AWS vs. Azure vs. Google Cloud: Which Cloud Platform is Best for Analytics?
Choosing a cloud platform for analytics isn't just about picking the biggest name. It’s about finding the right combination of tools, pricing, scalability, and integration capabilities that suit your organisation’s data goals. AWS, Azure, and Google Cloud all offer enterprise-grade analytics solutions, but each has strengths worth weighing up before locking in.
For businesses navigating cloud adoption or upgrading their data infrastructure, understanding these differences is essential for driving performance and ROI.
Key Considerations When Choosing a Cloud Analytics Platform
Before diving into the pros and cons of each platform, it's helpful to define what makes a cloud service suitable for analytics:
Data ingestion and integration – How well can it pull in data from various sources?
Scalability and performance – Can it handle growth and peak workloads?
Advanced analytics and AI/ML tools – What native services are on offer?
Ease of use – Is it developer-friendly or suitable for business analysts too?
Pricing – Are the cost structures transparent and manageable?
Local availability – Are there data centres in Australia for compliance and speed?
With that context, let’s compare how AWS, Azure, and Google Cloud stack up.
Amazon Web Services (AWS)
Strengths:
AWS is the largest and most mature cloud platform, with a deep catalogue of analytics services. Products like Amazon Redshift, AWS Glue, and Amazon Athena are widely adopted and well-documented. AWS also supports strong data lake architecture with Amazon S3, and integrates seamlessly with a wide range of third-party tools.
For machine learning, SageMaker offers a managed environment to build, train, and deploy models, making AWS appealing for organisations investing in predictive analytics.
Considerations:
Pricing can become complex due to the sheer number of services and potential overprovisioning.
The learning curve can be steep, especially for businesses without dedicated cloud engineers.
In some use cases, such as streaming analytics, AWS may require stitching together multiple services, increasing operational overhead.
Best For:
Organisations looking for enterprise-grade performance, a wide choice of services, and strong ML capabilities – particularly those already embedded in the AWS ecosystem.
Microsoft Azure
Strengths:
Azure is often the preferred choice for businesses already using Microsoft products like Power BI, Excel, and SQL Server. Azure Synapse Analytics is a standout offering – combining data warehousing and big data analytics in a single platform. The platform also features Azure Data Factory for ETL and Azure Machine Learning for advanced analytics.
Another major advantage is its integration with Active Directory and other Microsoft enterprise tools, making it easier to manage identity and access across platforms.
Considerations:
While the ecosystem is improving rapidly, some services are not as mature or widely adopted as AWS alternatives.
Performance tuning in Azure Synapse can require deeper technical knowledge than expected.
Best For:
Enterprises with a Microsoft-heavy stack, or those that want a tightly integrated cloud ecosystem with strong BI capabilities. Also a solid choice for organisations focusing on governance and identity management.
Google Cloud Platform (GCP)
Strengths:
Google Cloud is widely seen as the innovator in analytics, largely thanks to BigQuery – a fully-managed, serverless data warehouse that’s ideal for massive-scale analytics. BigQuery’s performance with SQL-based analysis over huge datasets is exceptional, and its pay-per-query model can be cost-effective for variable workloads.
Google also leads in artificial intelligence and machine learning, offering Vertex AI and easy access to Google’s pre-trained models. Integration with tools like Looker and Data Studio enhances visualisation and reporting.
Considerations:
While GCP is gaining popularity, it still trails AWS and Azure in market share and enterprise adoption, particularly in Australia.
Some services may feel geared more toward technical users and data scientists than business analysts.
Best For:
Data-driven businesses prioritising innovation, scalability, and advanced analytics. GCP is particularly suited to startups, digital-native firms, and organisations with strong data science teams.
Australian Context: What’s Relevant Locally?
For businesses operating in Australia, data residency and latency are important. All three providers have local data centres – AWS in Sydney and Melbourne, Azure in multiple regions including Canberra for government clients, and Google Cloud in Sydney and Melbourne.
Another key factor is compliance. If you’re handling sensitive data, particularly in health or finance, Azure’s alignment with Microsoft’s compliance framework and government standards may give it an edge.
Also, the local partner ecosystem matters. AWS tends to have a broader partner network, but Microsoft and Google are actively investing in Australian cloud adoption programs and certifications. Support availability, training options, and consulting partnerships can affect long-term success on any platform.
So, Which Cloud is Best for Analytics?
There’s no one-size-fits-all winner – the best platform depends on your use case:
For many businesses, a multi-cloud or hybrid approach may offer the most flexibility. For instance, using Azure for reporting and governance while leveraging BigQuery for high-speed ad-hoc analytics.
When it comes to cloud analytics, the best choice isn’t about popularity – it’s about aligning the platform’s strengths with your business goals, internal capabilities, and growth plans.