Parallel Processing
Parallel Processing in the Data Pipeline
Section titled “Parallel Processing in the Data Pipeline”Big Data Parallel Processing Workflow
Section titled “Big Data Parallel Processing Workflow”Three-Step Process
Section titled “Three-Step Process”- Split: Large dataset divided into smaller parts
- Process: Parts processed in parallel simultaneously
- Aggregate: Results combined into final output
Benefits
Section titled “Benefits”- Time Reduction: Solve large problems in shorter periods
- Concurrency: Run problem components simultaneously
- Scalability: Use cluster of hundreds/thousands of servers
Example: Digital Library Word Count
Section titled “Example: Digital Library Word Count”- Task: Count words in 1 million books
- Approach: Process 10 batches of 100,000 books each
- Result: Aggregate 10 batch results into total word occurrence count
- Time Savings: 1,000 log files taking 16+ hours sequentially complete in 1 minute with parallel processing
Amazon EMR
Section titled “Amazon EMR”Core Capabilities
Section titled “Core Capabilities”- Management: Handles cluster infrastructure management automatically
- Applications: Includes Apache Hadoop applications (MapReduce, Apache Spark)
- Deployment Options: Amazon EC2 instances, Amazon EKS, AWS Outposts
- Serverless: EMR Serverless supports serverless cluster operations
Infrastructure Management
Section titled “Infrastructure Management”- Provisioning: Automatic infrastructure provisioning and cluster setup
- Configuration: Handles configuration and tuning
- Deployment Flexibility:
- Single AZ deployment on EC2 instances in VPC subnet
- Multi-AZ deployments using existing Amazon EKS clusters
- On-premises deployment on AWS Outposts
Choosing Parallel Processing Solutions
Section titled “Choosing Parallel Processing Solutions”Solution Comparison
Section titled “Solution Comparison”| Requirement | Amazon EMR | EMR Serverless | AWS Glue |
|---|---|---|---|
| Manage and Control Clusters | Yes | No | No |
| Lift and Shift Legacy Hadoop/Spark | Yes | No | No |
| Develop New Cloud Applications | No | Yes | Yes |
| Pay-Per-Job Price Model | No | Yes | Yes |
| Run Only Apache Spark Jobs | No | No | Yes |
Selection Guidelines
Section titled “Selection Guidelines”Choose Amazon EMR when:
- Need full control of clusters
- Moving legacy Apache Hadoop applications to AWS without code changes
Choose EMR Serverless when:
- Developing AWS native cloud applications for batch data processing
- Prefer pay-per-job pricing model
- Team has Hadoop MapReduce or Apache Spark experience
Choose AWS Glue when:
- Team is new to data analytics
- Prefer to run only Apache Spark jobs
- Want simplest serverless option
Data Curation with Amazon EMR
Section titled “Data Curation with Amazon EMR”Three-Zone Data Lake Architecture
Section titled “Three-Zone Data Lake Architecture”Best practice: Split data lake into zones based on data quality
Workflow Example
Section titled “Workflow Example”-
Data Drop Zone:
- Multiple on-premises data sources transfer to designated Amazon S3 zone
- Lake Formation provides security access to data lake
-
Data Analytics Zone:
- EMR data cleaning job copies from data drop zone
- Runs processing steps to clean data
- Copies result set to analytics zone for consumption
-
Curated Data Zone:
- EMR data curation job copies from analytics zone
- Runs processing steps to curate data
- Copies result set to curated zone for visualization and analysis
Progressive Data Quality
Section titled “Progressive Data Quality”This approach provides progressively higher data quality:
- Raw Data → Cleaned Data → Curated Data
- Each zone serves different user needs and use cases
- Analytics applications consume data appropriate to their requirements
Key Implementation Considerations
Section titled “Key Implementation Considerations”Performance Benefits
Section titled “Performance Benefits”- Parallel Processing: Break large datasets into manageable parts
- Distributed Computing: Leverage cluster resources effectively
- Framework Options: Choose between Hadoop MapReduce and Apache Spark based on use case
Management Trade-offs
Section titled “Management Trade-offs”- Amazon EMR: Maximum control, requires cluster management
- EMR Serverless: Balanced approach with automatic scaling
- AWS Glue: Simplest option, fully managed service
Cost Optimization
Section titled “Cost Optimization”- Pay-per-Job: EMR Serverless and AWS Glue offer usage-based pricing
- Cluster Management: Amazon EMR requires ongoing cluster costs
- Resource Efficiency: Parallel processing optimizes compute resource utilization
Big data parallel processing breaks large datasets into smaller parts for simultaneous processing, dramatically reducing processing time. Amazon EMR provides cluster management capabilities, while EMR Serverless and AWS Glue offer serverless alternatives for different use cases and expertise levels.