We only have spreadsheets. Is that enough to start?

Yes. Many projects begin by consolidating spreadsheets and exports into a proper database, then automating the refresh.

Which cloud do you work on?

We work with Azure and AWS, and can also design lightweight setups on managed databases when a full warehouse would be overkill.

Can you add reporting to an app you did not build?

Usually, yes. We connect to its database or API and build reporting alongside it.

Service

Data Engineering

Your data is only useful when people can trust it and find it. We build the pipelines, models and dashboards that move data from scattered systems to a single, reliable source of truth.

Discuss this service

What we do

What’s included

ETL / ELT pipelines
Scheduled, monitored pipelines that pull data from apps, APIs and files into one place, orchestrated with tools like Apache Airflow.
Real-time & streaming
Low-latency stream processing with Apache Flink for data that cannot wait for a nightly batch.
Data warehousing & lakehouse
Clean, documented schemas and Microsoft Fabric or cloud warehouses designed for the questions your team actually asks.
Analytics APIs & semantic layers
Cube.js data models and APIs that power consistent, real-time reporting and embedded analytics.
Dashboards & monitoring
Self-serve reports, KQL queries and Grafana dashboards that replace manual spreadsheets.
Data quality & automation
Validation, lineage, access rules and automated workflows so numbers stay trustworthy.

$ stack --list

Tools we reach for

Python
SQL
Apache Flink
Apache Airflow
Cube.js
Microsoft Fabric
Grafana
AWS
Azure
PostgreSQL
MS SQL Server
MongoDB
Supabase

How it runs

Three steps, no surprises

01
Map your sources
We inventory the systems, owners and questions behind your reporting.
02
Model, then move
A clear data model first, then pipelines that populate it reliably.
03
Report and monitor
Dashboards plus alerts when a pipeline slows or a number looks wrong.

Good questions