In our previous newsletter, we showed you how a modern data platform can slash the time needed for complex analytical operations from hours to just a few minutes. Reading that, you might think: “Sounds great, but implementing something like this must cost millions and take years.”
And you have every right to think that, because the old approach conditioned us to expect astronomical costs. Except technology has moved forward. Let’s look at what has changed.
Where did budgets vanish without a trace in traditional projects?
In the classic approach to building traditional data warehouses (so-called Legacy DWH), the high cost rarely stemmed from the business value itself. The real budget-guzzler was… the technological “plumbing”.
A traditional project was like trying to assemble a car from parts bought from a dozen different manufacturers. A company had to pay separately for database server licenses, separate data integration tools, different software for data cleansing and yet another for reporting.
Then, a team of expensive engineers would spend months just forcing all these pieces to work together stably. You were paying to build bridges between technologies, rather than for the data analysis itself. The budget evaporated before the board ever saw a single valuable report.
Paying for value, not “connecting cables”: The Microsoft Fabric Revolution
Microsoft Fabric completely flips this model on its head because it operates as a SaaS (Software as a Service) platform. All the necessary components are already pre-integrated into a single ecosystem.
What does this mean for a company’s budget?
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- No costs for “gluing” tools together: Since all elements of the platform are integrated out of the box, the deployment budget isn’t wasted on configuring connections between software from different vendors. Instead, from day one, you pay for what matters most – organizing your data and creating business-ready reports.
- Flexible cost scaling: You pay for a single, shared pool of compute capacity. You can drastically scale it up in the morning when the board is generating reports and scale it down (or turn it off completely) at night when the system is idle. This dramatically lowers cloud infrastructure costs compared to traditional, always-on servers.
This is best illustrated by hard data from one of our projects. Moving processes to a modern engine allowed us to slash data processing time by over 95% – from 4 hours to just 7 minutes. Instead of paying for hours spent by analysts waiting for a report refresh, the business gets information almost in real time.
Why your legacy data warehouse is blocking your AI?
Today, every board of directors wants to leverage artificial intelligence for things like sales forecasting or process automation. However, trying to “plug” modern AI models into a legacy data warehouse acts as a technological handbrake.
Classic systems were designed with structured tables and historical reporting in mind. Artificial intelligence needs more: it must instantly analyze text, files, and data from various CRM, ERP, and POS systems, which a legacy warehouse cannot process efficiently. What’s more, traditional databases typically refresh data only once a day – which is far too slow for AI models.
A modern data platform stores information in open formats, giving AI algorithms instant access to data – with no delays, no copying and no additional infrastructure costs.
In the next article
Now that you know why it’s time to ditch your legacy data warehouse, it’s time to answer the key question: how do you do it safely, without months of downtime and the risk of data loss?
In the next article, we will take you behind the scenes of our proprietary framework at DataRiseLab. We’ll show you how automation allows us to slash deployment time by 80%, cut Microsoft Fabric migration costs up to 3x, and wrap up the entire project in just 5 to 9 weeks – all while guaranteeing a seamless transition with zero disruption to your daily business operations.
Do you feel like your infrastructure costs are climbing, yet your systems are still not ready for the era of AI?
Instead of guessing, let’s talk about the realities of your business. Write to us directly or reach out to our Head of Growth, Bartosz Rutkowski: