Data Warehouse Selection Tool

Unified Microsoft ecosystem and the fastest path to Power BI executive reporting, with governance and Copilot built in.

Best when you are Microsoft-first. Less suited to heavy custom ML on non-Microsoft stacks.

Rock-solid SQL warehouse with strong data sharing, multi-cloud portability, and near zero-admin overhead.

Credit-based consumption. Compute is up roughly 20-30% since 2023; watch usage, but little to tune operationally.

Consumption can creep without monitoring, and heavy AI/ML usually pairs it with a second engine.

Lakehouse plus AI/ML, unstructured and real-time data at scale, and 20-40% cheaper large-scale ETL.

Consumption and serverless. Potentially the lowest compute, but only with skilled platform engineers tuning clusters.

Needs Python and engineering depth; serverless query costs can spike without continuous optimization.

It scores Microsoft Fabric, Snowflake, and Databricks against your ecosystem, primary workload, team skills, cost priority, and data types, using 2026 positioning. The platform that fits the most of your answers ranks first, and you see the fit percentage and rationale for all three.

No. Thinklytics is vendor-neutral and implements all three. The scoring reflects where each platform is strongest: Fabric for Microsoft-first BI, Snowflake for SQL warehousing and sharing, Databricks for AI/ML and unstructured data at scale.

We flag it when the leaders are within a couple of points, because that usually means either could work and the decision should come down to your team and total cost of ownership. Many enterprises run two by design, for example Snowflake for governed SQL and Databricks for ML.

At a directional level. Fabric is capacity-based and predictable, Snowflake is credit-based consumption, and Databricks can be the cheapest compute but needs engineering to tune. Exact cost depends on your workloads, which is what a proper assessment models.

No. Your inputs are used only to generate your recommendation and are stored in our own systems so we can follow up if you ask. We never sell or share them.

A ranked recommendation with fit percentages for all three platforms, the rationale, pricing notes, and watch-outs, a downloadable PDF, and a copy emailed to you.

Many enterprises run two platforms by design, for example Snowflake for governed SQL analytics and Databricks for ML, with Fabric for Power BI. The decision should come down to your team and total cost of ownership, not vendor marketing.

Thinklytics is vendor-neutral and implements all three. A free 30-day Analytics Truth Audit pressure-tests this against your real workloads and total cost of ownership before you commit.

We will validate the shortlist against our real workloads before committing budget.

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Tell us your ecosystem, workloads, team, and cost priority. We score all three on 2026 positioning and give you a ranked recommendation with the rationale, pricing, and the watch-outs a vendor demo will not mention. Vendor-neutral.

Many enterprises run two platforms by design, for example Snowflake for governed SQL analytics and Databricks for ML, with Fabric for Power BI. The right call comes down to your team, your workloads, and total cost of ownership, not vendor marketing. This tool gets you to a confident shortlist; a proper assessment proves it on your real workloads.

Thinklytics is vendor-neutral and implements all three. The free 30-day Analytics Truth Audit pressure-tests this recommendation against your real workloads and total cost of ownership before you sign anything.

Directional recommendation based on 2026 platform positioning and pricing. Fit scores reflect ecosystem, workload, skills, cost priority, and data types. Not a substitute for a workload-level total-cost-of-ownership assessment.