Serverless Data Processing: Building Elastic Pipelines
Build scalable data pipelines using serverless services. Learn how AWS Lambda, Azure Functions, and Cloud Functions integrate for cost-effective processing.
Posts tagged with #data-engineering
Slowly Changing Dimensions: History in Data Warehouses
Master Slowly Changing Dimension techniques including Type 1, Type 2, and Type 3 for maintaining historical accuracy in data warehouses.
Snowflake Schema: Normalized Dimension Design
Explore snowflake schema design, where dimension tables are normalized into related sub-tables, reducing redundancy and enabling shared reference data.
Star Schema: The Workhorse of Dimensional Data Modeling
Discover why star schema remains the dominant approach for analytical databases, how it enables fast queries, and when to use it versus alternatives.
Data Warehousing
OLAP vs OLTP comparison. Star and snowflake schemas, fact and dimension tables, slowly changing dimensions, and columnar storage in data warehouses.
Data Engineering Roadmap: From Pipelines to Data Warehouse Architecture
A practical learning path for building reliable data pipelines, choosing between batch and stream processing, and designing analytics infrastructure that actually works in production.