Phase 3.2 Real-time Analytics Platform

[zepor]

Estimated Time: 8 weeks Tasks and Detailed Requirements:

1. Implement Low-Latency Data Processing Pipeline: ○ Time: 4 weeks ○ Tools Required: Apache Kafka, Spark Streaming (within Azure) ○ Steps:
   1. Set up Apache Kafka for real-time data ingestion. □ Configure Kafka brokers, topics, and partitions.
   2. Develop Spark Streaming jobs to process data in real-time. □ Ensure low-latency processing and scalability.
   3. Integrate the data processing pipeline with existing data sources and the centralized data warehouse. □ Ensure seamless data flow and minimal latency.
   4. 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.

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