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Scalable Data Architecture with Java

You're reading from   Scalable Data Architecture with Java Build efficient enterprise-grade data architecting solutions using Java

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781801073080
Length 382 pages
Edition 1st Edition
Languages
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Author (1):
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Sinchan Banerjee Sinchan Banerjee
Author Profile Icon Sinchan Banerjee
Sinchan Banerjee
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1 – Foundation of Data Systems
2. Chapter 1: Basics of Modern Data Architecture FREE CHAPTER 3. Chapter 2: Data Storage and Databases 4. Chapter 3: Identifying the Right Data Platform 5. Section 2 – Building Data Processing Pipelines
6. Chapter 4: ETL Data Load – A Batch-Based Solution to Ingesting Data in a Data Warehouse 7. Chapter 5: Architecting a Batch Processing Pipeline 8. Chapter 6: Architecting a Real-Time Processing Pipeline 9. Chapter 7: Core Architectural Design Patterns 10. Chapter 8: Enabling Data Security and Governance 11. Section 3 – Enabling Data as a Service
12. Chapter 9: Exposing MongoDB Data as a Service 13. Chapter 10: Federated and Scalable DaaS with GraphQL 14. Section 4 – Choosing Suitable Data Architecture
15. Chapter 11: Measuring Performance and Benchmarking Your Applications 16. Chapter 12: Evaluating, Recommending, and Presenting Your Solutions 17. Index 18. Other Books You May Enjoy

Summary

In this chapter, we started by discussing various popular batch processing patterns. We covered five commonly used patterns to solve batch processing problems. We also looked at examples of those patterns and real-world scenarios where such patterns are used. Then, we looked at five popular patterns available to architect stream processing pipelines and how they are used to solve real-world problems in data engineering. Next, we learned about the Lambda and Kappa architectures and how they are useful for both batch and stream processing. Finally, we learned what serverless architecture is and looked at two popular serverless architectures that are used to solve many data engineering problems in the cloud.

At this point, we know how to implement batch and streaming solutions, as well as have a fair idea of different data engineering patterns that are commonly used across the industry. Now, it is time to put some amount of security and data governance into our solutions. In...

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