Cairn for Fabric
How can you migrate from a legacy DWH to Microsoft Fabric in weeks , not months?
DataRiseLab’s proprietary solution, Cairn for Fabric, helps organizations build a modern data platform faster while reducing implementation costs and the effort needed to launch it.
5-9
weeks until the platform launches
up to 80%
shorter implementation time
2-3x
lower cost
2
engineers instead of a team of 5–8 people
Why not every Microsoft Fabric migration needs to start from scratch?
In traditional projects, much of the work has to be rebuilt from the ground up each time. Cairn for Fabric uses a shared, battle-tested foundation and automates repetitive delivery tasks, reducing manual effort, minimizing team size requirements, and making project scope and timelines far more predictable.
Traditional approach
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- much of the platform is built from scratch
- repetitive tasks are handled manually
- larger teams and less predictable project scope
- quality controls, monitoring, and standards are designed separately for every implementation
Cairn for Fabric
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- reusable, proven foundation components
- automation of repetitive delivery tasks
- leaner teams and greater project predictability
- built-in quality, monitoring, and governance from the start
When should you consider migrating from a legacy data warehouse to Microsoft Fabric?
As organizations grow, so do the number of systems, data sources, and reporting requirements. Businesses expect faster access to information, greater flexibility, and the ability to leverage advanced analytics and AI.
Over time, however, expanding an existing data platform requires increasing effort. Each new integration or change involves additional specialists, extends delivery timelines, and drives up costs.
This does not mean the existing data warehouse was poorly designed. It was simply built for different business needs and within a different technological landscape.
What is Cairn for Fabric?
The name of the solution is no coincidence. A cairn is a stone marker that helps travelers find the right path when the route is not obvious.
Cairn for Fabric works in much the same way. It is DataRiseLab’s proprietary framework that structures and automates the journey from a legacy data warehouse to Microsoft Fabric. By leveraging proven components and architectural patterns, it eliminates the need to build every part of the data platform from scratch, accelerating delivery while reducing implementation effort and risk.
Cairn for Fabric works like an advanced digital assembly line. Instead of building every house from the ground up, you arrive on site with high-quality prefabricated components ready to be assembled.
Łukasz Lelwic
Data Architect, DataRiseLab
What are the benefits of migrating to Microsoft Fabric with Cairn for Fabric?
Faster deployment, lower costs, and greater confidence that your platform is ready to scale and evolve.
Faster time to value
We automate and streamline the most repetitive tasks, including metadata analysis, dependency mapping, and platform artifact generation. This enables organizations to achieve their first business outcomes in 5-9 weeks, instead of waiting months for the platform to deliver value.
Lower costs and a more predictable budget
Achieve 2-3× lower implementation costs and deliver with a smaller team. We eliminate the need to recruit expensive, hard-to-find Microsoft Fabric specialists, while the SaaS model helps reduce ongoing maintenance costs in the years ahead.
Foundation for AI
AI is only as reliable as the data behind it. That’s why we begin by creating a unified, well-structured data platform, which then becomes the foundation for scalable and trustworthy AI solutions.
Zero-downtime migration
Your new platform is built alongside your existing environment. Once the data has been validated, the switch can be made seamlessly, allowing sales, invoicing, and reporting processes to continue without interruption.
The golden rule: Discovery
We automate technology, not business understanding. As the saying goes, “If you automate a mess, you’ll get a faster mess.” That’s why Discovery remains a critical phase, ensuring the platform is built on a solid business foundation and delivers meaningful outcomes from day one.
A platform that scales with you
Your team can continue developing the platform independently by adding new analytics capabilities, data sources, and AI models, without the need to constantly rely on external engineers. The result is a significantly lower Total Cost of Ownership (TCO).
How to ensure data quality during a Microsoft Fabric migration?
Data quality, monitoring, and auditability from day one
Data moves through a structured:
bronze → silver → gold architecture, with automated quality checks applied at every stage before it reaches reports and dashboards. Faulty records are automatically isolated in quarantine, preventing them from silently impacting business decisions.
This enables teams to detect and resolve issues before users are affected. At the same time, full data lineage ensures that every number can be traced back to its source and transformation history, making audits and root-cause analysis fast and straightforward.
Monitoring and alerts before users notice issues
The platform not only processes data but also continuously monitors it across every stage of the pipeline and automatically detects anomalies such as:
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- delays in data refreshes
- changes in source system structures
- sudden increases in missing values
- unusual volumes of processed records
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Auditability: where exactly does this number come from?
When executives or auditors ask about a specific figure, organizations often find themselves manually tracing data sources and searching for the person who remembers how the number was calculated.
With Cairn for Fabric, every metric retains information about its origin and transformation history. Teams can quickly identify the source data and review each step of the data processing journey without spending hours on investigation.
How to Prepare Data for AI in Microsoft Fabric?
AI is only as reliable as the data behind it.
Implementing an AI model alone is not enough if data across the organization is fragmented, inconsistent, or based on different definitions of the same business metrics. In such cases, even the most advanced AI model may deliver answers quickly, but not necessarily answers that can be trusted for decision-making.
We build Cairn for Fabric around a single, unified data layer in OneLake and proven architectural patterns that form the foundation of a modern data platform.
AI does not verify whether the data it works with is consistent, accurate, or trustworthy. As a result, it can confidently produce answers that appear convincing but are ultimately incorrect. That is why the sequence matters: first a well-governed data platform, then AI.
CASE STUDY
QSR
How Microsoft Fabric reduced analysis time from 4 hours to 7 minutes
A leading QSR (Quick Service Restaurant) chain was processing up to 80 million receipt line items per day. Calculating sales performance by hour took approximately four hours, meaning critical insights arrived too late to support timely operational decisions.
We did not replace the reports that the business team already knew and relied on. Instead, we migrated the analytical processing to Microsoft Fabric, effectively replacing the underlying data processing engine while preserving the existing reporting experience.
The result? The same analysis that previously required four hours is now available in just seven minutes.
This allows the Operations Director to identify a drop in sales at a specific location, unusual activity during a particular hour, or other anomalies much sooner and take corrective action while the information is still relevant and actionable.
%
Faster
Not sure if now is the right time to migrate to Microsoft Fabric?
Let’s talk.
Together, we’ll take a closer look at your data environment and assess whether migrating to Microsoft Fabric makes sense for your organization today. If it doesn’t, we’ll tell you so honestly.
During the session, we’ll identify your current starting point, discuss your key business requirements, and outline a potential roadmap for the future evolution of your data platform.