Kafka - Syllabus

Mastering Apache Kafka: Real-Time Data Streaming with Python

Apache Kafka is a distributed event streaming platform used for building real-time data pipelines and applications. This book provides a hands-on approach to mastering Kafka, covering setup, message processing, fault tolerance, and integrations with MinIO, Apache Spark, and Airflow.

Module 1: Introduction to Apache Kafka and Event Streaming#

• What is Apache Kafka? Key features and use cases
• Kafka’s architecture: Brokers, Topics, Producers, Consumers
• Understanding partitions, offsets, and replication
• Installing and setting up Kafka on local and cloud environments

Module 2: Producing and Consuming Data in Kafka#

• Writing Kafka Producers with Python
• Writing Kafka Consumers with Python
• Understanding message serialization (JSON, Avro, Protobuf)
• Optimizing producer-consumer performance

Module 3: Kafka Topics, Partitions, and Message Retention#

• Creating and managing Kafka topics
• Configuring partitions for scalability
• Message retention policies and log compaction
• Handling duplicate and out-of-order messages

Module 4: Kafka Connect and Data Integration#

• Introduction to Kafka Connect for external system integration
• Connecting Kafka to databases, APIs, and object storage (MinIO, PostgreSQL)
• Configuring Source and Sink Connectors
• Custom Connector Development with Python

Module 5: Stream Processing with Kafka Streams and PySpark#

• Introduction to Kafka Streams API
• Processing Kafka messages in real-time with Spark Structured Streaming
• Stateful transformations, windowing, and joins in Kafka Streams
• Handling backpressure and stream optimization

Module 6: Data Pipeline Orchestration with Apache Airflow#

• Automating Kafka workflows with Airflow DAGs
• Using Airflow’s KafkaOperator for event-driven workflows
• Managing ETL pipelines with Kafka, Spark, and Airflow
• Implementing failure recovery and monitoring

Module 7: Kafka Security and Fault Tolerance#

• Securing Kafka with SSL/TLS and SASL authentication
• Implementing ACLs and Role-Based Access Control (RBAC)
• Kafka disaster recovery and multi-cluster replication
• Monitoring Kafka clusters with Prometheus and Grafana

Module 8: Deploying Kafka in Production#

• Running Kafka on Kubernetes
• Deploying Kafka in AWS, GCP, and Azure
• Scaling Kafka clusters for high availability
• Best practices for Kafka performance tuning

Hands-On Projects

Project 1: Real-Time Log Processing with Kafka and MinIO#

• Stream logs into Kafka from multiple sources
• Store and retrieve event logs in MinIO
• Analyze log patterns in real-time with Spark Streaming

Project 2: Fraud Detection System Using Kafka and Spark#

• Ingest real-time transaction data into Kafka
• Process transactions for fraud detection using PySpark
• Deploy an alerting system for anomalies

Project 3: Building a Real-Time ETL Pipeline with Kafka and Airflow#

• Automate data ingestion from APIs using Kafka Producers
• Process and store data using Kafka Connect and PostgreSQL
• Schedule and monitor ETL jobs with Apache Airflow

Project 4: IoT Sensor Data Streaming with Kafka#

• Simulate IoT sensors producing real-time data
• Process and visualize IoT data using Kafka and Grafana
• Implement real-time anomaly detection for sensor failures

Project 5: Deploying a Scalable Kafka Cluster on Kubernetes#

• Set up Kafka in a Kubernetes environment
• Implement Kafka Streams for data transformation
• Secure and monitor Kafka with industry best practices

References#

• Apache Kafka Official Documentation
• Apache Spark Streaming Guide
• Apache Airflow Documentation
• MinIO Documentation