Consultants based in Stockholm · on site or remote
Data Engineering & Warehousing
We design and build the pipelines that move data from your business systems into one trusted place, and on to the reports, dashboards and AI models that use it. In your data centre, in the cloud, or a mix of both.
The lifecycle
Every stage of the data engineering lifecycle
We cover the whole journey, from the systems that generate data to the analytics, ML and operational tools that use it. Click a stage to see what we do there and the tools we use.
Data platform
Value delivered
Undercurrents
Generation
Data is born in ERP, CRM, IoT devices, SaaS apps and files. We map the source systems, their owners and how data can be extracted safely.
What it means for you
We find out where your important data really lives, so nothing gets missed and source systems are not slowed down.
- Source system analysis and data contracts
- Change data capture (CDC) where the source supports it
- Volumes, frequency and latency requirements
Tools we use
How we build it
From first meeting to a platform you own
A clear, step-by-step way of working. You see results early and keep full ownership of everything we build.
- 1
Discover
We meet the people who use the data and map your source systems, reports and pain points.
Current-state map and priorities
- 2
Design
We propose an architecture (cloud, on-premise or hybrid) with cost estimates and a delivery plan.
Architecture and roadmap
- 3
Build
We build pipelines and data models in short iterations, delivering the most important data first.
Working pipelines and models
- 4
Test & govern
Data quality tests, access rules, lineage and documentation are added as we build, not afterwards.
Trusted, documented data
- 5
Run & hand over
We set up monitoring and alerts, train your team, and support you as much or as little as you need.
A platform your team can run
Architecture options
Cloud, on-premise or hybrid: built where your data lives
There is no single right platform. We start from your systems, rules and budget, and pick the setup that fits. Choose an option to see how the pipeline is built.
On-premise sources, cloud analytics
Source systems stay where they are. Secure, outbound-only connections move the data to a cloud lakehouse, so you get modern analytics without moving your ERP.
1. Your systems
Where data is created
ERP database
SQL Server · Oracle
File shares
CSV · Excel
SaaS CRM
Dynamics 365 · Salesforce
2. Move
Pipelines collect it
Self-hosted integration runtime
Azure Data Factory · outbound only
SSIS packages (lift & shift)
Azure-SSIS IR
3. Store & refine
Cleaned and combined
Lakehouse
Databricks · Delta Lake
4. Serve
Made ready to use
Semantic model
Databricks SQL · Fabric
5. Use
Where value is created
Power BI service
Reports · dashboards
ML & AI
MLflow · Azure OpenAI
Apps & APIs
Streamlit · React
A good fit when
- Core systems must stay on-premise, but you want cloud analytics
- You are moving to the cloud step by step
- You have existing SSIS packages worth keeping
What you get
- No firewall openings: connections go outbound only
- Existing SSIS work reused or migrated gradually
- Cloud scale for analytics and AI
See it in action
One catalog for every pipeline
An interactive example in the style of OpenMetadata: browse assets across SSIS, Azure Data Factory, Databricks and Power BI, follow lineage, run data quality tests and watch pipelines execute.
- Click any node in the lineage graph to jump to that asset
- Run the data quality suite or a pipeline to see live results
- Search across all services from the top bar
Capabilities: click one to see it in the example
customers
Customers matched between CRM and ERP, with full change history (SCD type 2).
Click a node to open that asset.
Illustrative demo with sample data.
Lineage: know where every number comes from
When a figure in a report looks odd, you can trace it back through every step to the source system in seconds, and see what a change would affect before you make it.
Data quality: catch errors before your reports do
Automatic tests check the data every time it loads. Bad rows are set aside and the owner is alerted, so wrong numbers never quietly reach a dashboard.
Pipeline runs: see that data arrived on time
Every load, whether it is an SSIS package on-premise or a Databricks job in the cloud, is visible in one place with its history and duration.
Let's talk data.
Tell us where you are today and where you want to go. We will help you find the shortest path there.