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 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 Databricks
CryspIQ® is not an alternative to Databricks — it runs on top of the lakehouse. Where the medallion architecture puts business meaning, why that meaning gets re-added per consumer, and what CryspIQ® changes about it.
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 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 Snowflake
CryspIQ® is not an alternative to Snowflake — it runs on top of it. What Snowflake supplies, what it expects you to build yourself, and which of those things CryspIQ® provides as part of the model.