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CryspIQ® vs Microsoft Fabric

CryspIQ® supplies what Microsoft Fabric expects you to build, rather than replacing Fabric. Fabric is a unified SaaS analytics platform: OneLake for storage, Data Factory for ingestion, Synapse workloads for engineering and warehousing, Real-Time Intelligence, data science and Power BI, on one capacity and one bill. CryspIQ® is the enterprise model those workloads are meant to be pointed at. The question is not which to buy — it is where business meaning gets attached, and how many times.

A note on the name​

Microsoft Fabric is a product. A data fabric is an architectural pattern, and the phrase predates the product by years — it is a G2 software category with more than eighty listings in it.

CryspIQ® describes itself as a data fabric in the pattern sense: one governed model over data from many sources, independent of where each source came from. That is not a claim to be Microsoft Fabric, or an alternative to it. Both things being called fabric is unhelpful, and worth saying plainly rather than leaving a reader to work out.

What Fabric gives you​

A genuinely unified estate. One tenant-wide lake in OneLake with a single physical copy and shortcuts instead of duplicates, ingestion and orchestration, warehouse and lakehouse compute over the same Delta tables, real-time streams, notebooks, and Power BI sitting directly on top through Direct Lake. Purview adds cataloguing and lineage across Fabric and beyond it, and everything inherits Entra identity.

For an organisation already committed to Microsoft, the integration is the product, and CryspIQ® depends on infrastructure like this rather than reproducing it.

Where Fabric puts meaning​

In two places, which is the part worth examining.

The gold layer. Fabric's recommended pattern is medallion: bronze as landed, silver cleaned and conformed, gold business-ready for a particular audience. Meaning accumulates in the pipeline rather than in the data, so it is applied per gold table, by whichever team built it.

Power BI semantic models. These are Fabric's answer to business definitions, and a good one — measures and relationships defined once and inherited by every report that connects through the model.

The difficulty is that these are two answers to the same question, and neither is authoritative over the other. A measure defined in a semantic model governs the reports using that model. A notebook, an ML job, a Real-Time Intelligence query or a second workspace's model reading the same Delta tables in OneLake gets the tables, not the measure — and defines revenue again, its own way.

OneLake solves the copy problem, not the meaning problem. One physical copy of the data is a real advance over the several most organisations hold. It says nothing about how many definitions of customer sit on top of that single copy, and in practice the number rises with each workspace.

What CryspIQ® changes​

CryspIQ® attaches meaning at entry. The fact type carries the business definition and the security classification, and quality is measured as data loads. Once meaning is a property of the stored data, the layers whose job was to add it later have nothing left to do — and there is no bypass route, because a consumer reading the data reads the definitions with it.

On FabricWith CryspIQ®
Where business meaning livesGold tables and Power BI semantic modelsOn the fact type, once
Consumers outside the semantic modelGet Delta tables and their own interpretationInherit the same definitions
Enterprise data modelYou design and build itPre-defined, patented, ready on day one
Master dataYour approach to maintainA single instance per party, enforced structurally
Data qualityApplied per pipeline, downstreamAssessed as data loads, scored organisation-wide
LineageReconstructed via Purview and pipeline metadataAutomatic, through the link key
A new business questionUsually needs a gold table or model change firstNo modelling step between question and answer

Where Fabric is stronger​

Everything CryspIQ® does not attempt, and the list is long: real-time streaming analytics, data science and model training, unstructured data at volume, arbitrary transformation, and exploratory work where the shape of the answer is not known in advance.

It is also far less invasive. Fabric requires no mapping exercise — you point it at your sources and you are running. And if your organisation lives in Power BI and Microsoft 365 already, the adoption cost for report consumers is close to zero, which is a real advantage and should not be talked past.

There is a genuine trade in flexibility too. Fabric will model anything you can write code for. CryspIQ® asks your data to fit seven fact types and a fixed set of dimensions — fixed at the level where organisations are alike, open at the level where they differ.

How they fit together​

CryspIQ® maps from the bronze or silver layer already landing in OneLake into standardised business entities, independent of how each source produced them. Fabric keeps ingesting, keeps serving real-time and data science workloads, and keeps Power BI as the presentation surface.

The useful arrangement is usually that Power BI semantic models stop being the place definitions are decided and become the place they are presented — sourced from one governed model instead of maintained per workspace. Where a semantic model is already embedded in hundreds of reports, that is the sensible order of events. See CryspIQ® vs a semantic layer for the underlying distinction, and co-existence for how adoption sequences without a migration.