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 from keeping up with these innovations as a data professional.
But when I reflect on the issues my clients face daily as a Salesforce consultant or as a data scientist in college, the challenges often seem more tangible: examples include faster access to data, better data quality, and better data quality. or increasing employees' technological skills. The key issues are usually less futuristic and can usually be simplified. That is the focus of this and the next article:
I've compiled 12 terms you're sure to 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 delve deeper.
table of Contents
1: data warehouse, data lake, data lake house
2 – Cloud platforms such as AWS, Azure and Google Cloud Platform
3 — Data storage optimization
4 – Big data technologies such as Apache Spark, Kafka
5 — How Data Integration Becomes Real-Time Capable: ETL, ELT, and Zero-ETL
6 – Peer-based architecture (EDA)
Part 2 Term 7-12: Data Lineage and XAI, Generation ai, Agent ai, Inference Time Calculation, Almost…
