Analytics and AI systems are only as reliable as the data that feeds them. Data engineering is the discipline of building the pipelines, storage, and quality controls that move information from where it is produced to where it is used.
This section covers pipeline design, ETL and ELT approaches, data warehouses, data quality, and streaming data.
In-depth guides for each topic are in preparation and will be linked here as they are published.
Understand the components and engineering decisions involved in moving data between systems.
Compare data transformation approaches and learn when each may be appropriate.
Explore analytical data storage, architecture options, and warehouse design principles.
Learn how accuracy, completeness, consistency, and monitoring affect the usefulness of data.
Understand event streams, real-time data pipelines, and their operational tradeoffs.