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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 discussed how to analyze a real-time data engineering problem, identify the streaming platform, and considered the basic characteristics that our solution must have to become an effective real-time solution. First, we learned how to choose a hybrid platform to suit legal needs as well as performance and cost-effectiveness.

Then, we learned how to use our conclusions from our problem analysis to build a robust, reliable, and effective real-time data engineering solution. After that, we learned how to install and run Apache Kafka on our local machine and create topics in that Kafka cluster. We also learned how to develop a Kafka Streams application to do stream processing and write the result to an output topic. Then, we learned how to unit test a Kafka Streams application to make the code more robust and defect-free. After that, we learned how to set up a MongoDB Atlas instance on the AWS cloud. Finally, we learned about Kafka Connect and how to configure...

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