Day 1: Apache Kafka and Data Integration
Introduction to Apache Kafka
- Kafka’s Role in Data Analytics
- Advantages of Kafka for Real-Time Data Analytics
- Key Kafka Components (Producer, Consumer, Topic, Partition, Broker)
Data Integration with Kafka Connect
- Kafka Connect Architecture and Use Cases
- Source and Sink Connectors
- Using JDBC Connector for Data Ingestion and Delivery
- HDFS and Object Storage Sink Connector Usage
- Writing and Configuring Custom Connectors
Querying Data with Kafka SQL
- What is KSQLDB?
- Concepts of Streams and Tables in KSQL
- Data Filtering and Transformation with KSQL
- Stream-Stream and Stream-Table Joins
- Creating Real-Time Dashboards with KSQL
Day 2: Real-Time Data Processing with Apache Flink and Spark
Processing Kafka Data with Apache Flink
- Overview of Flink Architecture
- Flink and Kafka Integration
- Stream Processing with Flink
- Flink Windowing Concepts and Use Cases
- Stateful Processing with Flink
Processing Kafka Data with Apache Spark
- Introduction to Spark Streaming Architecture
- Reading Data from Kafka with Spark Structured Streaming
- Window Aggregation and Filtering with Spark Streaming
- Combining Stream and Batch Processing with Spark
- Checkpointing and Fault Tolerance Management
Day 3: Advanced Applications and Performance Optimization
Advanced Kafka SQL and Stream Processing
- Aggregation and Window Functions with KSQL
- Persistent Queries in KSQL
- Optimizing Performance with Partitioning and Parallelism
Advanced Applications with Flink
- Managing State Backends in Flink
- Event Time and Watermark Usage
- Detecting Data Anomalies and Alerting with Flink
Advanced Applications with Spark
- ML Pipeline Integration with Spark Streaming
- Real-Time Analytics Dashboard with Spark and Kafka
- Spark Streaming Performance Optimization
Kafka Ecosystem Tools for Data Analytics
- Kafka Monitoring Tools (Confluent Control Center, Prometheus, Grafana)
- Using Schema Registry and Avro/Protobuf Serialization
- Log Compaction and Cleanup Policy Management
