A robust data engineering strategy is the backbone to deliver value and ensure AI success.

A well-defined data engineering lifecycle ensures efficient processing, quality data, and accelerates business value delivery.

The data engineering lifecycle is divided into 5 stages:
✅ Generation: Source systems
A source system is the origin of the data used in the data engineering lifecycle.
✅ Storage:
Choosing a storage solution is key to success in the rest of the data lifecycle.
✅ Ingestion:
Batch versus streaming: Batch is a great way to do many common things, like training models and sending out weekly reports. In streaming data is ingested instantly after being collected.
✅ Transformation:
The data is shaped, cleaned and curated to fit the requirements.
✅ Serving Data:
Data is served to create business value in different use cases: analytics, business intelligence, machine learning, Gen AI.

Image from the book Fundamentals of Data Engineering