Apache Kafka: Real-Time Data Streaming

Uplatz provides this comprehensive course on Apache Kafka. It is a self-paced course with pre-recorded video tutorials. You will be awarded Course Completion Certificate at the end of the course.

Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications. It was originally developed by LinkedIn and later open-sourced as part of the Apache Software Foundation. Kafka is designed to handle high-throughput, fault-tolerant, and scalable event streaming in real-time, making it a popular choice for use cases such as log aggregation, stream processing, real-time analytics, and event-driven architectures.

Key features of Apache Kafka include:

1. Distributed Architecture: Kafka is designed as a distributed system that can scale horizontally across multiple nodes or clusters. This distributed architecture provides fault tolerance, high availability, and scalability for handling large volumes of data.

2. Publish-Subscribe Messaging: Kafka follows a publish-subscribe messaging model where producers publish messages to topics, and consumers subscribe to topics to receive messages. This decoupled architecture allows for asynchronous communication between producers and consumers.

3. Topics and Partitions: Messages in Kafka are organized into topics, which are divided into partitions. Each partition is replicated across multiple brokers to provide fault tolerance and high availability. Partitioning allows Kafka to scale out by distributing data across multiple nodes.

4. Producer API: Kafka provides producer APIs for writing data to Kafka topics. Producers can publish messages to one or more topics, and Kafka handles the distribution and replication of messages across partitions.

5. Consumer API: Kafka provides consumer APIs for reading data from Kafka topics. Consumers can subscribe to one or more topics and consume messages in real-time. Kafka supports both consumer groups for parallel processing and offset management for message replayability.

6. Stream Processing: Kafka Streams is a built-in library for stream processing and real-time analytics. It allows developers to write and deploy stream processing applications directly on top of Kafka clusters, enabling real-time data transformation, aggregation, and analysis.

7. Connectors: Kafka Connect is a framework for building and running connectors that integrate Kafka with external data sources and sinks. Connectors simplify the process of ingesting data into Kafka from databases, message queues, and other systems, as well as exporting data from Kafka to external systems.

8. Scalability and Performance: Kafka is designed for high-throughput, low-latency event streaming at scale. It can handle millions of messages per second and supports horizontal scalability by adding more brokers or partitions to the cluster.

9. Reliability and Durability: Kafka provides built-in replication and fault tolerance mechanisms to ensure data durability and reliability. Messages are replicated across multiple brokers, and Kafka guarantees message delivery even in the event of node failures.

10. Security: Kafka supports authentication, authorization, encryption, and SSL/TLS for securing data in transit and at rest. It provides fine-grained access control through ACLs (Access Control Lists) and integrates with external authentication providers such as LDAP and Kerberos.

Apache Kafka is a powerful platform for building real-time streaming applications and data pipelines, offering scalability, reliability, and flexibility for handling diverse use cases in modern data architectures.

This Apache Kafka course provides participants with comprehensive knowledge and practical skills in building real-time data streaming applications using Apache Kafka. Participants will learn how to design, deploy, and manage Kafka clusters, develop Kafka producers and consumers, implement stream processing with Kafka Streams, and integrate Kafka with other systems for real-time data analytics and processing.

Apache Kafka - Course Syllabus

1. Introduction to Apache Kafka

   • Overview of Apache Kafka and its architecture
   • Understanding Kafka topics, partitions, and brokers
   • Use cases and applications of Kafka in real-time data streaming

2. Setting up Apache Kafka

   • Installing and configuring Apache Kafka clusters
   • Managing topics, partitions, and replication in Kafka
   • Monitoring and managing Kafka clusters using command-line tools and web interfaces

3. Kafka Producers and Consumers

   • Writing Kafka producers to publish messages to topics
   • Developing Kafka consumers to subscribe to topics and process messages
   • Configuring producers and consumers for high throughput and fault tolerance

4. Kafka Connect: Integrating with External Systems

   • Introduction to Kafka Connect framework
   • Building and deploying Kafka connectors for integrating with external data sources and sinks
   • Configuring connectors for various use cases such as databases, message queues, and file systems

5. Kafka Streams: Stream Processing with Kafka

   • Introduction to Kafka Streams library
   • Developing stream processing applications using Kafka Streams DSL
   • Implementing real-time data transformation, aggregation, and analytics with Kafka Streams

6. Advanced Kafka Concepts

   • Kafka architecture patterns and best practices
   • Security and authentication in Kafka clusters
   • Performance tuning and optimization techniques for Kafka deployments

7. Real-world Kafka Applications and Use Cases

   • Case studies and examples of real-world Kafka deployments
   • Building end-to-end streaming applications with Kafka for use cases such as log aggregation, event-driven architectures, and IoT data processing

8. Monitoring and Operations

   • Monitoring Kafka clusters and applications using metrics and logging
   • Performing maintenance tasks such as scaling, upgrading, and reconfiguring Kafka clusters
   • Handling common operational challenges and troubleshooting issues in Kafka deployments

9. Best Practices and Optimization

   • Best practices for designing, deploying, and managing Kafka clusters
   • Optimization techniques for improving Kafka performance, scalability, and reliability
   • Implementing disaster recovery and high availability strategies for Kafka deployments

10. Hands-on Projects and Labs

    • Hands-on exercises and projects applying learned concepts and techniques
    • Building real-time data streaming applications using Kafka
    • Implementing end-to-end data pipelines with Kafka for various use cases

11. Final Project and Certification

    • Capstone project demonstrating mastery of Apache Kafka concepts and skills
    • Evaluation and feedback from instructors and peers
    • Course completion certificate for successful participants

This syllabus covers a comprehensive range of topics to equip participants with the knowledge, skills, and practical experience needed to design, deploy, and manage real-time data streaming applications using Apache Kafka.

Everyone

Passion & determination to achieve your goals!

• Kafka Developer
• Kafka Engineer
• Kafka Architect
• Kafka Administrator
• Kafka Consultant
• Kafka Integration Specialist
• Streaming Data Engineer
• Real-Time Data Engineer
• Data Streaming Architect
• Big Data Engineer (with Kafka specialization)
• Messaging Middleware Engineer
• Data Infrastructure Engineer
• Data Operations Engineer
• Platform Engineer
• Solutions Architect
• Cloud Architect
• Cloud Engineer
• DevOps Engineer

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Uplatz Certificate of Completion

Digital certificate - Included