There is no single right way to run Snowflake and Microsoft Fabric together. Enterprises typically land on one of four paths: keep Snowflake as the primary platform and add Fabric for BI and AI consumption, share data through open Iceberg tables to cut storage costs, coexist through Fabric Mirroring while Snowflake runs core workloads, or phase a full migration to Fabric over 12 to 18 months. The right path depends on four questions: whether the ROI of migration is real, whether both platforms are actually needed, what a phased transition looks like, and how governance holds up across two systems.
Enterprise leaders evaluating Snowflake and Microsoft Fabric are not just choosing a platform. They are weighing licensing costs against migration risk, deciding whether governance can hold up across two systems, and figuring out which platform actually fits their existing team and Microsoft investment.
Get the decision wrong and the cost shows up fast: duplicate infrastructure, conflicting reports, and a compliance team stuck reconciling two audit logs. Get it right, and Snowflake and Microsoft Fabric work as a single, governed data layer instead of two competing platforms.
In our latest webinar , Kanerika’s experts, Amit Chandak, Chief Analytics Officer and Microsoft MVP, and Shaurya Singh Chauhan, Lead Snowflake Engineer, walked enterprise data leaders through the decision framework behind that choice. This article breaks down the four decision questions, the four practical paths enterprises are choosing between, and a real deployment that cut reporting costs by 40%.
Key Takeaways There’s no single right answer, enterprises land on one of four paths: keep Snowflake primary, share via Iceberg, coexist via Mirroring, or phase migration to Fabric. Four questions decide the path: is the ROI real, do you need both platforms, what does a phased transition look like, and does governance hold up across two systems. Native integration (Mirroring, Iceberg tables) already solves data movement, licensing, access control, and team training still need a deliberate decision.A Kanerika-led Fabric Mirroring deployment cut a client’s reporting costs by 40% and dashboard refresh time by 65%, with zero duplicate data. Most Snowflake-Fabric mistakes happen when teams treat the technical integration as done instead of the start of an operating model.
The Snowflake and Microsoft Fabric Question Every Data Leader Is Asking “Now that Fabric is here, what do we do with Snowflake?” That was the question Amit opened with, and he called it the single most common question Kanerika hears from data leaders right now.
The honest answer he gave the audience: there is no universal answer. Enterprises that built their reporting and governance around Snowflake for years are not going to rip it out because a new Microsoft product launched. At the same time, organizations already deep in the Microsoft ecosystem have real reasons to bring workloads into Fabric.
Shaurya added the practical framing that shaped the rest of the session. The question is not which platform wins in the abstract. It is what the right move looks like for a specific organization’s stack, team, and budget.
Deciding Between Snowflake, Fabric, or Both? Get a workload-level assessment of your Snowflake estate and a clear recommendation on which of the four paths fits your stack, team, and budget.
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The Multi-Platform Reality Behind the Snowflake and Fabric Question The webinar opened with the data Kanerika’s team uses to explain why this question keeps coming up in 2026. Microsoft Fabric has crossed 35,000 paid customers. Snowflake remains embedded across the enterprise market, with Kanerika’s research placing the platform in use at more than 790 Fortune 2000 companies.
Metric Figure Microsoft Fabric paid customers 35,000+ Fortune 2000 companies using Snowflake 790+ Enterprises running hybrid or multi-cloud data environments today 70% Enterprises expected to have a formal multi-cloud data strategy by 2026 75%
Neither number is shrinking. Enterprises with Microsoft-centric stacks are consolidating workloads around Fabric to cut licensing overhead and simplify their BI layer. At the same time, Snowflake investment keeps growing among organizations that value its multi-cloud portability and mature governance model, a shift consistent with Gartner’s broader research on why enterprises adopt multi-cloud strategies .
The practical result, as Amit put it to the audience, is that many enterprises no longer need to pick a winner. They need both platforms to work together instead of competing for budget and attention.
Key Decision Questions That Matter Rather than framing the session as Snowflake versus Fabric, Kanerika’s experts walked through four decision lenses that map to how enterprise data teams actually make this call.
Migrate. Is the ROI of a Fabric migration real, or is the cost of the move bigger than what the pitch promised? This question forces a hard look at licensing savings against migration effort and retraining cost.
Coexist. Does the organization actually need both platforms running in production, or is it paying to maintain duplicate infrastructure for the same data? Coexistence only makes sense when each platform is doing distinct work.
Phase it. What does a 12 to 18 month transition actually look like in practice, workload by workload, rather than as a single cutover event?
Govern it. How do compliance and audit trails hold up when data lives across two platforms with two different access control models?
These four questions, more than any feature comparison, are what Kanerika’s team uses to guide enterprise clients through the decision. A platform comparison tells a team what each system can do. These four questions tell them what they should actually do next.
How Snowflake and Fabric Already Share Data with Each Other Before getting into strategy, Shaurya walked attendees through the two ways Snowflake and Fabric now share data natively, without a custom ETL layer in between.
Fabric Mirroring replicates Snowflake data into OneLake in near real time. There are no pipelines to build or maintain, and the mirrored data stays current automatically. It works best for teams that need live Snowflake data available inside Fabric workloads such as Power BI reporting.
Apache Iceberg tables give both enginesIceberg tables guide .
Both mechanisms matter, and Kanerika has published a dedicated technical walkthrough of how Mirroring and Iceberg tables compare architecture by architecture, including latency, governance, and cost tradeoffs, in Enterprise Data Sharing: Snowflake to Microsoft Fabric . This webinar recap focuses on the decision layer above that mechanics, which is where most enterprise leaders actually get stuck.
What Native Snowflake and Fabric Integration Solved, and What Still Needs a Decision The clearest insight from the session was this: native integration solved the data movement problem. It did not solve the platform decision.
What Mirroring, Iceberg tables, and OneLake shortcuts made possible in production:
A single copy of data accessible from both platforms No custom ETL pipelines required between Snowflake and Fabric Open formats across the stack, including Parquet, Delta, and Iceberg What still requires a deliberate decision from leadership:
Two cost models running in parallel: Snowflake compute credits versus Fabric capacity licensing Two access control frameworks: Snowflake RBAC versus Microsoft Purview Two audit logs feeding one compliance team, which has to reconcile both Two platforms supported by one team that may only be trained on one of them That last point came up repeatedly in the Q&A. Enterprises that skip governance and training planning end up with a technically working integration and an operationally confused team.
Four Practical Snowflake and Microsoft Fabric Paths Enterprises Are Taking Kanerika’s engineers see enterprise clients sort into one of four paths once the four decision lenses have been worked through.
Keep Snowflake primary. Fabric handles BI, AI, and Microsoft ecosystem workloads while the Snowflake estate stays unchanged. This suits teams where Snowflake is stable and Power BI is already the primary reporting tool.Share through Iceberg tables. Both platforms read from a shared open table layer, cutting storage duplication without requiring a full migration.Coexist through Mirroring. Snowflake continues running core workloads while Fabric consumes replicated data for reporting and AI use cases .Phase migration to Fabric. Workloads move to Fabric in stages, sequenced by cost, usage patterns, and risk tolerance, typically over 12 to 18 months.Enterprise Decision Matrix
Organization’s Situation Recommended Path Snowflake is stable and Power BI is the primary BI tool Keep Snowflake primary, add Fabric for consumption Need Fabric access fast without disrupting Snowflake Coexist through Fabric Mirroring Very large datasets, shared storage already on ADLS Share through Iceberg tables Microsoft stack dominant and licensing cost is a pressure point Phased migration to Fabric
None of these paths is inherently superior. The right one depends on what the audience polls during the webinar confirmed in real time. Attendees were split fairly evenly between running both platforms, actively planning a Fabric migration, and still evaluating where they stand, which mirrors what Kanerika sees across its own client base. Enterprises considering the phased path can see how a comparable migration plays out cost-wise in Kanerika’s breakdown of Snowflake to Microsoft Fabric migration costs .
Common Snowflake and Microsoft Fabric Mistakes Enterprises Make Shaurya closed the technical portion of the session with a list of mistakes that come up repeatedly in Snowflake-Fabric engagements, regardless of which of the four paths a client eventually chooses.
Mirroring every table without modeling the cost impact first Adopting Iceberg tables before the team is ready to own shared storage operationally Treating Power BI as only a reporting layer instead of part of the architecture decision Skipping semantic model cleanup before connecting new data sources Moving workloads to Fabric without analyzing actual usage patterns first Missing a rollback plan before starting a phased migration
Most of these mistakes share a root cause. Teams treat the technical integration as the finish line instead of the starting point of an operating model that has to be governed, staffed, and cost-managed over time.
Real-Time Insights Across Distributed Operations With Snowflake Migration See how Kanerika helped another enterprise client modernize its Snowflake environment to support real-time reporting across distributed operations
Read full case study
Snowflake-Fabric Strategy: How Kanerika Helps Enterprises Decide Kanerika’s role in these engagements starts before any migration or mirroring configuration begins. The team assesses the current Snowflake estate, maps workloads by cost, risk, and business value, and identifies what should be kept, shared, phased, or migrated. Governance and ownership get defined early, followed by a pilot on real workloads before scaling the chosen model across the organization.
Case Study: A Global Consumer Goods Provider Cuts Reporting Costs by 40% With Fabric Mirroring One deployment from the webinar shows what this framework looks like once it reaches production. A global consumer goods provider brought Kanerika in to fix a reporting setup that had broken down under manual effort.
The Challenge Manual exports between Snowflake and Fabric had created duplicate, outdated report copies, and no two teams were working from the same numbers. Full-table refreshes across both platforms were driving up pipeline costs and slowing dashboard updates, while the lack of a governed shared layer had blurred which copy of the data was actually current.
The Solution Kanerika configured Fabric Mirroring across more than 60 Snowflake tables, landing data continuously into OneLake without manual exports. DirectLake dashboards were then built directly on the mirrored data, removing import refreshes entirely and giving every team the same live source. The full setup, from assessment to rollout, shipped in under three weeks.
The Results Metric Result Reporting costs 40% lower Duplicate data copies 0 Dashboard refresh cycles 65% faster Implementation timeline Under 3 weeks
The result is a single, governed version of the data that both platforms read from, which is the exact outcome the four decision lenses are meant to drive toward.
Kanerika’s Snowflake-Fabric implementation support covers migration planning, Fabric Mirroring setup, Iceberg data sharing, Power BI DirectLake implementation , Microsoft Purview governance , cost modeling, and validation through rollout. The team works across both the Snowflake and Microsoft Fabric technology stacks as a Snowflake Select Tier Partner and Microsoft Fabric Featured Partner.
Deciding Between Snowflake, Fabric, or Both? Get a workload-level assessment of your Snowflake estate and a clear recommendation on which of the four paths fits your stack, team, and budget.
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Wrapping Up The Snowflake and Fabric question is not going away, and it does not have a single correct answer. What Kanerika’s webinar made clear is that the decision comes down to four practical questions about ROI, duplication, transition timeline, and governance, not a feature-by-feature platform comparison.
Enterprises that treat native integration as the finish line tend to end up with a working pipeline and an unclear operating model. The ones that succeed treat the technical connection as step one, then build the cost model, governance structure, and team plan around it before scaling.
FAQs Can Snowflake and Fabric work together without a full migration? Yes. Fabric Mirroring and Apache Iceberg tables let both platforms read and share data natively, without building custom ETL pipelines between them. Many enterprises choose to run both platforms long term instead of migrating fully, using Microsoft Fabric for BI and AI consumption while Snowflake continues handling core data workloads, governance, and existing reporting infrastructure that was already built around it.
What is Fabric Mirroring used for? Fabric Mirroring replicates Snowflake data into Microsoft OneLake in near real time, without requiring manual pipelines or scheduled exports. It is best suited for teams that need current Snowflake data available inside Fabric workloads, such as Power BI reporting built on DirectLake, where dashboards read directly from the mirrored data instead of importing and refreshing separate copies.
Do we need to migrate off Snowflake if we adopt Microsoft Fabric? Not necessarily. Many organizations keep Snowflake as their primary data platform and use Fabric selectively for BI, AI, and Microsoft ecosystem workloads. A full migration only makes sense when the licensing savings from Fabric clearly outweigh the cost, effort, and risk of moving core workloads, so most enterprises start with coexistence before deciding on a longer-term direction.
How long does a phased migration from Snowflake to Fabric typically take? A phased migration typically takes 12 to 18 months, with workloads moved to Fabric in stages based on cost, usage, and risk rather than a single cutover event. Actual timelines vary depending on the size of the Snowflake estate, the number of dependent reports, and how much governance and access control work needs to happen alongside the technical move.
What is the biggest governance challenge when running Snowflake and Fabric together? The biggest challenge is reconciling two separate access control models, Snowflake RBAC and Microsoft Purview, under a single compliance team. Enterprises that skip this step often end up with two audit logs that do not tell a consistent story about who accessed what data, which creates real problems during audits, security reviews, or regulatory reporting cycles.
Is Apache Iceberg or Fabric Mirroring the better choice for data sharing? It depends on the goal. Iceberg tables reduce storage duplication by having both platforms read the same open format files, which suits teams focused on cutting storage costs across a shared lakehouse. Mirroring is the better fit for teams that need Snowflake data reflected inside Fabric in near real time, without taking on the operational work of managing shared storage.
What mistakes do enterprises most often make when connecting Snowflake and Fabric? The most common mistakes include mirroring every table without modeling the cost impact first, adopting Iceberg tables before the team is ready to operationally own shared storage, and moving workloads to Fabric without analyzing actual usage patterns. Most of these mistakes share the same root cause, treating the technical integration as finished instead of the starting point of an ongoing operating model.
How does Kanerika help enterprises decide between Snowflake, Fabric, or both? Kanerika assesses the existing Snowflake estate, maps workloads by cost, risk, and business value, and defines governance and ownership before recommending a path forward. The team then runs a pilot on real workloads before scaling the chosen model across the organization, covering everything from Mirroring setup and Iceberg configuration to Purview governance, cost modeling, and validation through rollout.
