Master Kafka Real-Time Data Processing
Learn to build scalable, real-time data pipelines with Apache Kafka in this hands-on course designed for developers and data engineers.
This Course Includes
- 30 Hours of Hands-on Training
- Tools: Kafka, Zookeeper, Kafka Streams
- Online Practical Labs
- Learn Data Streaming Skills
- Real-World Projects and Exercises
- Data Pipeline Design and Optimization
Things You'll Learn
- Kafka architecture and core concepts
- Real-time data streaming and processing
- Building producers and consumers
- Kafka Streams and KSQL for data transformation
- Scaling and optimizing Kafka clusters
Course Content
Introduction to Apache Kafka
- Overview of Kafka and its role in real-time data processing.
- Key concepts: Topics, Partitions, Brokers, and Replication.
- Understanding Kafka’s architecture and components.
- Hands-on exercise: Setting up a Kafka environment with Docker.
- Comparing Kafka with traditional messaging systems.
- Real-world use cases: IoT, log aggregation, and event sourcing.
- Introduction to Zookeeper and its role in Kafka.
- Basic Kafka commands and configurations.
Kafka Producers and Consumers
- Building Kafka producers to publish data streams.
- Configuring producer properties: acks, retries, and batching.
- Hands-on lab: Creating a producer with Java/Python.
- Understanding consumers and consumer groups.
- Hands-on exercise: Subscribing to topics and consuming messages.
- Managing offsets and ensuring data reliability.
- Real-world scenario: Streaming live sensor data.
- Error handling and retries in producers/consumers.
Kafka Streams and KSQL
- Introduction to Kafka Streams for real-time processing.
- Building stream processing applications with Kafka Streams.
- Hands-on lab: Filtering and aggregating data streams.
- Using KSQL for querying Kafka topics.
- Hands-on exercise: Writing KSQL queries for real-time analytics.
- Stateful processing: Joins, windows, and aggregations.
- Real-world case study: Processing e-commerce transaction data.
- Best practices for stream processing design.
Scaling and Optimizing Kafka
- Configuring Kafka for high throughput and low latency.
- Managing partitions and replication for scalability.
- Hands-on lab: Scaling a Kafka cluster with additional brokers.
- Monitoring Kafka with tools like Kafka Manager and Prometheus.
- Optimizing performance: Compression, batching, and tuning.
- Hands-on exercise: Load testing a Kafka cluster.
- Handling failures: Broker recovery and rebalancing.
- Real-world example: Scaling for a global messaging platform.
Advanced Kafka Integrations
- Integrating Kafka with databases (e.g., Kafka Connect).
- Hands-on lab: Streaming data from MySQL to Kafka.
- Using Kafka with big data tools: Spark, Flink, and Hadoop.
- Hands-on exercise: Processing Kafka streams with Spark.
- Securing Kafka: SSL, SASL, and ACLs.
- Real-world scenario: Building a data pipeline for analytics.
- Deploying Kafka in the cloud (AWS, GCP, Azure).
- Best practices for production-ready Kafka systems.
Why Choose This Course?
- Led by Kafka experts with industry experience
- Hands-on labs with real-time data scenarios
- Flexible online format for your convenience
- Practical projects to build your portfolio
- Prepares you for Kafka certifications