Data Processing

Data processing and automation

That report that takes hours every week? The data moved by hand? We automate it from start to finish.

The problem

Every Monday, the same ritual

Without data processing and automation, this costs time every week. Every Monday morning the same ritual. Someone opens three systems, exports CSVs, pastes them into an Excel, adjusts the columns, runs a few formulas, and sends the result around. Two hours of work. Next week again. And when that person is off, nobody does it.

Meanwhile the numbers do not match. The CRM says 847 customers, accounting 832, and last month's spreadsheet says 819. Which number is right? Nobody knows. But decisions get made on it anyway. Budgets, forecasts, reports to management. All built on quicksand.

And then that one time someone forgot to copy a row. Or overwrote a formula. Or mapped a column wrong. Those are not hypothetical scenarios. They happen. Every month. And you only notice when a customer calls, or when the quarterly numbers are off.

Our approach

Automation with a pipeline that takes over the work

We build a pipeline that takes over the manual work. Pull data from your sources, clean it, combine it, transform it into the right format, and deliver it where it belongs. Every night, every hour, or every five minutes. Whatever you need. Without anyone having to think about it.

That monthly report generates itself and sits in the inbox of everyone who needs it on Monday morning. The data that now moves by hand from your webshop to your accounting, we synchronise automatically. The same numbers, everywhere. No more debate over which figure is right.

Every pipeline has monitoring built in. You see exactly how many records were processed and whether anything went wrong. Validation rules check the data before it lands in your system: wrong postcodes, duplicate records, missing fields. The pipeline filters those out, so you only see clean data.

What you get

Data processing and automation that runs itself

Pull data from your sources, clean it, and deliver it where it belongs
Jobs that run automatically: every 5 minutes, every night, or every Monday
Raw data turned into something usable: right formats, no duplicates
Systems kept in sync so everyone works with the same numbers
Alerts when something goes wrong, so you know before your customer does
Reports that generate themselves and land in your inbox
Large volumes processed without your system feeling the strain
Automatic checks that filter out bad data before it does damage
ETL pipelines that combine multiple sources into one reliable whole
Logging per run so you can always trace back what was processed
Stack:
Python
SQL
Data Pipelines
Automation Tools
PostgreSQL
Who it is for

When data processing and automation pays off

If you shift data between systems every week or build reports by hand, you will probably recognise one of these situations.

Automate monthly reporting

The report someone builds by hand every month. A pipeline pulls the data, runs the calculations and delivers the report straight to the inbox.

Synchronise systems

Stock, orders or customer data that has to match between webshop, WMS and accounting. A pipeline keeps the numbers aligned, with no retyping.

Clean up and migrate data

A move to a new system or a clean-up of messy data. We transform, deduplicate and validate before anything comes in.

Merge data sources

Numbers from different systems that never come together today. An ETL pipeline combines them into one dataset you can actually steer on.

How it works

From raw source to clean output

Every pipeline follows the same three steps, automatic and repeatable.

01

Extract

We pull data from your sources: databases, APIs, CSV, Excel or XML feeds. However messy the source is.

02

Clean and transform

Duplicates out, formats aligned, validation rules applied. Only clean data moves through.

03

Load

The result lands where it belongs: your database, your dashboard, or as a report in the inbox.

In practice

What a pipeline looks like day to day

Say you sell through a webshop and book in your accounting. Every night the pipeline pulls the new orders from the webshop, checks for duplicates, links them to the right customer and queues them for invoicing. No export, no pasting, no Monday morning ritual.

For a report it works the same way. The pipeline pulls numbers from multiple sources, runs the calculations and delivers a clean report to the inbox of whoever needs it on Monday morning. The same numbers for everyone, every time, without anyone having to think about it.

Why this way

ETL pipelines with validation built in

We build the data processing as an ETL pipeline: extract from the source, transform into the right format, load into the destination. We work with Python and SQL because they handle the heavy lifting and scale well. Large datasets we split into chunks and process in parallel.

Validation sits in every step. Wrong postcodes, duplicate records and missing fields are filtered out by the pipeline before they do damage. Every run logs what it processed, and if something goes wrong you get an alert. Clean data in your system, and certainty that it is correct.

In production

What we built with this

Logistics

Warehouse Management System

Pipelines that synchronise stock, orders and customer data every night between warehouse, webshop and accounting. Runs in production, handling the daily order flow. No more manual work, no more discrepancies.

Python
PostgreSQL
ETL
Automation

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Frequently asked questions about data automation

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