CR

3_Parallel Processing in the Data Pipeline

Parallel Processing for Big Data

  • Parallel processing techniques address limitations of single servers in handling large datasets.
  • Basic Steps:
    • Split data into parts.
    • Process parts in parallel on server clusters.
    • Aggregate results into a final output.
  • Significance: Reduces the processing time for big data jobs.
  • Examples of Open Source Frameworks: Apache Hadoop and Spark.

Amazon EMR

  • Amazon EMR is a managed cluster platform.
  • Simplifies running big data frameworks (e.g., Apache, Hadoop, Spark).

Data Pipeline Example Using Amazon EMR

  • Scenario: Cleaning and preparing data in an analytics pipeline.
  • Data Lake Organization: Utilizes zones for data of different states.
  • Data Transfer: Data moves from on-premises sources to an Amazon S3 data drop zone.
  • Data Cleaning Job:
    • Copies data from the drop zone.
    • Runs data cleaning processes.
    • Copies the result set to the data analytics zone.
  • Data Curation Job:
    • Copies data from the data analytics zone.
    • Performs data curation steps.
    • Copies results to the curated data zone.
  • Curated Data Usage: Accessed for visualization and analysis; often loaded into a data warehouse.

Key Takeaways

  • Parallel processing uses server clusters to accelerate big data jobs via data segmentation.
  • Amazon EMR is a cloud-based service for executing big data frameworks with reduced complexity.