Turning raw data into reliable data
Most organizations are able to collect data. But data that’s accurate, stored in the right place, and usable for decision-making, reporting, and AI—that’s a different story.
//01 — What is data engineering?
The infrastructure that makes data usable.
Data engineering involves building and managing the systems and processes that move data from point A to point B in the right format, at the right time, and without errors.
Think of pipelines that automatically retrieve data from sources such as your ERP, online store, CRM, or marketing tool, clean and transform that data, and then make it available in a central environment where everyone can work with it.
The difference from a system integration lies in the focus. An integration ensures that data moves between systems. Data engineering is about what happens next: how data is consolidated, structured, stored, and made available for analysis, reporting, or modeling. The two complement each other, and at Factor Blue, we build both.
The result of effective data engineering is a single central data source—a data warehouse or lakehouse—where all relevant information from your organization converges. It’s up-to-date, consistent, and accessible: to your team via dashboards, to your data analysts via SQL, and to your AI models via structured datasets.
Our work is supported by a strong network of technical partners, complemented by expertise in e-commerce and technology that we continuously develop and refine.
//02 — What do we build?
From raw data to actionable insights
The four components of a data engineering project.
Data pipelines
A data pipeline is an automated process that retrieves data from sources, transforms it, and makes it available in a central environment. We build pipelines that run at fixed intervals or respond to events in real time.
Data warehouse
A data warehouse is the central repository where data from all your systems is consolidated, structured, and ready for analysis. We work with cloud platforms such as Snowflake, Google BigQuery, and Azure Synapse, depending on what best suits your scale, budget, and existing infrastructure.
Transformation
Raw data from sources is rarely ready for immediate use. Fields have different names, dates are in various formats, and orders are spread across three tables that need to be merged. We build transformation layers using tools that clean, enrich, standardize, and merge data.
AI-ready data
AI models, predictive analytics, and machine learning only work effectively if the underlying data is clean, complete, and consistent. We structure data environments specifically to serve as a foundation for AI: well-modeled datasets, historical data for training, and an infrastructure that processes new data.
Trusted by ambitious brands
//04 — Factor Blue
Why choose Factor Blue for data engineering?
End-to-end: from source to insight
We build the entire data chain—from integrations, through data engineering, to dashboards and visualizations. You’ll work with a single partner who understands the entire stack
Certified engineers
Our data engineers are certified and work daily on integration projects for a wide range of organizations. They know the systems and the pitfalls.
Direct communication, no interference
You’ll work directly with the developers. Honest feedback, clear agreements, and no surprises down the road.
Post-delivery management
A data warehouse that isn’t maintained will deteriorate. Sources change, data models evolve, and new systems are integrated. Through Data Care, we manage the data environment in a structured manner.
//05 — Customer Feedback
Real customer experiences
Choosing a new technical partner isn’t something you do lightly. That’s why we let our clients and partners share their experiences working with us.
“I am extremely satisfied with Factor Blue’s technical expertise and solution-oriented approach, as well as the way they deliver the requested features as part of an ongoing development process. I can definitely recommend Factor Blue, especially in the field of B2B commerce.”
“We’ve been working together for quite some time now, and what we appreciate most is their commitment. Whenever we run into a challenge, the team is ready to help and resolve issues quickly. It feels like a partnership rather than just a supplier. We can wholeheartedly recommend Factor Blue to anyone looking for a Magento developer.”
“We’ve been working with Factor Blue for nearly three years now and have been very pleased with the partnership. We really appreciate the short lines of communication, their quick response to issues, and their expert advice on various challenges. In addition, their personal approach contributes to the success of our collaboration.”
“The Factor Blue team built a fantastic online store tailored exactly to our needs. The team immediately addressed the implementation phase and the necessary adjustments after the online store went live; they gave—and continue to give—us, as a client, their full attention.”
“We’ve been working with Factor Blue for years here at Hypernode. They’re a team of top professionals who are always a pleasure to work with—both for us and our mutual clients. True specialists in the field of Magento development.”
“Factor Blue excels at development, and we excel at online marketing. These are two disciplines that rely on each other to drive sustainable growth. Factor Blue implements the changes we need at the highest level. We’re very pleased with our partnership and are happy to recommend Factor Blue to both new and existing clients!”
“My experience working with Factor Blue has been very positive. They’re a reliable partner for the development of our platform. A clear plan, a hard-working team, and great communication!”
TELL US WHAT'S GOING ON
We’re the technical team that lies awake at night worrying about your uptime, your performance, and your next steps. Not as a contractor. Not as a supplier. But as the people who have just as much at stake in your success as you do.
You tell us what’s going on; we’ll tell you what we see.
//07 — Our Approach
From the initial consultation to
a fully functional data platform
Discovery
We start by taking stock of all relevant sources: what systems do you have, what data is in them, how reliable is that data, and what do you ultimately want to be able to do with it? That determines the architecture and the order in which we build.
Architecture Selection
Based on the assessment, we select the cloud platform, the pipeline architecture, and the transformation tools. We explain our choices: what it costs, what benefits it offers, and what the limitations are.
Building pipelines & loading data
We build the pipelines that retrieve data from the sources and load it into the data warehouse. Step by step, source by source, so you can see usable data in the central environment early in the process.
Transformations
We build the transformation layer that converts raw data into usable data models—clean, consistent, and documented. Data that’s accurate in every report and every dashboard.
Validation, documentation & handover
Before delivery, we validate the data against the sources. We provide documentation so your team understands how the environment works and can perform maintenance. And we connect you to Data Care if you want ongoing management support.
//08 — Frequently Asked Questions
Frequently Asked Questions
about data engineering
Frequently Asked Questions. Can’t find your question here? We’d be happy to answer it personally.
01. What is the difference between data engineering and data analysis?
Data engineering is all about the infrastructure: building pipelines, data warehouses, and transformation layers that make data accessible and reliable.
Data analysis is what you do next: discovering patterns, identifying trends, and supporting decisions.
Data engineering is the foundation without which analysis is unreliable. A data analyst working with poor or incomplete data will draw the wrong conclusions, no matter how good the analysis is.
Factor Blue builds the engineering layer; you can perform the analysis yourself or together with us using dashboards.
02. Which cloud platform is best for a data warehouse?
There is no one-size-fits-all solution—it depends on your existing infrastructure, your team, your scale, and your budget. Snowflake excels in flexibility, ease of use, and scalability, making it popular among medium-sized organizations. Google BigQuery works well if you’re already in the Google Cloud environment and process large amounts of data.
Azure Synapse is the logical choice if you have a Microsoft environment with Dynamics, Power BI, and Azure services.
Factor Blue works with all three and provides recommendations based on your specific situation, not on personal preference.
03. How long does it take to set up a data warehouse?
A first working version of a data warehouse with a limited number of sources and basic data models can be implemented in four to eight weeks.
A fully configured platform with multiple sources, complex transformation logic, and validated data models takes three to six months. The speed of the process is largely determined by the quality and documentation of the source data: clean, well-documented sources significantly accelerate the process.
Factor Blue works in iterations, so you see value early in the process rather than only upon delivery.
04. Do I need to modify my existing IT environment to get started with data engineering?
No. Data engineering works alongside your existing systems, not in place of them. Your ERP, online store, and CRM will continue to function exactly as they do now. The data pipelines retrieve data from those sources via APIs, database connections, or file exchange and write it to a separate data warehouse in the cloud. Your existing systems won’t be affected. What you do need is access: API keys, read permissions for the relevant tables, or an export option.
05. When does data engineering make sense, and when is it too much work?
Data engineering becomes useful as soon as you have more than two or three systems that you want to combine for reporting or analysis, or as soon as manual exports and Excel operations become too time-consuming or result in too many errors.
For an organization with a single primary system and limited analytics needs, a full-fledged data warehouse is often overkill—a direct connection to Power BI or Looker Studio may be sufficient.
As soon as you’re dealing with multiple data sources, historical data you want to retain, multiple teams working with data, or AI applications you want to build, a well-designed data warehouse is worth the investment.
