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To compare CryspIQ to other industry methods and market products, it is necessary look at the Method, Data Model and Solution separately. These comparisons are provided below.

Compare Industry Methods

The Industry Data Warehousing Methods are described below:

  1. Bill Inmom - Method is the top-down or data-driven strategy, in which we start with the data warehouse and break it down into data marts.
  2. Ralph Kimball - Method is the bottom-up approach where data marts are first created to provide reporting and analytical capabilities for a function.
  3. Data Lake - Method is storing data within a system or repository, in its natural format, that facilitates the collation of data in object blobs or files.
  4. Data Vault 2.0 - Method is designed to provide long-term historical storage of data coming in from multiple operational systems.
  5. CryspIQ - Method is the decomposition of source records to allow one to store the incoming data at the granular level clustered with data of like type.

To compare CryspIQ against other methodologies, please see table below:

FunctionCryspIQKimballInmomData LakeData Vault
Data Modelling SkillsNoYesYesYesYes
Single Source of Truth across FunctionsYesNoYesNoNo
Technical DependencyLowHighHighLowHigh
Source FlexibilityFlexibleInflexibleInflexibleFlexibleFlexible
Data QualityMeasuredLimitedMeasuredLimitedLimited
Upfront EffortLowLowHighLowMedium
Speed to AvailabilityFastFastSlowFastFast
End User TrainingYesYesYesNoYes
User Self ServiceYesNoNoYesNo
Change Request ImpactLowMediumHighLowHigh
Lineage & TraceabilityAutomaticManualManualManualManual
Data ConsistencyConsistentCompromisedConsistentCompromisedConsistent
Platform ScalabilityScalableLimitedLimitedLimitedScalable
Ongoing Support and Maintenance CostsLowHighHighHighHigh
Enables AutomationYesNoNoNoYes

Compare Cloud Data Warehouse Products

CryspIQ is Cloud Data Warehouse which includes an Enterprise Data Model, thus a comparison against the most common Cloud Data Warehouses in the market has been prepared with the key differences. These are shown the table below:

FunctionCryspIQSnowflakeRedshiftSynapseBig Query
Data Collection ModelStaticSubjectiveSubjectiveSubjectiveSubjective
Factual Data or All Data*FactualAll DataAll DataAll DataAll Data
Data FootprintSmallLargeLargeLargeLarge
Separate Storage and ComputeYesYesNoYesYes
Query LanguageSQLSnowflake SQLAmazon SQLTSQlSQL
Massively Parallel Processing (MPP)YesYesYesYesYes
Foreign KeysYesYesYesYesYes
Structured DataYesYesYesYesYes
Unstructured DataYesYesNoNoNo

Please note: *This may feel like you are missing Data because you are only ingesting the business critical or factual data. However any missing data is a "mapping" away from being landed in CryspIQ. Thus, you are not actually missing any Data, but rather selecting (sometimes known as business critical data) what's important to your business.

Compare End to End Solutions

CryspIQ Data Warehouse Solution collapses the layers to measure quality at the point of entry and reduce failure points in the processing and management of Data. This is demonstrated in the diagram below:


Standard Data Warehouse Solutions usually consist of a number of different layers usually built with different technology toolsets. This is shown the diagram below: