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Datawarehousing Fundementals

Datawarehousing Fundementals#

Data mining - showing unknown trends If insights are not correlated they are useless

BI - reporting, analytics, management information, based on facts BI - Gain insights and make trusted decisions

Advanced analytics - extracts fact

Operational data store / data lake - a staging area Data warehouse purpose - enhance rate of knowledge acquisition and answering management questions

Can’t do it on an operational system - must be done on a data warehouse.

Don’t want to disturb operational data

Can’t do both. Operations must be independent.

Quick wins with data sceince scripts after a while it will use more resources.

  • Components of a data warehouse?
  • Mananging relationship and syncing of operational database to Datawarehousing - data duplication.

Single source of truth

KPI: COst of Capital, Headcount, Earnings per share, Cost per Unit

Users in BI:

  • Strategic - Executive make more decisions
  • Tactical - Data scienctists, make some decisions.
  • Operational - Little to no decision.

Data warehouse Enterprise Informatin Managment Framework - Data sources, Integration, Derived Information, Semantics (Presentaiton Logic), Deliver / Manipulation / Consumption

Cube is a precalcilated values stored in memory

Star schema: A fact and dimensions around it Fact: Quantity, cost, total Dimensions: Time, Customer, Product, Branch

Delta processing

Surrogate key is an extra key for delta processing and seeing changed records