Build a Real-Time Streaming Pipeline

Step 1: Set up Kafka cluster

Deploy Apache Kafka using Docker Compose. Configure topics, partitions, and replication for your use case.

version: '3'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper
  kafka:
    image: confluentinc/cp-kafka

Step 2: Create producers

Write Python producers to send data to Kafka topics. Handle serialization, partitioning, and error scenarios.

Step 3: Build stream processors

Use Kafka Streams or kafka-python to process streams in real-time. Implement transformations, aggregations, and filtering.

Step 4: Set up consumers

Create consumer groups to read and process messages. Implement proper offset management and error handling.

Step 5: Add monitoring

Monitor Kafka cluster health, consumer lag, and throughput. Set up alerts for bottlenecks and failures.

Step 6: Test end-to-end

Test your streaming pipeline with realistic data volumes. Verify latency, throughput, and data correctness.