Leveraging AWS for Scalable and High-Performance Data Processing

Suppose you have a scenario in your organization where your company is using a combination of open source tools to manage data analysis workflows and Docker containers running on servers in its on-premises data center to process large amount of data.

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Data is generated and stored on a local storage area network (SAN), and then the data is processed. The development teams are facing bottlenecks and capacity issues in their workloads. How would you use AWS to re-architect this platform.

To re-architect the analysis platform on AWS and address the scaling and performance requirements, the following solution can be implemented:

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1. Use Amazon S3 for Storage

• Reason: Amazon S3 is scalable, durable, and cost-effective for storing large amounts of data.

• Implementation:

  • Use AWS DataSync to transfer data from the on-premises SAN to Amazon S3.

  • Organize S3 buckets and prefix structures to store input data, intermediate outputs, and final results.

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2. Use Amazon FSx for Lustre for High-Performance File Storage

• Reason: Amazon FSx for Lustre provides high-performance, low-latency file systems optimized for workloads .

• Implementation:

  • Use FSx for Lustre to link S3 data and provide a file system interface for processing jobs.

  • Automatically export processed data back to S3.

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3. Use AWS Batch for Job Orchestration

• Reason: AWS Batch is designed for batch processing workloads and can manage the job queue, resource provisioning, and execution.

• Implementation:

  • Create an AWS Batch Compute Environment with a mix of On-Demand and Spot Instances to optimize cost and scale capacity.

  • Define a job queue and job definitions to process workloads.

  • Submit job requests to AWS Batch with parameters like input file locations and processing configurations.

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4. Use Amazon EC2 Instances or Spot Instances for Compute

• Reason: Workloads are compute-intensive and benefit from high-performance EC2 instances (e.g., C5, M5, or R5 instance families).

• Implementation:

  • Configure AWS Batch to use EC2 Spot Instances for cost-efficiency.

  • Use instance types with high CPU and memory ratios to optimize processing.

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5. Use Docker Containers for Portability

• Reason: Docker containers are already used for analysis, ensuring compatibility and ease of deployment.

• Implementation:

  • Build Docker images with the required analysis tools and upload them to Amazon Elastic Container Registry (ECR).

  • Use these Docker images in AWS Batch job definitions.

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6. Monitor and Optimize Performance

• Reason: Monitoring ensures efficient use of resources and allows for optimization.

• Implementation:

  • Use Amazon CloudWatch to monitor job performance, compute usage, and system health.

  • Implement Auto Scaling for AWS Batch to dynamically adjust capacity based on demand.

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Workflow Summary

1. Data Ingestion:

   • Sequencers generate data locally.

   • Use AWS DataSync to upload data from the SAN to S3.

2. Job Execution:

   • AWS Batch retrieves data from S3.

   • Jobs are processed using Docker containers on EC2 or Spot Instances.

   • FSx for Lustre provides high-performance storage for intermediate data.

3. Result Storage:

   • Processed results are exported to S3 for storage and further analysis.

4. Scaling:

   • AWS Batch dynamically scales resources based on the number of jobs.

5. Cost Optimization:

   • Use Spot Instances for cost-efficient compute capacity.

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Benefits of This Solution

• Scalability: Automatically scales resources to handle daily workloads.

• Performance: High-performance compute and storage reduce processing time.

• Cost-Efficiency: Spot Instances and S3 reduce costs while maintaining performance.

• Ease of Management: AWS Batch handles job orchestration and scaling.

This solution provides a flexible, scalable, and cost-effective architecture to meet the company's requirements and reduce turnaround time.