Details zum E-Book

Data Architecture for Data Engineers helps data engineers move beyond pipelines and tools to understand how scalable data solutions are designed.
Starting with the foundations, you will learn the principles behind data architecture, including governance, data quality, security, scalability, accessibility, lineage, and stewardship. You’ll then work through data modeling and database design, comparing conceptual, logical, and physical models, relational databases, NoSQL databases, columnar databases, normalization, denormalization, and trade-offs behind design choices. The book then moves into data pipelines, covering ingestion, processing, storage, ETL, ELT, batch and real-time processing, monitoring, optimization, and robust design. You will compare data lakes, data warehouses, lakehouses, data mesh, and data fabric, and see where AWS, Azure, Databricks, Delta Lake, Snowflake, and pipeline tools fit into modern architectures. Practical scenarios show how industries shape requirements and why architecture must follow the use case, not trend.
By the end, you will have a view of data solutions and a framework for choosing solutions that fit business needs.