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Modern Data Architectures with Python

You're reading from  Modern Data Architectures with Python

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781801070492
Pages 318 pages
Edition 1st Edition
Languages
Author (1):
Brian Lipp Brian Lipp
Profile icon Brian Lipp
Toc

Table of Contents (19) Chapters close

Preface 1. Part 1:Fundamental Data Knowledge
2. Chapter 1: Modern Data Processing Architecture 3. Chapter 2: Understanding Data Analytics 4. Part 2: Data Engineering Toolset
5. Chapter 3: Apache Spark Deep Dive 6. Chapter 4: Batch and Stream Data Processing Using PySpark 7. Chapter 5: Streaming Data with Kafka 8. Part 3:Modernizing the Data Platform
9. Chapter 6: MLOps 10. Chapter 7: Data and Information Visualization 11. Chapter 8: Integrating Continous Integration into Your Workflow 12. Chapter 9: Orchestrating Your Data Workflows 13. Part 4:Hands-on Project
14. Chapter 10: Data Governance 15. Chapter 11: Building out the Groundwork 16. Chapter 12: Completing Our Project 17. Index 18. Other Books You May Enjoy

Schema Registry

Kafka guarantees the delivery of events sent from producers, but it does not attempt to guarantee quality. Kafka assumes that your applications can coordinate quality data between consumers and producers. On the surface, this seems reasonable and easy to accomplish. The reality is that even in ideal situations, this type of assumed coordination is unrealistic. This type of problem is common among data producers and consumers; the solution is to enforce a data contract.

The general rule of thumb is, garbage in, garbage out. Confluent Schema Registry is an attempt at building contracts for your data schema in Kafka. Confluent Schema Registry is a layer that sits in front of Kafka and stands as the gatekeeper to Kafka. Events can’t be produced for a topic unless Confluent Schema Registry first gives its blessing. Consumers can know exactly what they will get by checking the Confluent Schema Registry first.

This process happens behind the scenes, and the Confluent...

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