Microsoft Fabric vs Snowflake vs Databricks - Which Data Platform
Microsoft Fabric vs Snowflake vs Databricks - Which Data Platform
We get asked this question constantly. A client is rethinking their data platform, they've shortlisted Microsoft Fabric, Snowflake, and Databricks, and they want to know which one to pick.
The honest answer is that all three are strong platforms, but they come from different backgrounds, excel at different things, and suit different organisational profiles. In this post, I'll share what we've seen across dozens of engagements helping Australian businesses choose and implement data platforms.
Quick Comparison Overview
Before we get into detail, here's a high-level comparison:
| Dimension | Microsoft Fabric | Snowflake | Databricks |
|---|---|---|---|
| Primary strength | Unified Microsoft analytics | Cloud data warehousing | Data engineering and ML |
| Pricing model | Capacity Units (CU) | Credit-based (per query) | DBU-based (per cluster) |
| Best for | Microsoft-heavy organisations | SQL-heavy analytics teams | Data science and engineering teams |
| Reporting | Power BI (native) | Third-party (Tableau, Power BI) | Third-party or Databricks SQL |
| Real-time | Good (KQL, Eventstreams) | Limited (Snowpipe Streaming) | Good (Structured Streaming) |
| Data lake support | OneLake (native) | Iceberg support | Delta Lake (native) |
| Governance | Purview integration | Native governance | Unity Catalog |
| Australian data centres | Yes (Australia East/Southeast) | Yes (Sydney) | Yes (Sydney) |
| Learning curve | Moderate (familiar for MS users) | Low (SQL-centric) | High (Spark-centric) |
Where Microsoft Fabric Wins
You're already a Microsoft shop. If your organisation runs Microsoft 365, uses Azure for infrastructure, has Power BI for reporting, and your data team knows T-SQL, Fabric is the path of least resistance. The integration between OneLake, Power BI, Data Factory, and the Fabric Warehouse is genuinely good. Your team doesn't need to learn a new ecosystem.
Unified billing matters to you. Fabric's single capacity model means one bill, one pool of compute, and no surprises from multiple services charging independently. We've seen this simplify budgeting significantly for finance teams that struggled with unpredictable Azure Synapse and Databricks invoices.
Power BI is your reporting standard. No other platform matches the native Power BI experience in Fabric. Direct Lake mode - where Power BI reads directly from OneLake Parquet files without importing data - is a genuine technical advantage. It eliminates data duplication and refresh delays.
You want an all-in-one platform. Fabric covers data ingestion, transformation, warehousing, data science, real-time analytics, and reporting in a single service. If you're a mid-market company that doesn't want to manage five different platforms, this matters.
In our experience, Fabric is the strongest choice for organisations with 500-10,000 employees that are already invested in the Microsoft ecosystem and want to consolidate their analytics stack.
Where Snowflake Wins
Pure SQL performance at scale. Snowflake's query engine is exceptional. For organisations that run thousands of SQL queries per day across terabytes of data, Snowflake's auto-scaling virtual warehouses are hard to beat. The ability to spin up isolated compute for different teams without contention is a genuine advantage.
Multi-cloud flexibility. If your organisation runs workloads across AWS, Azure, and GCP, Snowflake gives you a consistent data platform across all three. Fabric is Azure-only. Databricks runs on multiple clouds but requires separate deployments.
Data sharing and marketplace. Snowflake's data sharing capabilities are best-in-class. If you need to share live data with partners, suppliers, or customers without moving files around, Snowflake makes this straightforward. The Snowflake Marketplace also gives you access to third-party data sets.
Your team is SQL-first. Snowflake has the lowest learning curve for analysts who think in SQL. The interface is clean, the documentation is excellent, and the SQL dialect is well-designed.
We typically recommend Snowflake for organisations where the primary use case is large-scale SQL analytics, where multi-cloud is a real requirement (not just a theoretical one), or where data sharing with external parties is a core business need.
Where Databricks Wins
Data engineering at scale. If you're processing petabytes of data with complex transformations, Databricks is purpose-built for this. Apache Spark is still the gold standard for large-scale data processing, and Databricks provides the best managed Spark experience available.
Machine learning and AI. Databricks has the most mature ML tooling of the three platforms. MLflow for experiment tracking, feature stores, model serving, and the integration with popular ML frameworks make it the natural choice for organisations with dedicated data science teams.
Delta Lake and Lakehouse architecture. Databricks pioneered the lakehouse concept with Delta Lake, and their implementation is the most mature. If you want ACID transactions on your data lake with time travel, schema enforcement, and optimised file management, Delta Lake on Databricks is excellent.
Open-source alignment. Databricks contributes heavily to open-source projects (Spark, Delta Lake, MLflow). If your organisation values open standards and wants to avoid vendor lock-in on the data format layer, Databricks aligns well with that philosophy.
We recommend Databricks for organisations with strong data engineering and data science teams who need maximum flexibility and are comfortable with a Spark-based ecosystem.
Head-to-Head Pricing Comparison for Australian Organisations
Pricing is notoriously difficult to compare across these platforms because they use different billing models. Here's our best attempt at an apples-to-apples comparison for a typical mid-market workload:
Scenario: Mid-market company with 2TB of data, daily ETL, 50 report users, moderate query load
| Cost Component | Microsoft Fabric | Snowflake | Databricks |
|---|---|---|---|
| Compute | F32 capacity: ~$6,400/mo | Medium warehouse (8hr/day): ~$4,000-6,000/mo | Standard cluster (8hr/day): ~$4,500-7,000/mo |
| Storage | OneLake: ~$100-200/mo | Snowflake storage: ~$120-250/mo | ADLS/S3 + Delta: ~$100-200/mo |
| Reporting | Included (Power BI) | Separate (Power BI Pro: ~$17/user x 50): ~$850/mo | Separate (Power BI Pro): ~$850/mo |
| Governance | Included (basic) | Included | Unity Catalog: Included in Premium |
| Estimated total | ~$6,700-6,800/mo | ~$5,000-7,100/mo | ~$5,450-8,050/mo |
These are rough estimates. Actual costs depend heavily on query patterns, data volumes, and how aggressively you optimise.
The takeaway is that for a typical mid-market workload, the three platforms land in a similar cost range. The total cost of ownership differences come down to:
- Existing licences - If you already have Power BI Premium or an Azure EA, Fabric gets cheaper.
- Team skills - The platform your team already knows costs less to operate.
- Integration costs - The platform that best fits your existing stack requires fewer add-on tools.
The Real Decision Framework
After working through this comparison with many clients, we've found that the technical capabilities matter less than these three questions:
1. What ecosystem are you already in?
If 80% of your infrastructure is Azure and Microsoft, choose Fabric. If you're on AWS with Tableau for reporting, Snowflake or Databricks will integrate more naturally. Going against your existing ecosystem creates friction that costs more than any platform savings.
2. What does your team know?
A Fabric implementation led by a team that knows Spark and Python but not T-SQL or Power BI will struggle. Similarly, putting Databricks in front of a team of SQL analysts is asking for trouble. Match the platform to the people, not the other way around.
3. What's your primary use case?
- Reporting and BI - Fabric (with Power BI) or Snowflake (with your choice of BI tool)
- Large-scale data engineering - Databricks or Fabric (Spark notebooks)
- Machine learning and AI - Databricks, then Fabric (with Azure ML integration)
- Multi-cloud data sharing - Snowflake
- Real-time analytics - Fabric (KQL) or Databricks (Structured Streaming)
- All of the above - Fabric offers the broadest coverage in a single platform, but won't be best-in-class at every individual task
What About Using Multiple Platforms?
Some larger organisations use two platforms - for example, Databricks for data engineering and ML with Snowflake or Fabric for warehousing and reporting. This can work, but it adds complexity in data movement, governance, and team skills.
We generally advise against multi-platform architectures for organisations under 2,000 employees. The operational overhead isn't worth it. Pick one platform and go deep.
For larger enterprises with dedicated data engineering and analytics teams that operate somewhat independently, a dual-platform approach can make sense. Just be deliberate about where the boundary sits and how data flows between platforms.
Common Mistakes We See in Platform Selection
Choosing based on a vendor demo. Every platform looks amazing in a demo. Insist on a proof of concept with your actual data and your actual team. We've seen many organisations pick a platform based on a polished sales demo and regret it six months later.
Over-weighting features you won't use. Databricks has incredible ML capabilities, but if your organisation doesn't have a data science team and isn't planning to build one, you're paying for features that sit idle. Be honest about what you'll actually use in the next 12-18 months.
Ignoring the partner ecosystem. In Australia, the pool of experienced consultants varies by platform. Microsoft Fabric and Power BI consultants are the most abundant. Snowflake specialists are growing but still fewer in number. Databricks experts are the scarcest. Factor in your ability to hire or engage external help.
Letting one engineer's preference drive the decision. Platform choice is a business decision, not a technical one. We've seen cases where a senior engineer pushed for Databricks because they loved Spark, but the rest of the 10-person analytics team was SQL-based. The result was a platform that one person could use effectively and nine people struggled with.
Our Recommendation for Most Australian Mid-Market Companies
If you're a mid-market Australian company (500-5,000 employees) with a Microsoft-centric IT environment, Microsoft Fabric is the safest and most cost-effective choice. It covers the broadest range of use cases in a single platform, integrates with tools your team already knows, and the pricing is predictable.
If you're a larger enterprise with specific requirements around multi-cloud, data sharing, or advanced ML, Snowflake or Databricks may be the better fit - but go in with clear eyes about the additional integration and tooling costs.
How Team 400 Can Help
We've helped organisations across Australia evaluate, select, and implement data platforms. Our Microsoft Fabric consulting practice is the most established, but we work across all three platforms and can give you an honest, vendor-neutral assessment.
A typical engagement starts with a two-week assessment where we:
- Audit your current data stack and costs
- Map your requirements to platform capabilities
- Run a proof of concept on the shortlisted platform(s)
- Deliver a recommendation with cost projections
If you're in the middle of a platform decision and want an experienced second opinion, reach out to us. We'll help you make the right call for your organisation, not just the most technically interesting one.
You can also learn more about our data and analytics services or explore our work with Power BI and Azure Data Factory.