Ultimate Guide to Data Pipelines for Data Engineers
Learn how data pipelines work, covering batch and streaming processing, ETL/ELT, data contracts, and practical architecture patterns for data engineers.
Data Engineering & ArchitectureA collection of 13 posts
Design scalable, secure, and efficient data architectures, pipelines, and data platforms that support analytics and AI.
Data architecture in real-life: Warehouse, Lake, Lakehouse, Fabric and Mesh
Warehouse, Lake, Lakehouse, Fabric and Mesh organize data differently. Understand their differences and choose the right approach.
Masterclass Data Modeling: How Data Changes Drive Schema Design
Shows why mutable versus immutable data shapes the schema, compares event sourcing, slowly changing dimensions and snapshots.
Architectural Deep Dive: Data Mesh
Explains Data Mesh, each domain owns its data product, gives a simple checklist for roles and shared rules.
Architectural Deep Dive: Data Fabric
Wish all your data lived under one roof? Data Fabric weaves lakes, warehouses and SaaS into a single governed view.
Architectural Deep Dive: Data Lakehouse
Lakehouse blends cheap object storage with warehouse smarts. See how one platform feeds BI dashboards and real-time AI together.
Architectural Deep Dive: Hybrid Data Warehousing
Cloud storage and a classic warehouse work together, cheap raw files in the cloud, clean tables in the warehouse, lists cost and governance trade-offs.
Architectural Deep Dive: Traditional Data Warehousing
Covers the classic warehouse, batch ETL, clean core tables, star marts for BI, shows why strong SQL and one truth still matter.
Data Modeling: Dive into data normalization levels
Normal forms sound scary? We make 1 NF to 5 NF clear and show when bending the rules speeds up your queries.
The Foundation of a Scalable Data Architecture: Data Modeling 101
Sketch, detail, build. Conceptual, logical and physical models turn ideas into rock-solid tables that last.