Software · Data and AI
Data pipelines
Pipelines that collect, clean and deliver data from every source to where it is analysed, and alert you when they fail.
Reports are only as good as the data feeding them. We build pipelines that extract from your apps, databases, APIs and spreadsheets, load into a warehouse or lake, and transform it into tested, documented tables. When a source changes or a load fails, the right person is told before the morning dashboards go out wrong.
What we build
- Batch ELT from ERP, CRM, payment and marketing tools into your warehouse
- Change data capture streams from PostgreSQL, MySQL and SQL Server databases
- dbt models that turn raw tables into clean, documented business tables
- Data quality tests for freshness, duplicates, nulls and broken relationships
- Orchestration with retries, backfills and clear run history for every job
- Lineage showing which dashboards are affected when a source table changes
DemoInteractive · sample data
Try it live.
A working sample with made-up data. It runs in your browser; nothing is sent anywhere.
- 02:00 schedule, or new files in the orders bucket
- Extract
- Capture changes
- Load raw
- Transform
- Test
- Publish
What it does well
Tested transforms
Every model has tests for uniqueness, nulls and accepted values that run on each load.
Freshness alerts
If a source stops arriving on time, its owner is alerted on Slack, Teams or email.
Safe backfills
Historic data can be reloaded for any date range without creating duplicate rows.
Schema change handling
New or renamed source columns are detected and reported before they break models.
Lineage and docs
Each table lists its sources, its owner and the dashboards that depend on it.
Cost-aware loads
Incremental loads and partitioning keep warehouse compute bills in check.
Questions
Should we buy a connector tool or build pipelines ourselves?
Use managed connectors for common sources like Salesforce, Shopify or Google Ads; they save time and keep up with API changes. Custom code makes sense for in-house systems, unusual formats or very large volumes. Most setups end up as a mix of both.
Do we need real-time data?
Usually not for reporting; hourly or daily loads are cheaper and simpler. Real-time streams pay off for fraud checks, stock levels, live tracking and customer-facing features. We look at each use and pick the slowest schedule that still serves the decision.
What happens when a pipeline fails?
It retries automatically, then alerts the owner with the error and the affected tables. Downstream models are held back so dashboards do not show half-loaded data, and once the cause is fixed the missed period is backfilled.