ETL and data pipelines that run on schedule and fail loudly.
We build batch and event-driven pipelines with tested transformations, clear lineage and alerting, so the data downstream is complete, current and explainable.
- Pipeline inventory and lineage
- Extraction connectors
- Tested transformations
- Orchestration and scheduling
- Monitoring and alerting
Where this helps
What we deliver
How it works
- 01
Map what exists
We trace current jobs, dependencies and consumers, and identify the pipelines that break most often or matter most.
- 02
Design the pattern
We agree on the load pattern — full, incremental or change data capture — along with orchestration, testing standards and naming.
- 03
Build or migrate
Pipelines are rebuilt or migrated one at a time, with outputs compared against the old version before switching.
- 04
Add observability
Alerts, lineage and run dashboards are set up as each pipeline goes live.
- 05
Hand over
Your team receives runbooks and on-call guidance for each pipeline.
Design decisions we make with you
ETL or ELT
Loading raw data first and transforming in the warehouse suits most modern stacks. Transforming before load still makes sense for sensitive fields or constrained targets.
Batch, micro-batch or streaming
Frequency follows the business need. Hourly or daily batches are simpler to run and debug; event streaming is used where latency genuinely matters.
Build or buy connectors
Managed connectors save time for common SaaS sources. Custom extraction is used for internal systems, unusual APIs or where data must not pass through a third party.
Failure behavior
For each pipeline we decide whether a failure should stop downstream jobs, load partial data with a flag or retry, and who is alerted.
Applications
Related capabilities
- Fraud & Risk SignalsScore transactions, claims or applications for risk and send the suspicious ones to investigators with the reasons attached.
- Predictive MaintenanceUse equipment signals and maintenance history to rank which assets need attention, so maintenance teams can plan work before failures.
- Data EngineeringData architecture, pipelines, quality and access that make your data usable for reporting, operations and AI.
- Data Analytics & BIAgreed metric definitions, dashboards and analysis designed around the decisions your teams actually make.
- Web Scraping & Data ExtractionStructured collection from permitted web sources, with validation, change detection and a reliable refresh cadence.
- Cloud & DevOpsCloud architecture, infrastructure as code, deployment pipelines, reliability practices and cost visibility.
Questions buyers ask
Pipelines are one part of the broader platform. Data engineering covers architecture, data models, governance and access; this service focuses on moving and transforming data reliably.
Yes. We migrate one job at a time, run old and new in parallel and compare outputs before switching, so downstream reports do not change unexpectedly.
Often not. We start from how quickly decisions actually depend on the data. Streaming adds operational cost, so we recommend it only where the latency is needed.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.