Loading…
Topic
1 post about AWS Glue. Every summary links to the original.
See the idea
AWS Glue runs serverless Spark jobs that transform data in batches; engine-native pipelines transform during ingestion. This comparison shows how to pick between them by freshness, cost, and data fidelity.
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
Newest first
The post describes design tradeoffs and workarounds encountered while building the open-source Bulk Executor tool for Amazon DynamoDB on AWS Glue. Core requirements included real-time local-script feel, scaling to hundreds of parallel workers without user infrastructure management, and easy command extensibility. AWS Glue was chosen for serverless parallelism and a built-in DynamoDB connector, but the connector simplified data types, losing distinctions like string set versus list of strings. Challenges included estimating costs for provisioned tables, handling job termination with ^C, and streaming output via CloudWatch Live Tail which samples events and delivers out-of-order. Solutions included a two-phase cost estimate for deletes, programmatic interrupt handling, writing large output to S3 with a preview, mandating point-in-time recovery for mutating commands, and using AI for testing and linting; the team also migrated from AWS Glue 4 to 5 and 6 for faster startup and 30 percent lower DPU costs.
The best engineering reads, once a week.
