Large-Scale Document ETL Pipelines

The problem

The document intelligence sitting above these pipelines — summarization, risk extraction — is only ever as good as what reaches it. Volume at this scale arrives unevenly: quiet for hours, then a burst that would swamp anything sized for the average. Provision for the peak and you pay for idle capacity most of the time; provision for the mean and you drop documents exactly when there are most of them.

The approach

Serverless ingestion and transformation on AWS, structured as explicit workflow stages rather than as a chain of triggers:

• Load — documents landed in S3 as the durable entry point, decoupling arrival from processing so a burst queues instead of failing.
• Parse — heterogeneous formats normalised into something the downstream stages can rely on without special-casing every source.
• Transform — processing split across Lambda for short bursty work and ECS for jobs that need to run longer than a function reasonably should.
• Deliver — results written back to S3 for the summarization and risk systems that consume them.
• Step Functions orchestration, so the pipeline is a state machine you can inspect and resume rather than something you reconstruct from logs after it breaks.
• DynamoDB for per-document state, so a failure retries one document instead of reprocessing the batch it happened to arrive in.
• AWS SAM for reproducible deploys, keeping the infrastructure a definition rather than a sequence of console actions.

The outcome

Throughput scales with demand rather than with provisioned capacity — which is where both the reliability and the drop in operational overhead come from. It is also the layer the document summarizer and RiskFocus sit on: neither of them has to think about ingestion, because this does it for them.