Set up Apache Kafka for real-time data ingestion.
□ Configure Kafka brokers, topics, and partitions.
Develop Spark Streaming jobs to process data in real-time.
□ Ensure low-latency processing and scalability.
Integrate the data processing pipeline with existing data sources and the centralized data warehouse.
□ Ensure seamless data flow and minimal latency.
Store pipeline configuration details in GitHub Secrets.
□ Secrets Needed: KAFKA_BROKER_URL, SPARK_JOB_CONFIG
○ Documentation:
§ Detailed configuration and setup guides for Kafka and Spark Streaming.
§ Spark Streaming job scripts with examples.
○ Major Milestone: Low-latency data processing pipeline implemented.
○ GitHub Issue:
Implement Low-Latency Data Processing Pipeline
Description: Implement a low-latency real-time data processing pipeline using Apache Kafka and Spark Streaming.
Tasks:
Set up Apache Kafka for real-time data ingestion.
Develop Spark Streaming jobs for real-time data processing.
Integrate with existing data sources and the centralized data warehouse.
Store pipeline configuration details in GitHub Secrets: KAFKA_BROKER_URL, SPARK_JOB_CONFIG.
Milestone: Low-latency data processing pipeline implemented.
Estimated Time: 8 weeks Tasks and Detailed Requirements:
Implement Low-Latency Data Processing Pipeline
Description: Implement a low-latency real-time data processing pipeline using Apache Kafka and Spark Streaming. Tasks:
KAFKA_BROKER_URL
,SPARK_JOB_CONFIG
. Milestone: Low-latency data processing pipeline implemented.