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Fundamentals of Data Engineering
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Fundamentals of Data Engineering
by Joe Reis, Matt Housley
Fundamentals of Data Engineering
1. Data Engineering Described
What Is Data Engineering?
Evolution of the Data Engineer
Data Engineering Defined
Data Engineering and Data Science
Data Engineering Skills and Activities
Data Maturity and the Data Engineer
The Background and Skills of a Data Engineer
Business Responsibilities
Technical Responsibilities
The Continuum of Data Engineering Roles, from a to B
Data Engineers Inside an Organization
Internal Vs External Facing Data Engineers
Data Engineers and Other Technical Roles
Data Engineers and Business Leadership
Summary
Further Reading
Links
2. The Data Engineering Lifecycle
What Is the Data Engineering Lifecycle?
The Data Lifecycle Versus the Data Engineering Lifecycle
Generation - Source Systems
Ingestion
Storage
Transformation
Serving Data for Analytics, Machine Learning, and Reverse Etl
The Major Undercurrents Across the Data Engineering Lifecycle
Data Management
Orchestration
Dataops
Data Architecture
Software Engineering
Chapter Summary
Further Reading
Data Transformation and Processing
Undercurrents
Further Watching
3. Choosing Technologies Across the Data Engineering Lifecycle
Cost Optimization: Total Cost of Ownership and Opportunity Cost
Total Cost of Ownership
Opportunity Cost
Today Vs the Future - Immutable Vs Transitory Technologies
Our Advice
Location: On-Prem, Cloud, Hybrid, Multi-Cloud, and More
On-Premises
Cloud
Hybrid Cloud
Multi-Cloud
Decentralized - Blockchain and the Edge
Be Cautious with Repatriation Arguments
Our Advice
Build Vs Buy
Open-Source Software (Oss)
Proprietary Walled-Gardens
Our Advice
Monolith Vs Modular
Monolith
Modularity
The Distributed Monolith Pattern
Our Advice
Serverless Vs Servers
Serverless
Containers
When Servers Make Sense
Our Advice
Undercurrents and How They Impact Choosing Technologies
Data Management
Dataops
Data Architecture
Orchestration
Software Engineering
Summary
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