Scheduled Jobs That Move and Clean Your Data on Their Own
ETL pipelines are the scheduled jobs that extract data from your source systems, apply cleaning and business rules, and load it into your reporting database — built and monitored so a refresh happens without anyone running it by hand.
Extract, Transform, Load, in Practice
Extract, transform, load — a pipeline pulls data out of a source system, applies the cleaning and calculation rules your business has agreed, and writes the result into a reporting database. Once built, it runs on a schedule without a person repeating the same manual export and paste every period.
What a Pipeline Does, Step by Step
Extract
Pulling records from a source — an API, a direct database read, or a scheduled file export — only the data that has changed since the last run, where the source allows it.
Transform
Applying standardisation, matching and calculation rules — correcting date formats, matching duplicate parties, computing derived figures — consistently on every run.
Load
Writing the cleaned result into the reporting database in a structure the dashboards and reports query from, replacing or appending records as the design requires.
What Happens When a Run Fails
A source system can be briefly unreachable, a file can arrive malformed, or a new record can break an assumption the pipeline made. When that happens, the run is designed to stop and alert rather than load partial or incorrect data silently — the failure reaches whoever administers the reporting the same day, with enough detail to fix and rerun it.
Incremental Versus Full Loads
- An incremental load pulls only records changed since the last run, keeping refreshes fast as your data grows
- A full load re-reads everything and is used for the first run, after a rule change, or when reconciling to catch drift
- Most pipelines run incrementally day to day, with a full load scheduled periodically as a check
Frequently asked questions
How often can a pipeline run?
As often as the source system and your reporting needs justify — nightly is common for most business reporting, with hourly or more frequent runs used only where the underlying dashboard genuinely needs it.
Does pulling data on a schedule slow down our source system?
Extraction is read-only and, where the source allows it, scheduled outside peak working hours specifically to avoid competing with the transactions your staff are entering.
What happens if a source system's structure changes — a new field, a renamed column?
A pipeline is written against the current structure, so a source-side change can require an update to keep loading correctly. Monitoring is set up to catch this as a failure rather than let it load silently wrong data.
Can a pipeline apply business rules, or does it just copy data across?
It applies whatever rules your business has agreed — matching duplicate customers, converting units, computing a derived figure — not a plain copy. Those rules are documented so they can be reviewed and changed as your business does.
How do we know a pipeline actually ran successfully last night?
Run history, record counts and failure alerts are part of the build, so a missed or partial run is visible rather than discovered only when a dashboard looks wrong.
Who maintains the pipelines after they are built?
You own the code and can maintain it with your own team, or we continue to monitor and adjust it under a support arrangement — either way, nothing about the pipeline is locked to us.
Tell us what you need.
Send a short brief and one of our engineers will come back to you — usually the same day.
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- We reply the same working day
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