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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

Table of Contents (19) Chapters

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

Kafka Connect

Confluent noticed that many tasks are almost cookie-cutter and could be shared across projects and clients. One example might be listening to a topic and copying it into a Snowflake table. Confluent created Connect to allow engineers to create very generic tasks and deploy them to a cluster to run those tasks. If you need a source or sink connector that doesn’t exist, you can build your own. Connect is a very robust platform that can be a major pipeline component. Connect also offers some basic transformations, which can be very useful. Confluent Cloud offers a hosted connect cluster the same way it offers Schema Registry and Kafka Core.

To access Connect in Confluent Cloud, we will navigate to our cluster and then look for the Connectors option in the menu bar.

Figure 5.10: The Connectors option in the menu bar

Figure 5.10: The Connectors option in the menu bar

Next, we can set up a simple connector that creates fake data. We will then have the option to add a new connector.

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