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CHAPTER 1-Caboodle Console - The Caboodle Console is a web application housed
on the Caboodle server. It includes the following:
Dictionary
Dictionary Editor
Executions
Work Queue
Configuration
CHAPTER 1-Data Warehouse - In a data warehouse, multiple sources may load data
pertaining to a single entity. This means that more than one package may populate a
given row in a Caboodle table. As a result, there may be multiple business key values
associated with a single entity in a Caboodle table.
CHAPTER 1-ETL - Extract, Transform, Load
CHAPTER 1-SSIS Package - The architecture of Caboodle includes a staging database
and a reporting database. Data is extracted from source systems (like Clarity),
transformed in the staging database, and presented for users in the reporting database.
This movement of data is realized via a set of SQL Server Integration Services (SSIS)
packages.
CHAPTER 1-Data Lineage - Generally, data lineage refers to the process of identifying
the source of a specific piece of information. In Caboodle, data lineage is defined at the
package level.
CHAPTER 1-Star Schema - The standard schema for a dimensional data model. The
name refers to the image of a fact table surrounded by many linked dimension tables,
which loosely resembles a star.
The Caboodle data model structure is based on a "star schema" ‐ where one central
fact table will join to many associated lookup or dimension tables. This structure
provides the foundation of the Caboodle data model.
CHAPTER 1-DMC - DATA MODEL COMPONENT
No table in Caboodle "stands alone." Each is considered part of a Data Model
Component, which refers to the collection of metadata tables that support the ETL
process and reporting views stored in the FullAccess schema.
Each DMC gets a type. Strict table naming conventions are followed in Caboodle, so
that a table's suffix provides information about its structure and purpose.
These suffixes are:
· Dim for dimensions (e.g. PatientDim)

· Fact for facts (e.g. EncounterFact)
· Bridge for bridges (e.g. DiagnosisBridge)
· DataMart for data marts (e.g. HospitalReadmissionDataMart)
· AttributeValueDim for EAV tables (e.g. PatientAttributeValueDim)
· X for custom tables (e.g. CustomFactX)
CHAPTER 1-Staging Database - The Caboodle database into which records are loaded
by SSIS packages and stored procedures.
CHAPTER 1-Reporting Database - The architecture of Caboodle includes a staging
database and a reporting database. Data is extracted from source systems (like Clarity),
transformed in the staging database, and presented for users in the reporting database.
This movement of data is realized via a set of SQL Server Integration Services (SSIS)
packages.
CHAPTER 1-Dbo Schema - STAGING DATABASE
Import tables and Mapping tables live here. This is
primarily used by administrators for moving data into Caboodle.
REPORTING DATABASE
The dbo schema stores reporting data and acts as the
data source for SlicerDicer. The Caboodle Dictionary reflects the contents of the dbo
schema.
CHAPTER 1-FullAccess Schema - STAGING DATABASE
The FullAccess schema does not exist on the Staging database.
REPORTING DATABASE
The FullAccess schema houses views that simplify reporting. FullAccess should be your
default schema when reporting.
CHAPTER 1-Identify key characteristics of the dimensional data model - MADE for
report writers.
· Simpler and more intuitive.
· Easily extensible.
· More performant..
CHAPTER 1-Identify the benefits of the dimensional data model? - It centralizes its
structure around more flexible fact and dimension tables, and the joins between these
are more intuitive.
CHAPTER 1-Identify documentation resources for reporting out of Caboodle - Caboodle
Dictionary
Reporting with Caboodle document
Caboodle ER diagram

CHAPTER 1-Identify reporting needs that best fit Caboodle - Custom data packages
can be written by Caboodle developers to accommodate your organization's reporting
needs.
CHAPTER 1-How does Epic data flow into Caboodle - Epic data moves between
several databases before it gets to Caboodle.
CHRONICLES flows into CLARITY via ETL. After transformation, the data is stored in a
relational database on a separate server. Even though the structure of the Chronicles
and Clarity databases differ significantly, the ETL process preserves the relationships
mapped in Chronicles.
CLARITY flows into Caboodle data is extracted
from Clarity, transformed in the staging database, and presented for users in the
reporting database. This movement of data is realized via a set of SQL Server
Integration Services (SSIS) packages.
CHAPTER 1-How does Non-Epic data flow into Caboodle - The Caboodle developer
designs custom DMCs (Data Model Components) and writes SSIS
packages to bring additional data into the warehouse. This may be additional Epic data
from Clarity or non‐Epic data from 3rd party sources.
CHAPTER 2-Which of the following would you expect to be stored in a dimension table?
A. Users
B. ED Visits
C. Diagnosis definitions - A. Users
B. ED Visits
Users and diagnosis definitions would be in their own dimension tables. ED Visits are
considered reportable, measureable events and would be in a fact table.
CHAPTER 2-Which of the following are possible in Caboodle? For each of the answers
you select, use the Caboodle Dictionary to find an example.
A. Joining a Fact table to another Fact table
B. Joining a Fact table to a Dimension table
C. Joining a Dimension table to a Dimension table
D. Joining two different Fact tables to the same Profile Dimension table - (Examples
may vary)
a. EncounterFact can join to HospitalAdmissionFact
b. MedicationOrderFact can join to PatientDim
c. PatientDim can join to ProviderDim
CHAPTER 2-Which of the following can be populated by more than one package?
Select ALL that apply.
A. A single table
B. A single row in a table - A & B

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CHAPTER 2-TRUE or FALSE: Caboodle and Clarity can be used interchangeably. -
FALSE
CHAPTER 2-Fact Table - The core table types in a dimensional data model are facts
and dimensions. A row in a fact table corresponds to the occurrence of some significant,
measurable event, such as the ordering of a medication or the posting of a charge
transaction.
A fact table typically contains lookup columns to several dimension tables. This
arrangement somewhat resembles a star and so is referred to as a "star schema
Fact tables typically join to dimension tables, but may also join to other types of DMCs,
such as other fact tables and bridge tables.
All fact tables have a Count column, which stores 0 for the negative rows, 0 for any row
that represents deleted data, and 1 otherwise.
The names of fact tables end with "Fact."
All Fact Tables in Caboodle use a surrogate key as their primary key.
CHAPTER 2-Dimension Table - The core table types in a dimensional data model are
facts and dimensions.
A row in a dimension table represents a concrete entity which provides context for an
event, such as the patient for whom a medication was ordered. A fact table typically
contains lookup columns to several dimension tables. This arrangement somewhat
resembles a star and so is referred to as a "star schema."
Fact tables typically join to dimension tables, but may also join to other types of DMCs,
such as other fact
tables and bridge tables. It is possible, but not common, for a dimension table to contain
lookup columns to other DMCs.
All Dimension Tables in Caboodle use a surrogate key as their primary key.
CHAPTER 2-Surrogate Key - A system‐generated identifier in Caboodle. Not inherently
meaningful to report consumers.
All fact and dimension tables in Caboodle use a surrogate key as their primary key.
Surrogate key column names match the name of the table. Surrogate key values are
created during the ETL process. Each
surrogate key value uniquely identifies one row of the table.
Surrogate key values are not the same as source identifiers.

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