Data Pipeline Modernisation Crew
ETL CREW FOR RETAIL
Silent drift doesn't ship.
A multi-market retailer's legacy Hive jobs — orders, inventory, customer activity, promotions — rewritten in AWS Glue PySpark. A window function that shifts during conversion still returns a plausible number, and plausible-but-wrong is harder to catch than an outright failure, so every pipeline gets checked against the source before it ships.
- 0/mo
- Pipeline throughput
- $0K–169K
- Capacity value released per month
Capabilities
Four steps, every pipeline.
Maps the dependency graph
Maps source tables, dependent jobs, acceptance criteria and the required Glue configuration.
Rewrites the logic
Converts HQL joins, window functions and partition transformations into maintainable PySpark.
Checks it against the source
Runs Spark unit tests and compares counts, aggregates, partitions and rejected-record behaviour.
Ships with the evidence
Bundles the code, tests and any exceptions into a pull request and a Jira update.
Where it is deployed
INDUSTRIES
Governance
Every pipeline runs inside a Role Card scoped to the conversion backlog, and nothing ships without a source comparison attached.
- Autonomy tier
- Assist — converts and validates inside the approved ticket scope; a human reviews every pull request before it merges.
- Human Principal
- A named person owns performance and approves scope. Every AI Coworker reports to a human.
- Off limits
- No pipeline reaches production without a passing comparison against the source — counts, aggregates, partitioning and rejected records.
- Audit
- Every classification, response and escalation logged. SOC 2 Type II compliant.
Built on 
Correctness is evidenced per pipeline, not sampled at the end of a migration wave — window logic, partitioning and rejected-record behaviour checked against the source before anything merges.
In production
Multi-market retailer, APAC
Legacy Hive jobs for orders, inventory, customer activity and promotions, rewritten in AWS Glue PySpark with partitioning and window logic checked pipeline by pipeline.
0
Pipelines per month
$0K–169K
Capacity value released per month
$0.00
AWS platform cost per month
Deployment
OBZ operates the crew end to end — you get converted PySpark and a source comparison for every pipeline, with nothing licensed for your team to run.
Scoped to your Hive-to-Glue backlog.
Talk to us about deploying Etl Crew For RetailWhat we need from you
- Hive/HQL job inventory across orders, inventory, customer and promotions
- Jira for work management, GitHub for source control
- Named data-engineering lead
- Sign-off on window-logic and partitioning parity
