About this Book
How to Read & Terminologies
Introducing Chapters
Part 1: Intro
1.
Introduction to the Field of Data Engineering
❱
1.1.
The History and State of Data Engineering
1.2.
Challenges in Data Engineering
2.
Introduction to Data Engineering Design Patterns (DEDP)
❱
2.1.
Understanding Convergent Evolution
3.
Convergent Evolution and its Patterns
❱
3.1.
Business Intelligence, Semantic Layer, Modern OLAP, Data Virtualization
3.2.
Materialized Views vs. One Big Table (OBT) vs. dbt tables vs. Traditional OLAP vs. DWA
3.3.
Bash-Script vs. Stored Procedure vs. Traditional ETL Tools vs. Python-Script
3.4.
Data Warehouses vs. Master Data Management vs. Data Lakes vs. Reverse-ETL vs. CDP
3.5.
Data Contracts: Haven't we validated schemas and data types all our life?
3.6.
ODS vs Kafka
3.7.
Foreign Keys vs. Temporal Joins
3.8.
Monolith (SAP/Oracle/Cloud DWH) vs. Microservices vs. Data Mesh
3.9.
More to come..
Part 2: Mastering the DEDP
4.
Data Engineering Patterns (DEP)
❱
4.1.
Cache
4.2.
In-memory and Ad-hoc Querying
4.3.
Business Transformation
4.4.
DE Packaging
4.5.
Data Sharing
4.6.
Change Management
4.7.
Data Asset
4.8.
Orchestration
4.9.
More to come..
5.
Data Engineering Design Patterns (DEDP)
❱
5.1.
Dynamic Querying
5.2.
Declarative Pipelines
5.3.
Integrated Data Platform with Open Sorce
5.4.
Asset-based Governance
5.5.
Open Data Platform: Lakehouse
5.6.
More to come..
Part 3: Navigating DEDP
Changelog
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Rust
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Navy
Ayu
📖 Data Engineering Design Patterns (DEDP)
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