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From Batch to Real-Time: Migrating a Traditional Hadoop Hive Warehouse to a Kafka-Centric Streaming Platform on AWS

Hadoop Hive Warehouse

Migrating a Traditional Hadoop Hive Warehouse to a Kafka-Centric Streaming Platform on AWS

  • Use Case: Organizations traditionally using Hadoop Hive warehouses for batch analytics face challenges in meeting modern real-time data processing needs, such as fraud detection, IoT data monitoring, and clickstream analysis. Migrating from batch-oriented pipelines to a Kafka-centric streaming platform enables low-latency analytics, real-time decision-making, and scalable event-driven architectures.

Objective

  • To design and implement a migration strategy from Hadoop Hive (batch-oriented) to a Kafka-centric real-time data pipeline.

    To compare latency, throughput, and cost between the two architectures.

    To demonstrate how AWS-managed services can simplify and optimize streaming workloads.

Project Description

  • Legacy System Setup (Baseline):

    Deploy Amazon EMR with Hadoop + Hive to store and query batch datasets.

    Run ETL jobs on batch data (e.g., daily/hourly ingestion from logs).

    Use Hive tables for analytics and benchmarking.

Streaming Platform Migration

  • Deploy Amazon MSK (Managed Kafka) as the backbone for real-time ingestion.

    Ingest data from streaming sources (IoT sensor data, logs, or clickstream).

    Use Amazon EMR with Spark Structured Streaming or Amazon Kinesis Data Analytics (Flink) for real-time transformations.

    Store transformed data in Amazon S3 (data lake) and Amazon Redshift for analytics.

Coexistence & Hybrid Querying

  • Benchmark performance between Hive batch queries vs. Kafka-streamed queries.
  • Use Athena and Redshift Spectrum to query data across both environments.

Visualization & Insights

  • Build dashboards in Amazon QuickSight to compare latency, throughput, and cost savings post-migration.
  • AWS Services & Technologies :
    AWS Service Role
    Amazon EMR (Hadoop + Hive) Baseline batch-oriented data warehouse
    Amazon MSK (Kafka) Real-time streaming ingestion backbone
    Amazon EMR (Spark Streaming) Real-time stream processing engine
    Amazon Kinesis Data Analytics (Flink) Alternative for stream analytics
    Amazon S3 Centralized data lake (raw + transformed)
    Amazon Redshift Data warehouse for analytical queries
    Amazon Athena Interactive queries across S3 datasets
    AWS Lambda Event-driven orchestration & stream triggers
    Amazon CloudWatch Monitoring performance, cost, and system health
    Amazon QuickSight Visualization of migration impact & performance metrics