Comparisons

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:

Function	CryspIQ	Kimball	Inmom	Data Lake	Data Vault
Data Modelling Skills	No	Yes	Yes	Yes	Yes
Single Source of Truth across Functions	Yes	No	Yes	No	No
Technical Dependency	Low	High	High	Low	High
Source Flexibility	Flexible	Inflexible	Inflexible	Flexible	Flexible
Data Quality	Measured	Limited	Measured	Limited	Limited
Upfront Effort	Low	Low	High	Low	Medium
Speed to Availability	Fast	Fast	Slow	Fast	Fast
End User Training	Yes	Yes	Yes	No	Yes
User Self Service	Yes	No	No	Yes	No
Change Request Impact	Low	Medium	High	Low	High
Lineage & Traceability	Automatic	Manual	Manual	Manual	Manual
Data Consistency	Consistent	Compromised	Consistent	Compromised	Consistent
Platform Scalability	Scalable	Limited	Limited	Limited	Scalable
Ongoing Support and Maintenance Costs	Low	High	High	High	High
Enables Automation	Yes	No	No	No	Yes

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:

Function	CryspIQ	Snowflake	Redshift	Synapse	Big Query
Data Collection Model	Static	Subjective	Subjective	Subjective	Subjective
Factual Data or All Data*	Factual	All Data	All Data	All Data	All Data
Data Footprint	Small	Large	Large	Large	Large
Multi-Cloud	Yes	Yes	No	No	No
Separate Storage and Compute	Yes	Yes	No	Yes	Yes
Query Language	SQL	Snowflake SQL	Amazon SQL	TSQl	SQL
Massively Parallel Processing (MPP)	Yes	Yes	Yes	Yes	Yes
Columnar	Yes	Yes	Yes	Yes	Yes
Foreign Keys	Yes	Yes	Yes	Yes	Yes
Structured Data	Yes	Yes	Yes	Yes	Yes
Unstructured Data	Yes	Yes	No	No	No
Concurrency	Yes	Yes	Yes	Yes	Yes
Automation	Yes	No	No	No	No

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: