# data · data.sql
Data Pipelines
ETL pipelines built to ingest, clean and load a dataset the way your brief specifies.
A data pipeline assignment is rarely marked on the happy-path run — it's marked on what happens when the dataset has a missing column or a malformed row, which real datasets always do. We build in validation and logging at every stage, so a failure tells you where it happened instead of just failing.
What you get
- Pipeline stages documented — ingest, transform, validate, load
- Handles the messy real-world data your brief actually gives you
- Logging at each stage so a failure is traceable, not silent
- Scheduled or batch, matched to what your assignment asks for
- Output format matched exactly to your grading script's expectations
- A short architecture diagram included for your report
What to send first
- The dataset (or a sample of it) and where it came from
- The questions the analysis has to answer, or the model's target
- Required tools — SQL dialect, Python libraries, R, Excel version
- The report format: notebook, PDF write-up, dashboard or slides
How it runs
- 01
Send the spec
The brief, the rubric, any starter code and the versions your marker uses. A photo of the PDF is fine to begin with.
- 02
Scope and quote in writing
We read it properly, ask what is unclear, and confirm a fixed price and delivery date. A small advance confirms the order.
- 03
Build against the brief
Work is tracked task by task against the spec. Longer projects are split into milestones you can review as they land.
- 04
Tested handover
A repo that installs from a README, run against your test cases, with the write-up if your rubric asks for one.
- 05
Fixes and walkthrough
One round of fixes if a marker flags something, and a walkthrough of how it works so you can explain it.
Data Pipelines: common questions
The notebook or scripts that produced every number, so the analysis can be re-run end to end. Results pasted into a report without the code behind them are hard to defend.
No. Data is used only for your task, never shared or reused, and deleted on request once the work is signed off.
It depends on the scope of the brief, the stack and the deadline. Send the spec on WhatsApp and you get a fixed price and delivery date in writing before any work starts — no hourly meter.
Turnaround depends on scope and is agreed with the quote. Tell us the real deadline, including the timezone, and we will say honestly whether it is workable before you commit.
// related
Often paired with data pipelines
./send-brief
Send your dataset.
Paste the data pipelines brief into WhatsApp — the message already names the service. Scope and price come back in writing.