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Benchmarking Big Data Processing on Azure: A Performance and Cost Analysis of HDInsight (Spark) vs. Databricks for ETL Workloads

HDInsight

A Performance and Cost Analysis of HDInsight (Spark) vs. Databricks for ETL Workloads

  • Use Case: Organizations often run ETL (Extract, Transform, Load) workloads on large datasets for analytics and decision-making. On Azure, both HDInsight (Spark-based cluster) and Azure Databricks (optimized Spark environment) are popular choices. Companies need to evaluate performance, scalability, and cost-efficiency of these services before committing to a long-term solution for big data processing.

Objective

  • To benchmark Azure HDInsight (Spark) against Azure Databricks for ETL workloads.

    To analyze performance trade-offs, execution speed, resource utilization, and cost efficiency.

    To provide recommendations for optimal workload placement based on use case (batch ETL, streaming, ML preprocessing).

Project Description

  • The project sets up a controlled benchmarking environment on Azure to compare HDInsight and Databricks for running large-scale ETL pipelines.

    Data Source: Structured + semi-structured data (CSV, JSON, Parquet) ingested into Azure Data Lake Storage (ADLS Gen2).

    Workloads: Common ETL tasks such as data ingestion, transformation (joins, aggregations, filtering), and load into Azure Synapse Analytics / Power BI.

    Benchmarking Metrics: Query execution time, cluster provisioning time, cost per workload, scalability, and ease of integration with Azure services.

    Outcome: A comparative performance and cost analysis report highlighting the strengths and weaknesses of each platform for big data ETL workloads.
  • Azure Services and Technologies :
    Azure Service Purpose in Project
    Azure Data Lake Storage Gen2 (ADLS Gen2) Stores raw and transformed datasets (input + output of ETL).
    Azure HDInsight (Spark Cluster) Provides managed Spark environment for ETL workloads.
    Azure Databricks Optimized Spark platform with ML/AI integration, tested against HDInsight.
    Azure Synapse Analytics Data warehouse for storing transformed data and running analytical queries.
    Azure Monitor + Log Analytics Collects performance metrics, logs, and cost analysis.
    Azure Cost Management + Pricing Calculator Provides cost benchmarking between HDInsight and Databricks.
    Azure Key Vault (IAM + Security) Manages access, credentials, and secure keys for services.
    Power BI / Synapse Studio Data visualization and reporting for benchmarking results.