DATA
WAREHOU
SE
By: RAVI RANJAN
By: Ravi
Ranjan
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Data Warehousing: Definition, Components, Functionality, and Best Practices, Essays (high school) of Database Management Systems (DBMS)
An overview of data warehousing, including its definition, components such as staging area, data mart, and operational data store, functionality like data warehouse engine and metadata repository, and best practices for successful implementation. Data warehouses are large databases used to support business decisions by turning data into valuable information.
Typology: Essays (high school)
2012/2013
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DATA
WAREHOU
SE
By: RAVI RANJAN
By: Ravi
Ranjan
DEFINITION
Data Warehouse A collection of corporate information, derived directly from operational systems and some external data sources. Its specific purpose is to support business decisions, not business operations.
Data Warehouse Components
- (^) Staging Area
- (^) A preparatory repository where transaction data can be transformed for use in the data warehouse
- (^) Data Mart
- (^) Traditional dimensionally modeled set of dimension and fact tables
- (^) Per Kimball, a data warehouse is the union of a set of data marts
- (^) Operational Data Store (ODS)
- (^) Modeled to support near real-time reporting needs.
DATA WAREHOUSE FUNCTIONALITY
Data Warehouse Engine
Data Warehouse Engine
Optimized LoaderOptimized Loader Extraction Cleansing
Extraction Cleansing
Analyze Query
Analyze Query
Metadata RepositoryMetadata Repository
Relational Databases
Legacy Data
Purchased Data
ERP Systems
VERY LARGE DATA BASES
(^) Terabytes -- 10^
bytes:
(^) Petabytes -- 10^
bytes:
(^) Exabytes -- 10^
bytes:
(^) Zettabytes -- 10^
Wal-Mart -- 24 Terabytes
Geographic Information Systems National Medical Records
Weather images
Intelligence Agency Videos
WAREHOUSES ARE VERY LARGE DATABASES
COMPLEXITIES OF CREATING A DATA WAREHOUSE
Incomplete errors
Missing Fields
Records or Fields That, by Design, are
not Being Recorded
Incorrect errors
Wrong Calculations, Aggregations
Duplicate Records
Wrong Information Entered into Source
System
DATA WAREHOUSE PITFALLS
(^) You are going to spend much time extracting,
cleaning, and loading data
(^) You are going to find problems with systems
feeding the data warehouse
(^) You will find the need to store/validate data not
being captured/validated by any existing system
(^) Large scale data warehousing can become an
exercise in data homogenizing
DATA WAREHOUSE PITFALLS…
(^) The time it takes to load the warehouse will
expand to the amount of the time in the available window... and then some (^) You are building a HIGH maintenance system
(^) You will fail if you concentrate on resource
optimization to the neglect of project, data, and customer management issues and an understanding of what adds value to the customer
Thank
You
BACK TO ARCHITECTURE
Top-Down Architecture
Enterprise Data Mart Architecture
BACK TO ARCHITECTURE
Data Stage/Data Mart Architecture
BACK TO ARCHITECTURE