Most organisations running Snowflake and Power BI are not looking to replace either. Then Microsoft Fabric shows up on the roadmap, and suddenly there is a question nobody quite knows how to answer: does Fabric replace what is already there, or sit alongside it?

The instinct is to treat it as a replacement question. It usually is not. In our data engineering consulting work across Australia, all three platforms can work well together, provided you know what to ask each one to do.

What each platform is actually good at

These three platforms get conflated constantly. They should not be, they do genuinely different things.

Snowflake is the warehouse of record. Built for scale, SQL-heavy analytics, and running multiple teams against the same data without resource contention. Its data sharing capabilities, live data across organisations without moving files, are hard to replicate. Snowflake Cortex AI also brings LLM-powered analytics directly to governed data, useful for teams starting to build AI on top of their warehouse.

Microsoft Fabric is built around unification. Its central storage layer, OneLake, means data engineering, warehousing, real-time analytics, and Power BI reporting all read from and write to the same place, no copying, no syncing, no pipeline spaghetti. Copilot is also built in, and since Microsoft dropped the F64 requirement in April 2025, it is accessible on any paid Fabric capacity from F2 upward. For organisations already deep in Azure, the integration story is compelling: one billing surface, Purview for governance, and a natural extension of the Microsoft stack.

Power BI is where business users actually live. It connects directly to both Snowflake and Fabric, and in most cases it should stay exactly where it is.

The mistake most teams make

Fabric and Snowflake can both store data, run transformations, and support analytics workloads. Without clear boundaries, they start doing the same job, and the business ends up paying for duplication and managing two governance models instead of one.

The boundary conversation almost never happens upfront. Teams build what is convenient in the moment, and six months later they are maintaining two versions of the same pipeline with nobody sure which one is right. Once roles are defined clearly (Snowflake for scale, Fabric for unification, Power BI for accessibility), the stack becomes faster, cheaper, and far more effective.

Snowflake and Fabric are now designed to work together

As of February 2026, OneLake and Snowflake interoperability is generally available. Snowflake-managed Iceberg tables can be stored natively in OneLake, and Fabric data is automatically converted to Iceberg format for direct access from Snowflake, with no data movement required. Both sides read from the same copy, built on open standards.

The duplication problem is increasingly being solved at the platform level. Native interoperability does not tell you which platform should own what, though. That is still a decision a team needs to make deliberately, and it is the one that gets skipped most often.

How to actually make them work together

Define who owns what before you build anything. Snowflake holds governed, analytics-ready data as the system of truth. Fabric handles orchestration, pipelines, and lakehouse workloads. Power BI connects to whichever platform holds what it needs. We always push clients to write this down before anyone touches a pipeline. It is a five-minute conversation that saves weeks of untangling later.

Don't route everything through Fabric

A direct Power BI to Snowflake connection is often the right call. Adding Fabric as an intermediate layer introduces complexity that is not always justified, let the data take the shortest path that still meets governance requirements.

Align governance across all three

Access controls and data lineage need to work consistently across platforms, not just within each one. Purview handles a lot of this for Fabric and Power BI. The Snowflake side takes more deliberate effort but is achievable, and worth doing properly rather than patching it later.

Model your Fabric capacity before you scale

The CU billing structure is not intuitive. Workloads that looked fine in dev can start chewing through capacity in production in ways nobody had modelled. Sort this before committing production pipelines.

Audit for duplication regularly

A quarterly check of the pipeline inventory (what is running, where it lives, who owns it) costs almost nothing. On a recent build, the Microsoft Fabric implementation itself was the smooth part. The real time sink was access provisioning: getting service accounts set up across source systems meant waiting on other teams for weeks. Not a Fabric problem, but a project reality worth building into the timeline.

When it works, it really works

The stacks that perform well are not the ones with the fewest tools. They are the ones where someone made a deliberate call about what each tool is for, and stuck to it. Clear roles, clean boundaries, and every platform earning its place.

Frequently asked questions

Do I need to choose between Microsoft Fabric and Snowflake?

In a greenfield world, you would probably choose one. In the real world, you might inherit both, and they can work well together if you are deliberate about it. With native OneLake and Snowflake interoperability now generally available, they are increasingly designed to. The key is giving each a distinct role rather than letting them drift into doing the same job.

Can Power BI connect directly to Snowflake?

Yes, and it can be the right call. If the data is already analytics-ready in Snowflake, a direct connection avoids unnecessary duplication, and Power BI's native Snowflake connector is mature enough to handle it. Route through Fabric when you need to blend sources or manage lineage centrally.

What's the biggest risk of running all three platforms together?

Duplication. If the same dataset lives in multiple places without clear ownership, costs rise, governance gets messy, and data teams spend time reconciling versions instead of building. Define boundaries early and revisit them regularly.

How does Microsoft Fabric implementation work if we're already on Snowflake?

No migration needed. Introduce Fabric for net-new data engineering or lakehouse workloads while Snowflake continues to serve the governed analytics layer. The two platforms now share data natively via Iceberg, so the transition does not have to be all-or-nothing.

Is this relevant for Australian businesses specifically?

The architecture principles are universal, but context matters. Finance, retail, health, and infrastructure organisations in Australia face specific regulatory and data sovereignty requirements that shape how governance is designed across these platforms. It is worth working with a team that has navigated those constraints locally.

Not sure where Fabric fits in your existing stack

The Omnia Collective works with finance, retail, property and health organisations across Australia as certified partners across both Microsoft and Snowflake, helping teams work out where each platform earns its place before anything gets built. See our data engineering services or get in touch.