact: Do you know which is the most commonly used SQL command?

Read to the end of the article and you’ll find out 😉

Content
1 — What are data lakes, data warehouses and data lakehouses?
2 — What is the difference to a business intelligence tool & cloud storages?
3 — Why SQL & Data Modeling is important for Data Lakehouses or specific tools such as Salesforce Data Cloud
4 — Basics of SQL & Data Modeling for Cloud Applications
5 — Differences between Salesforce Data Cloud and other Cloud Tools
6 — Key Use Cases of Data Lakehouses for Data Scientists
7. Final Thoughts

1 — What are data lakes, data warehouses and data lakehouses?

When we talk about data platforms or data architectures, we need to understand what data lakes, data warehouses and data lakehouses are. Since we live in a world in which there is more and more data — a few years ago it was always said that ‘data is the new gold’ — we consequently also need systems that can store, process and utilize large…