We are migrating our local databases to AWS and need to build a highly scalable architecture. Which cloud-native tools do you recommend to orchestrate a high-volume ETL pipeline efficiently? We are looking closely at AWS Glue and EMR but are worried about managing costs as our data grows exponentially over the next year.
3 answers
AWS Glue is fantastic for serverless ease, but if you have predictable, massive workloads, running Spark on managed EMR clusters with spot instances is often much more budget-friendly. To keep your ETL pipeline optimized, use AWS Step Functions or Apache Airflow for orchestration rather than letting Glue manage everything. Also, ensure your data is partitioned properly in Amazon S3 by date or region; this single step drastically reduces data scanning costs and speeds up your transformation phases significantly.
Have you evaluated whether utilizing serverless components like AWS Lambda could handle the extraction phase cheaper than spinning up full Glue jobs?
For our migrations, AWS Glue paired with S3 partitioning cut our processing times in half while keeping our core ETL pipeline incredibly easy to maintain
S3 partitioning is definitely the secret sauce here, Kimberly. Without it, even the fastest cloud tools will waste money scanning useless data.
Lambda is perfect for small, frequent payloads, Douglas. However, it has a strict 15-minute execution limit. For a heavy, enterprise-grade ETL pipeline, those time limits will cause frequent failures when handling massive historical bulk loads.