Practical Confluent Platform Architecture
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Practical Confluent Platform Architecture
"Practical Confluent Platform Architecture" "Practical Confluent Platform Architecture" is a definitive guide for architects, engineers, and data professionals seeking to master the design and operation of enterprise-grade event streaming systems. The book begins by establishing a thorough understanding of Kafka’s evolution and its seamless integration into the Confluent Platform, meticulously explaining core concepts and architectural components such as brokers, ZooKeeper/KRaft, Kafka Connect, Schema Registry, and Control Center. Comprehensive explorations of cluster topologies—spanning single, multi-cluster, cloud-native, and hybrid deployments—lay the groundwork for architecting resilient, event-driven solutions, accompanied by thoughtful comparisons to alternative data-moving paradigms. Diving deeper, the text covers advanced cluster engineering, robust security frameworks, and rigorous schema governance practices. Readers will learn to design for high availability, optimize performance for high-throughput environments, and orchestrate cluster scaling, disaster recovery, and geo-replication strategies. A dedicated focus on security addresses all facets from encryption, authentication, and RBAC, to compliance with strict regulatory standards like GDPR and HIPAA. Practical schema management, real-time data quality monitoring, and change management strategies ensure consistent, governed data pipelines across dynamic and distributed environments. The latter chapters position readers to harness the full capabilities of Kafka’s stream processing, data integration, and operational observability. Through detailed guidance on Kafka Streams, ksqlDB, connector ecosystem architecture, and best practices for ETL pipelines and big data integrations, readers are empowered to build CI/CD-automated, self-healing event platforms. Contemporary topics—including hybrid and multi-cloud deployments, Infrastructure as Code, platform-level DevOps, and future trends such as serverless models and AI/ML integration—ensure this book is not only a comprehensive reference but also a vision for the evolving landscape of real-time data platforms.
Kafka: The Definitive Guide
Author: Neha Narkhede
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2017-08-31
Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems
Modern Data Architectures with Python
Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka Key Features Develop modern data skills used in emerging technologies Learn pragmatic design methodologies such as Data Mesh and data lakehouses Gain a deeper understanding of data governance Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionModern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You’ll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake. Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You’ll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you’ll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you’ll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you’ll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you’ll get hands-on experience with Apache Spark, one of the key data technologies in today’s market. By the end of this book, you’ll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.What you will learn Understand data patterns including delta architecture Discover how to increase performance with Spark internals Find out how to design critical data diagrams Explore MLOps with tools such as AutoML and MLflow Get to grips with building data products in a data mesh Discover data governance and build confidence in your data Introduce data visualizations and dashboards into your data practice Who this book is forThis book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they’re not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.