nordic_data

data engineering
consulting

with data engineering, due dilligence makes a difference - with nordic_data, you'll get your data cleaned, transformed, and ready to go

Strong data engineering is the backbone of every successful project. Clean, reliable, and well-structured data ensures that dashboards, automations, and analyses actually work as intended. Without it, insights are incomplete or misleading, and projects quickly lose value. By shaping and preparing data from the start, data engineering makes every other step more efficient and impactful.

explained:

what is data engineering?

Data engineering is the process of collecting, cleaning, and organizing raw data so it can be used effectively. Most data doesn’t arrive in the right format — it comes from different systems, with gaps, errors, or inconsistencies. Data engineers make sure this information is reliable, structured, and ready for use.

At nordic_data, we see data engineering as the foundation of every project. By building pipelines and models with tools like Azure Databricks and Microsoft Fabric, we ensure that data flows smoothly and can be trusted. Without this groundwork, dashboards, reports, and automation simply wouldn’t deliver real value.

advantages of data engineering

reliability

Data engineering ensures your data is accurate, consistent, and trustworthy. When the foundation is solid, every dashboard and analysis built on top of it delivers real value.

scalability

Well-designed data pipelines handle growing volumes of information without breaking. This means your systems stay fast and reliable as your organization expands.

integration

Data engineering connects information from different systems — cloud, on-prem, or third-party — into one place. This unified view makes decision-making easier and faster.

flexibility

With strong data engineering, your data is modeled in a way that adapts to new needs. Whether it’s a new dashboard, platform, or tool, your data is ready to support it.

which data engineering ecosystem is best?

Azure, AWS or GCP?

Choosing between Azure, AWS, and GCP depends less on which platform is “the best” and more on which one fits your organization’s needs. All three offer powerful tools for storage, pipelines, machine learning, and analytics. AWS is the most mature and feature-rich, Azure integrates seamlessly with Microsoft products like Power BI and Office 365, and GCP shines in machine learning and big data processing.

At nordic_data, we focus on meeting you where you are. If your business already runs on Microsoft, Azure will usually provide the smoothest path. If flexibility and breadth of services are key, AWS may be the better choice. And if advanced AI and data science are central to your projects, GCP often leads the way. The best ecosystem is the one that aligns with your existing stack, skills, and future goals.

NEW: Microsoft Fabric

the all-in-one solution

Microsoft Fabric is a modern end-to-end analytics platform that brings together data integration, engineering, real-time analytics, and business intelligence into a single SaaS environment.

unified platform

seamless integration

scalability and performance

related services

business intelligence (BI)

For many business-side stakeholders, BI dashboards are the only place they can directly interact with organizational data. This makes them crucial for data-driven development. Whether you're using Power BI, Tableau, or QlikSense, we can get you where you need to be.

automation (RPA)

At nordic_data, we use Microsoft's Power Platform to deliver automation at scale within the Microsoft ecosystem. For custom automation uses, we leverage UiPath.

data migration

Migration projects can be daunting, but our experienced data and ETL engineers can guide you through the process - whether you're moving to cloud, SAP, or Workday.

data analysis

If you're struggling to make sense of the data, we're here to help. Based on your business case and the data visualizations, we can deliver insights, forecasts, and recommendations. For exploratory approaches, we can use statistical methods, machine learning tools, and more.

ready to start?
let's talk

reach out and schedule a free 30-minute consultation

frequently asked questions

It's a lot of things. Data engineering can contain parts of data collection (via APIs, web scraping, etc.), data cleaning (such as removing duplicates or ensuring ISO standards), data transformation (such as introducing calculated fields or joining with other data) and more.

We are a Microsoft Partner, so we are most used to the Microsoft ecosystem, which is Azure and Fabric. However, if you're already using AWS, GCP, Snowflake or another tool, we can still make it work.

Sure. If you have specific changes in mind, we can introduce them to existing code. If you're unsure about what your data engineering pipeline needs to comply with your new business logic, we can work it out together.

Yes. By the end of the project, you will have full access to and ownership of any code we write. You will also receive full documentation if needed.

data engineering consulting

what is data engineering?

advantages of data engineering

reliability

scalability

integration

flexibility

which data engineering ecosystem is best?

NEW: Microsoft Fabric

related services

business intelligence (BI)

automation (RPA)

data migration

data analysis

ready to start? let's talk

frequently asked questions

ready to start? let's talk

quick links

services

data engineering
consulting

ready to start?
let's talk

ready to start?
let's talk