What Is Cloud Computing?

Cloud computing is the on-demand delivery of computing resources - servers, storage, databases, networking, analytics, and software - over the internet, paid for as you use them rather than owned and operated yourself. Instead of buying and maintaining physical hardware in your own data centre, you rent capacity from a provider such as AWS, Microsoft Azure, or Google Cloud, and scale it up or down in minutes. It is the foundation on which nearly every modern data platform - Snowflake, Databricks, Microsoft Fabric - is built.

Cloud computing matters because it changed the economics and speed of working with data. Capacity that once required a months-long hardware purchase is now available instantly; storage and compute that once had to be sized for peak load can now flex with demand. For data teams this is transformative - but it also scatters data across services, regions, and accounts faster than any manual process can track, which is precisely why cloud adoption and data governance have to grow together.

Cloud Computing Defined

At its simplest, cloud computing is renting someone else's computers - and the services that run on them - instead of buying your own. The provider operates vast data centres and exposes their capacity as services you access over the network. Five characteristics, as defined in the widely cited NIST model, distinguish true cloud computing: on-demand self-service (provision without a phone call), broad network access, resource pooling (shared infrastructure), rapid elasticity (scale in minutes), and measured service (pay for what you use).

The shift it represents is from capital expenditure (buy hardware up front, depreciate it for years) to operational expenditure (pay monthly for what you consume). That single change is why a startup and a bank can now access the same class of infrastructure, and why data platforms could grow as large and fast as they have.

Service Models (IaaS, PaaS, SaaS)

Cloud services sit on a spectrum defined by how much you manage versus how much the provider manages. The three classic tiers are:

• IaaS (Infrastructure as a Service). You rent raw building blocks - virtual machines, storage, networks - and manage everything above them (OS, runtime, applications). Maximum control, maximum responsibility. Examples: AWS EC2, Azure VMs.
• PaaS (Platform as a Service). You rent a managed platform - a database, a runtime, an analytics engine - and the provider handles the underlying servers and OS. You manage your data and applications. Most cloud data warehouses (Snowflake, BigQuery) sit here.
• SaaS (Software as a Service). You rent finished software over the web and manage nothing but your usage and data. Examples: Salesforce, Microsoft 365 - and Dawiso itself.

The higher you climb, the less you operate and the faster you move - at the cost of some control. Most organizations use all three at once.

Deployment Models

Separate from how much the provider manages is where the cloud runs:

• Public cloud. Shared infrastructure operated by a provider (AWS, Azure, GCP). Most cost-effective and elastic; the default for most workloads.
• Private cloud. Dedicated infrastructure for a single organization - on-premises or hosted - chosen for control, security, or regulatory reasons.
• Hybrid & multi-cloud. A mix - some data in public cloud, some private, often spread across more than one provider. This is the reality for most large enterprises, and the hardest to govern, because data and its context fragment across boundaries.

The deployment choice is frequently driven by data sovereignty - where the law requires data to physically reside and whose jurisdiction governs it.

Why It Matters for Data

For data teams, the cloud is double-edged. The upside is real: elastic compute makes large-scale analytics and AI affordable, managed platforms remove operational toil, and new environments spin up in minutes. But the same speed creates new problems:

• Sprawl. Data multiplies across services, regions, and accounts faster than anyone documents it - the modern version of the lost dataset.
• Cost surprises. Pay-as-you-go means an unoptimized query or runaway pipeline can quietly run up a large bill; managing this is its own discipline (see total cost of ownership).
• Visibility gaps. When data lives in a dozen cloud services, no single tool shows what you have, what it means, or where it flows - exactly the gap a catalog fills.

Governing Cloud Data

The cloud changes where data lives, not the need to govern it - and in the shared-responsibility model, governing the meaning, access, and compliance of your data is always yours, never the provider's. This is why a governed data catalog becomes more important in the cloud, not less. Dawiso connects to 40+ cloud and on-prem platforms, automatically discovering assets across Snowflake, Databricks, BigQuery, and the rest, and unifying them into one searchable, governed view - with lineage that traces data across service and cloud boundaries that the providers' own tools stop at. For multi-cloud and hybrid estates especially, the catalog is the one place that knows the whole picture, so that cloud scale does not become cloud chaos.

Conclusion

Cloud computing is the quiet foundation beneath nearly all modern data work: on-demand resources, billed by use, that made large-scale analytics and AI accessible to everyone. Understanding its service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid) is the first step; the second is recognizing that the cloud's speed scatters data as fast as it scales it. The organizations that thrive in the cloud are the ones that pair its elasticity with a single governed view of their data - so that what the cloud makes easy to create stays easy to find, understand, and trust.