List of Topics:
Location Research Breakthrough Possible @S-Logix pro@slogix.in

Office Address

Social List

Cost-Performance Optimization of Batch Analytics: Comparing Hadoop MapReduce on EMR vs. AWS Glue for Large-Scale ETL Pipelines

Hadoop MapReduce

Comparing Hadoop MapReduce on EMR vs. AWS Glue for Large-Scale ETL Pipelines

  • Use Case:

    Organizations dealing with large-scale data pipelines (log processing, clickstream analytics, IoT data, financial transactions, healthcare data, etc.) often struggle with balancing cost efficiency and performance.

    Two common AWS solutions for ETL batch analytics are:

    Hadoop MapReduce on Amazon EMR → More customizable, but requires cluster management.

    AWS Glue (serverless ETL) → Fully managed and serverless, but less customizable and potentially higher cost for long-running heavy workloads.

    This project evaluates which service is more cost-effective and performant depending on workload size, data complexity, and frequency.

Objective

  • To compare performance and execution time of Hadoop MapReduce (on EMR) and AWS Glue for batch ETL workloads.

    To analyze cost trade-offs between managed cluster-based (EMR) and serverless (Glue) architectures.

    To optimize resource allocation for large-scale ETL pipelines while minimizing operational overhead.

    To provide recommendations for workload placement (e.g., when to choose EMR vs Glue).

Project Description

  • Data Ingestion:

    Collect large-scale structured (CSV, Parquet) and semi-structured (JSON, logs) datasets into Amazon S3.

    Example datasets: 1 TB web server logs, financial transactions, IoT sensor streams.

    ETL Pipeline Design

EMR Workflow

  • Deploy EMR cluster with Hadoop and MapReduce.

    Implement ETL jobs (data cleaning, joins, aggregations).

    Configure autoscaling & spot instances for cost reduction.

Glue Workflow

  • Use AWS Glue Data Catalog for schema discovery.

    Build ETL transformations using PySpark scripts.

    Execute in Glue serverless environment.

Performance Benchmarking

  • Compare job execution times for different dataset sizes (100GB → 1TB).

    Measure resource utilization (CPU, memory, I/O).

    Analyze failure/recovery performance.

Cost Analysis

  • Compare pay-per-second cost (Glue) vs cluster management cost (EMR).

    Evaluate impact of on-demand vs spot pricing for EMR.

    Identify cost-performance sweet spot.

    Visualization & Insights:

    Store results in Amazon Redshift or Athena for analysis.

    Use Amazon QuickSight for dashboards comparing EMR vs Glue performance and costs.
  • AWS Services & Technologies :
    AWS Service / Technology Role
    Amazon S3 Acts as a data lake for storing raw input datasets and processed ETL outputs.
    Amazon EMR (Hadoop MapReduce) Executes cluster-based ETL pipelines for batch processing and benchmarking.
    AWS Glue (ETL + Data Catalog + PySpark) Provides serverless ETL orchestration and metadata management for dataset schema.
    AWS Lambda Handles workflow orchestration by triggering ETL jobs in EMR or Glue.
    Amazon CloudWatch Monitors performance metrics, job execution logs, and resource utilization.
    Amazon Athena Performs SQL queries on benchmark logs stored in S3 for performance evaluation.
    Amazon QuickSight Builds dashboards & visualizations for cost vs. performance analysis.