vs Snowflake
CryspIQ® is not an alternative to Snowflake — it supplies the enterprise model Snowflake expects you to build yourself. What Snowflake supplies, what it expects you to build yourself, and which of those things CryspIQ® provides as part of the model.
vs Databricks
CryspIQ® is not an alternative to Databricks — it supplies the enterprise model the lakehouse expects you to build. Where the medallion architecture puts business meaning, why that meaning gets re-added per consumer, and what CryspIQ® changes about it.
vs Microsoft Fabric
CryspIQ® is not an alternative to Microsoft Fabric — it supplies the enterprise model Fabric expects you to build in the gold layer, Power BI semantic models and Fabric IQ ontologies. Where Fabric puts business meaning, why OneLake solves the copy problem and not the meaning problem, and how the two fit together.
vs Kimball
How CryspIQ® differs from Kimball dimensional modelling and star schemas — what each one fixes, what each leaves open, and when a purpose-built star schema is still the right answer.
vs Inmon
How CryspIQ® differs from the Inmon top-down enterprise data warehouse — the two agree on a single governed enterprise model and differ on who builds it, how long it takes and what a change costs.
vs Data Vault
How CryspIQ® differs from Data Vault 2.0 — Data Vault solves auditable loading and history and still needs a downstream model to answer questions, where the CryspIQ® decomposed store is itself the consumption model.
vs Data Lake
How CryspIQ® differs from a data lake, lakehouse or medallion architecture — a lake is a storage decision that preserves source structure, where CryspIQ® is a modelling decision that deliberately discards it.
vs a Semantic Layer
A semantic layer and CryspIQ® make the same promise — define once, reuse everywhere — and differ on when meaning is applied. One translates at query time; the other stores meaning with the data.
vs MDM
MDM decides which record is the master. CryspIQ® encourages every other source to align to the identified master data source — a business key made of fields every source should hold, data quality checks on those fields, and a Parking Lot that shows the data steward exactly which source records to fix — and holds the transactions in the same model.
Alternatives to MDM
What to do when an MDM programme is stalling — narrow the scope, fix the sources, add a semantic layer, adopt a governed enterprise model that encourages every source to align to the master, or commit properly. Which one depends on whether you have duplicate records, conflicting definitions, or systems that won't line up with the master.
vs Data Virtualisation
Data virtualisation and query federation ask "how do I get to my data faster?" CryspIQ® asks "can I trust the answer?" One leaves data where it is and asks each system every time; the other keeps one trusted copy of only the data that matters, checked once and stored with its business context.
vs a Data Fabric
A data fabric asks "how do I connect all my data without moving it?" CryspIQ® asks "can I trust the answer?" What a data fabric usually means, the other way the term is used, and how it differs from Microsoft Fabric the product.