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Transcript of Data Warehousing M R BRAHMAM. Data Warehousing - Architecture Enterprise Data Warehouse Enterprise...
![Page 1: Data Warehousing M R BRAHMAM. Data Warehousing - Architecture Enterprise Data Warehouse Enterprise Data Warehouse Data Mart Execution Systems CRM ERP.](https://reader035.fdocuments.in/reader035/viewer/2022081211/56649cd95503460f949a28e1/html5/thumbnails/1.jpg)
Data WarehousingData Warehousing
M R BRAHMAM
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Data Warehousing - Data Warehousing - ArchitectureArchitecture
EnterpriseData
Warehouse
EnterpriseData
WarehouseData MartData Mart
Data MartData Mart
ExecutionSystems
• CRM• ERP• Legacy• e-Commerce
ExecutionSystems
• CRM• ERP• Legacy• e-Commerce
Reporting Tools
OLAP Tools
Ad Hoc Query Tools
Data Mining Tools
Reporting Tools
OLAP Tools
Ad Hoc Query Tools
Data Mining Tools
ExternalData
• Purchased Market Data• Spreadsheets
ExternalData
• Purchased Market Data• Spreadsheets
•Oracle•SQL Server•Teradata•DB2
Data and Metadata Repository Layer
ETL Tools:•Informatica PowerMart•ETI•Oracle Warehouse Builder•Custom programs•SQL scripts
Extract, Transformation, and Load (ETL)
Layer
• Cleanse Data• Filter Records• Standardize Values• Decode Values• Apply Business Rules• Householding• Dedupe Records• Merge Records
Extract, Transformation, and Load (ETL)
Layer
• Cleanse Data• Filter Records• Standardize Values• Decode Values• Apply Business Rules• Householding• Dedupe Records• Merge Records
Presentation Layer
ETL Layer
Metadata Repository
Metadata Repository
ODSODS
•PeopleSoft•SAP•Siebel•Oracle Applications•Manugistics•Custom Systems
Data MartData Mart
•Custom Tools•HTML Reports
•Cognos•Business Objects
•MicroStrategy•Oracle Discoverer
•Brio•Data Mining Tools
•Portals
Source Systems
Sample Technologies:
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OLTP vs DWOLTP vs DW
OLTP DW
Data dependencies (E-R) model
Dimensional model
Microscopic data consistency
Global data consistency
Millions of transactions per day
One transaction per day
Mostly does not keep history
Keeping history is necessary
Gets loaded in the day Gets loaded in the night
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Dimensional Data ModelingDimensional Data Modeling
E-R model– Symmetric– Divides data into many entities– Describes entities and relationships– Seeks to eliminate data redundancy– Good for high transaction performance
Dimensional model– Asymmetric– Divides data into dimensions and facts– Describes dimensions and measures– Encourages data redundancy– Good for high query performance
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Facts/DimensionsFacts/Dimensions
Fact– Central, dominant table– Multi-part primary key– Holds millions & billions of records– Links directly to dimensions– Stores business measures– Constantly varying data
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Facts/Dimensions (contd.)Facts/Dimensions (contd.)
Dimensions– Single join to the fact table (single
primary key)– Stores business attributes– Attributes are textual in nature– Organized into hierarchies– More or less constant data– E.g. Time, Product, Customer, Store,
etc.
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Star/Snowflake schemaStar/Snowflake schema
Star schema– Fact surrounded by 4-15 dimensions– Dimensions are de-normalized
Snowflake schema– Star schema with secondary
dimensions– Don’t snowflake for saving space– Snowflake if secondary dimensions
have many attributes
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Star schemaStar schema
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Star schema exampleStar schema example
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Snowflake schema exampleSnowflake schema example
STORE KEY
Store Dimension
Store DescriptionCityStateDistrict IDDistrict Desc.Region_IDRegion Desc.Regional Mgr.
District_IDDistrict Desc.Region_ID
Region_ID
Region Desc.Regional Mgr.
STORE KEYPRODUCT KEYPERIOD KEY
DollarsUnitsPrice
Store Fact Table
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DM , DW & ODSDM , DW & ODS
DM– Organized around a single business
process– Represents small part of the
organization’s business– Logical subset of the complete data
warehouse– Faster roll out, but complex integration
in the long run
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DM , DW & ODS (contd.)DM , DW & ODS (contd.)
DW– Union of its constituent data marts– Queryable source of data in the organization– Requires extensive business modeling (may
take years to design and build) ODS
– Point of integration for operational systems– Low-level decision support– Can store integrated data, but at detailed level
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OLAPOLAP
Element of decision support systems (DSS) Support (almost) ad-hoc querying for business
analyst Helps the knowledge worker (executive, manager,
analyst) make faster & better decisions ROLAP - extended RDBMS that maps operations
on multidimensional data to standard relational operators
MOLAP - Special-purpose server that directly implements multidimensional data and operations
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OthersOthers
Additive, semi-additive & non-additive facts
Factless factsSlowly changing dimensionsConformed facts and dimensionsCubesDrill down / Drill upSlice and dice