Building Data ware House

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Presentation transcript:

Building Data ware House

Building a Data Warehouse Data Warehouse Lifecycle Analysis Design Import data Install front-end tools Test and deploy

Stage 1: Analysis Identify: Create an enterprise-level data dictionary Design Import data Install front-end tools Test and deploy Identify: Target Questions Data needs Timeliness of data Granularity Create an enterprise-level data dictionary Dimensional analysis Identify facts and dimensions

Stage 2: Design Star schema Data Transformation Aggregates Analysis Design Import data Install front-end tools Test and deploy Star schema Data Transformation Aggregates Pre-calculated Values HW/SW Architecture Dimensional Modeling

Dimensional Modeling Fact Table – The primary table in a dimensional model that is meant to contain measurements of the business. Dimension Table – One of a set of companion tables to a fact table. Most dimension tables contain many textual attributes that are the basis for constraining and grouping within data warehouse queries.

Stage 3: Import Data Identify data sources Analysis Design Import data Install front-end tools Test and deploy Identify data sources Extract the needed data from existing systems to a data staging area Transform and Clean the data Resolve data type conflicts Resolve naming and key conflicts Remove, correct, or flag bad data Conform Dimensions Load the data into the warehouse

Importing Data Into the Warehouse Operational Systems (source systems)

Stage 4: Install Front-end Tools Analysis Design Import data Install front-end tools Test and deploy Reporting tools Data mining tools GIS Etc.

Stage 5: Test and Deploy Usability tests Software installation Analysis Design Import data Install front-end tools Test and deploy Usability tests Software installation User training Performance tweaking based on usage

Waterfall Model

Spiral Model

Rapid Application Development (RAD)

DWH Life Cycle Model DESIGN ENHANCE PROTOTYPE OPERATE DEPLOY 58