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Optimization15GB+ ImpactClient: MetaGeo

MetaGeo

Redesigned architecture into an event-driven model where Lambda extracts lightweight metadata, computes requirements, and queues work via SQS to ECS worker tasks.

Primary MetricLambda execution reduced from minutes to sub-second responses
Reliability SLAFlawless processing for files exceeding 15GB
Architecture StandardMulti-AZ & IaC

01The Architecture Challenge

Serverless Lambda functions were failing with timeout and memory errors when processing massive 15GB+ geospatial datasets.

Key Technical Pain Points Addressed:

  • High operational risk of service disruption during production cutover.
  • Over-provisioned compute resources driving unnecessary cloud expenditure.
  • Lack of declarative configuration management and GitOps workflows.

02The Implemented Engineering Solution

Redesigned architecture into an event-driven model where Lambda extracts lightweight metadata, computes requirements, and queues work via SQS to ECS worker tasks.

Lambda execution reduced from minutes to sub-second responses
Flawless processing for files exceeding 15GB
Elastic task autoscaling based on queue depth
Zero dropped or timed-out processing requests

03Applied Technologies & Toolchains

Amazon S3AWS LambdaAmazon SQSAmazon ECSFargateEvent-Driven
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Client Organization:MetaGeo
Domain / Industry:Optimization
Delivery Timeline:Production Verified

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