ETL & data integration
Managed ETL with AWS Glue — orchestrated, monitored, and documented, so a failed load is a page rather than a discovery three weeks later.
Operational standard
Alerting
Every pipeline monitored, every failure paged.
- Basis
- Fixed price
- Tooling
- AWS Glue
- Includes
- Catalogue + lineage
What we deliver
Integration that is operable.
The hard part is not writing the transformation. It is knowing within minutes when it stopped working, and being able to rerun it safely.
Source connectors
Databases, SaaS APIs, and file drops brought in on a schedule you control.
Transformation
Versioned, tested transformation jobs rather than notebooks in someone's account.
Data catalogue
Glue catalogue and lineage so downstream users know what they are querying.
Orchestration
Dependency-aware scheduling with retries and idempotent reruns.
Quality checks
Assertions on volume, freshness, and nulls that fail the load, not the report.
How it runs
Wire one source properly.
First pipeline sets the standard — orchestration, quality checks, alerting — and the rest are built against it.
Map sources
Systems, volumes, refresh needs, and ownership documented.
Build reference pipeline
One source end to end with full quality and alerting in place.
Scale out
Remaining sources built to the same template and catalogued.
Hand over
Runbooks, on-call guidance, and rerun procedures with your team.
Technology
What we build it on.
- Glue
- Glue Catalogue
- Step Functions
- S3
- Athena
- EventBridge
Cost & AWS pricing
Fixed-price engagement. Glue bills per DPU-hour; job sizing and partitioning are part of the design, since most ETL overspend comes from unpartitioned scans.
See funded AWS programsNext step
Make the pipelines operable.
Discovery comes first — a short, bounded review that ends in an architecture, a scope, and a price. You keep the output either way.