From Data Lakehouses to Event-Driven Architecture — Master 12 data concepts and turn them into simple projects to stay ahead in IT.
When I scroll through YouTube or LinkedIn and see topics like RAG, Agents or Quantum Computing, I sometimes get a queasy feeling about keeping up with these innovations as a data professional.
But when I reflect then on the topics my customers face daily as a Salesforce Consultant or as a Data Scientist at university, the challenges often seem more tangible: examples are faster data access, better data quality or boosting employees’ tech skills. The key issues are often less futuristic and can usually be simplified. That’s the focus of this and the next article:
I have compiled 12 terms that you will certainly encounter as a data engineer, data scientist and data analyst in 2025. Why are they relevant? What are the challenges? And how can you apply them to a small project?
So — Let’s dive in.
Table of Content
1 — Data Warehouse, Data Lake, Data Lakehouse
2 — Cloud platforms as AWS, Azure & Google Cloud Platform
3 — Optimizing data storage
4 — Big data technologies such as Apache Spark, Kafka
5 — How data integration becomes real-time capable: ETL, ELT and Zero-ETL
6 — Even-Driven Architecture (EDA)
Term 7–12 in part 2: Data Lineage & XAI, Gen AI, Agentic AI, Inference Time Compute, Near…
