When companies transition to the cloud, they typically begin with applications and virtual machines, which is often the easier part of the process. The actual complexity arises later when databases are moved. To save time and effort, cloud adoption is more of a cloud migration in an IaaS manner, fulfilling current, but not future needs. Even organisations that are already in the cloud find that their databases, although “migrated,” are not genuinely modernised. This disparity becomes particularly evident when they begin to explore AI technologies.
Understanding Modernisation Beyond Migration
Database modernisation is distinct from merely relocating an outdated database
to Azure. It's about making your data layer ready for future needs, like
automation, real-time analytics, and AI capabilities. AI needs high throughput,
which can be achieved using native DB cloud capabilities.
When your database runs in a traditional setup (even hosted in the cloud), in
that case, you will encounter familiar challenges, including limited
scalability, slow data refresh rates, and complex ETL (Extract, Transform,
Load) pipelines. AI requires instantaneous data processing; it cannot wait for
nightly batch jobs to complete.
Recently, I assisted a client who had already migrated their systems to Azure.
Their SQL Server was running on an Azure VM, functional, but inflexible. When
they attempted to integrate AI-driven analytics and predictive models, their
performance suffered significantly because the database could not keep up.
I supported them in transitioning away from Azure VMs and integrating their
data with Microsoft Fabric. These changes had a significant impact on system
performance and database behaviour. Reports that previously took hours to
generate were now produced in minutes, and AI models could train on near-real-time
data without the need for complex data transfers or impact on other systems. The
effect of AI integration was the key factor that allowed them to fo forward
with the AI adoption.
They were in the same cloud
environment, but their experience and performance outcomes changed. This shows
that cloud modernisation is less about technological upgrades and more about
unlocking new potential using core services.
Modern cloud database services, such as Azure SQL Hyperscale and Cosmos DB, are
built for scalability and speed. They scale natively for various demands, are
designed for large datasets, and are well-integrated with AI endpoints.
With these modern services, the focus has shifted from managing or tuning
indexes to business outcomes, gaining insights, making predictions, and integrating
with AI. It empowers you to focus on AI initiatives, progressing from MVPs to
full production with confidence.
AI relies heavily on data. For this data to be truly valuable, it must be
fresh, consistent, and easily accessible. By modernising your database, you
create connections to a broader ecosystem, such as Microsoft Fabric, Power BI,
and AI ecosystem, as Azure Machine Learning. For instance, with Cosmos DB
mirroring to OneLake, your operational data can be integrated into analytical
models almost instantly.
This capability enables real-time applications, such as fraud detection,
personalised recommendations, and intelligent automation, all powered by your
now-modernised data estate.
Adopting AI inside the cloud means gaining better control over your data. Azure
SQL Database provide built-in encryption, automated backups, and advanced
threat protection. Microsoft Purview can classify and govern data across your
entire organisation. This is essential for AI, as data quality and compliance
directly impact model accuracy and trust. Modernisation promotes confidence
among your teams and leadership.
From my experience with numerous modernisation projects, one fundamental truth
remains: success occurs when teams abandon the perception of the database as
mere “infrastructure” and begin to view it as a strategic enabler. Once this
shift in mindset happens, everything else, like agility, innovation, and successful
AI adoption, naturally follows.
Conclusion
Database modernisation in the cloud is not just about adopting new features or
technology trends; it is about preparing your data foundation for
"the" AI.
Modernised databases provide you with faster insights, a more flexible system,
and your teams' focus on business outcomes, rather than on DB optimisation and
performance. The journey of modernisation starts not with the simple act of
moving data to the cloud but with making that data ready to think and evolve
alongside you.
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