Elastic Batch Processing for Faster Genomic Pipelines and Lower Cloud Cost
A US-based Food & Biosciences organization needed to process large volumes of genome sequencing data for machine learning pipelines without rising cloud costs and delivery delays. CES implemented an AWS Batch–driven, serverless batch processing model with automated scheduling, elastic provisioning, and monitoring – improving throughput while reducing idle infrastructure spend.
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The Challenge
High Cloud Operating Costs
Manual, Non-Elastic Infrastructure
the client
Food & Biosciences / AgriTech / Biotechnology
Technology Stack
- AWS Batch
- Amazon S3
- Amazon CloudWatch
Solution Area
- Cloud Data Processing Optimization & ML Pipeline Enablement
the impact
3×
Faster Data Processing
$30K
Monthly Cloud Savings
Automated Pipeline Scheduling
Elastic, On-Demand Compute
The shift was batch-processing–led. The result?
Faster insights at lower cloud cost.
The Need
The organization required a scalable and cost-efficient approach to process genome sequencing data across machine learning pipelines. Existing always-on environments led to high cloud costs, slow processing times, and operational delays caused by manual provisioning. Leadership needed an automated, elastic compute model that could scale with data volume, support concurrent workloads, and eliminate idle infrastructure without compromising throughput.
Challenges
- High Cloud Operating Costs – Constantly running pipelines and idle environments resulted in inflated infrastructure spending.
- Slow ML Pipeline Performance – Diverse and compute-intensive genetic datasets reduced training and inference speed.
- Manual and Non-Elastic Environments – Developers had to manually provision and decommission resources, limiting scalability and slowing delivery.
CES implemented a cloud-native batch processing architecture using AWS Batch to automate genomic workloads, scale compute on demand, and reduce idle infrastructure cost.
AWS Batch Architecture & Serverless Execution Model
- Selected AWS Batch for dynamic compute allocation and automated execution.
- Designed a serverless batch model to eliminate always-on environments.
Compute Environments, Job Design, and Resource Mapping
- Configured job queues, compute environments, and job definitions in AWS Batch.
- Mapped CPU-bound vs memory-intensive workloads to right-size resources and scaling.
Automated Scheduling and Job Submission
- Built automation scripts for scheduling and job submission.
- Enabled time-based and event-based triggers for genomic pipeline execution.
Data I/O, Monitoring, and Reliability Controls
- Integrated Amazon S3 for pipeline input/output orchestration.
- Implemented CloudWatch logging/monitoring with fault tolerance via retries for long-running jobs.
Performance Validation and Production Rollout
- Performed testing to validate throughput gains and cost reduction.
- Deployed to production with ongoing monitoring by the MLOps team for SLA adherence and stability.
- 3× Faster Processing – Optimized batch execution significantly reduced genomic data processing time.
- $30K Monthly Cloud Savings – Elastic scaling eliminated idle environments and overprovisioned resources.
- Automated Pipeline Scheduling – Batch jobs ran reliably without manual provisioning or teardown.
- Elastic Scalability – Compute capacity scaled dynamically to match data volume and concurrency needs.
- Improved Developer Efficiency – Teams focused on data and models instead of infrastructure management.
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Take the Next Step
Running data-intensive pipelines with rising cloud costs and manual overhead? Let’s design a batch-processing and automation strategy that improves performance while keeping infrastructure spend in check. Talk to our Strategic Client Partner.
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