Raw data has potential. Transformed data has power.
Scraped data rarely arrives in the exact shape your dashboards, models, or operational systems need. Our managed transformation pipelines reshape, join, aggregate, and enrich raw feeds — applying complex business logic automatically — so every record is tailored to your specific downstream requirements before it ever reaches your warehouse.
inputs: ["product_feed", "pricing_feed"]
transforms:
- join: "product_feed.sku = pricing_feed.sku"
- aggregate: "avg(price) by category, week"
- derive: "price_index = price / avg_price * 100"
output: "snowflake.public.transformed_data"
3 feeds → 1 unified view
12 derived fields added
Trusted data from leading platforms
Your data, shaped precisely for the job it needs to do
Data transformation is the critical middle step that turns a collection of raw scraped feeds into a single, coherent, and purpose‑built dataset. It's more than cleaning or normalising — it's about restructuring: joining product data with pricing data, aggregating daily snapshots into weekly trends, creating derived KPIs, filtering irrelevant records, and reshaping columns to match the exact schema your BI tool expects.
Our managed transformation pipelines are custom‑built to your business rules. Once configured, they run automatically on every new batch of data, delivering output that is ready for dashboards, models, or operational systems — without a single analyst spending time in Excel or writing SQL.
- Join, union, and merge multiple data feeds into a single view
- Aggregate, pivot, and summarise data for dashboards
- Create derived fields, KPIs, and calculated metrics
- Filter, sort, and reshape rows and columns for any target tool
- All logic applied in‑flight, automatically, on every data load
Why raw data rarely fits the tool you're feeding it into
The gap between extracted data and analysis‑ready data is where most teams burn the most hours.
Data is siloed across separate feeds
Product details come from one scraper, prices from another. Without joining them, you can't answer "what's the average price per category?" — the most basic competitive question.
Your BI tool expects a specific shape
Tableau needs unpivoted data; your ML model expects normalised floats; your API returns nested JSON. Transforming raw scraped data into each of those shapes is a separate engineering project — unless it's automated.
Business logic changes faster than scripts
A new product category, a revised KPI formula, a new competitor to track — every change requires a script update. Without a managed pipeline, your data engineering backlog grows faster than your team can clear it.
A transformation engine that reshapes data to your exact spec
Every feature below is configured once, applied continuously, and adapted by our team when your needs change.
Multi‑feed joining & merging
Combine product data, pricing, inventory, and reviews into a single unified dataset. Configurable join logic (inner, left, fuzzy) ensures records are matched exactly how you need.
Aggregation & summarisation
Roll up daily prices into weekly averages, count products per category, compute min/max/sum — any aggregate function, at any time granularity, delivered pre‑computed for your dashboards.
Derived fields & business KPIs
Create calculated columns — price index vs. market average, margin estimates, growth rates, ranking positions. Your business logic, codified and applied automatically to every record.
Output tailored to any destination
Reshape columns, pivot rows, nest JSON — the pipeline formats data to the exact schema of Tableau, Power BI, Snowflake, or your custom application. No manual reformatting after delivery.
{
"sku": "NB-574-GRY-9",
"title": "Classic 574 Sneakers",
"avg_price_7d": 87.34,
"price_index": 103.2,
"category": "Footwear"
}
From raw feeds to a perfectly shaped dataset
Requirements & output design
We define the target schema, KPIs, joins, and business rules with your stakeholders — producing a transformation blueprint.
Pipeline & logic build
Our engineers implement the joins, aggregations, derived fields, and formatting rules in a scalable transformation engine.
Sample validation & review
A test batch of your data is transformed and delivered alongside an accuracy report — you verify that every calculation and join is correct.
Production deployment & monitoring
Transformation runs automatically on every new data load. We monitor output quality and adapt logic when source schemas or business rules change.
Deliverables for every data transformation project
Transformation blueprint
A document specifying all joins, aggregations, derived fields, and output schemas — reviewed and approved by your data team.
Sample transformed dataset
A representative batch processed through the pipeline, delivered with a validation report showing row counts, join matches, and KPI values.
Production pipeline + runbook
Automated transformation live on your data. A runbook documents the logic, refresh cadence, and how to request changes.
Monthly transformation health report
Summary of volumes, join rates, KPI trends, and any logic adjustments made — delivered proactively.
Flexible plans for every transformation scope
One‑Time Transform
Best for a single batch of data that needs to be restructured, joined, or aggregated before a one‑off analysis or report.
- Fixed scope & price
- Up to 5 transformation steps
- Single delivery of transformed data
Managed Transformation Feed
Ongoing transformation of every new data batch — we maintain the logic, your output is always analysis‑ready.
- Daily or per‑batch execution
- Unlimited transformation steps
- 99.9% accuracy SLA
Enterprise Data Engineering
For complex multi‑source transformations, custom logic libraries, dedicated engineers, and private infrastructure.
- Unlimited sources & transformation logic
- Dedicated data engineering team
- SSO, audit logs, quarterly reviews
Teams whose downstream systems demand a specific data shape
Business Intelligence
Deliver pre‑joined, pre‑aggregated, and pre‑calculated datasets that Tableau, Power BI, or Looker can consume natively — no more custom SQL in every dashboard.
Machine Learning & AI
Reshape raw web data into feature tables, normalised training sets, and prediction‑ready formats — automatically, at scale.
Dynamic Pricing
Join competitor prices with your product catalog, compute price indices and margins, and feed the transformed data directly into your repricing engine.
ERP & Operational Systems
Map scraped supplier data to your internal item master, transform it into the exact CSV/API format your ERP expects, and keep it in sync automatically.
Data Transformation vs. manual SQL/Python scripting
| Capability | Managed Data Transformation | Manual SQL / Python Scripts | Self‑Serve ETL Tools |
|---|---|---|---|
| Complex multi‑feed joins & aggregations | |||
| Custom business logic & derived KPIs | |||
| No code to write or maintain | |||
| Automatic adaptation to source changes | |||
| Scalable to billions of records | |||
| Ongoing monitoring & support | Included | Your team | Your team |
“Our dashboards finally show the numbers we actually need — not the raw data we had to live with”
"We had product data in one feed and pricing in another. The transformation pipeline joined them, computed weekly average prices, and added a price index — all automatically. Our category managers now have a dashboard they actually use."
"We needed to feed Tableau a very specific unpivoted dataset. Manually reshaping it every week was a nightmare. ScraperScoop's pipeline now does it automatically — the data lands in the exact format our dashboards expect."
"Our ML models needed features derived from three different scraped feeds. The transformation pipeline computes everything — ratios, moving averages, rankings — and feeds the training set directly into S3. Our data scientists never touch raw data anymore."
Transformed data lands exactly where your systems expect it
Schema‑matched, pre‑shaped, and ready to load — no manual reformatting, no import wizards.
Get a scoped quote for your data transformation project
Describe the output you need and the feeds you have — we'll return a fixed‑price estimate and a sample transformed dataset within two weeks.
Common questions about Data Transformation
Your data, reshaped into exactly the format your team needs
Share your raw data feeds and describe the output you envision. We'll return a transformed sample and a fixed‑price estimate within two weeks — no commitment, no sales pitch.
Most transformation pipelines deliver the first shaped dataset within 2–3 weeks of kickoff.
