Chetan Bhirud Raza Mohammad Abinash Sahoo Online Marketing Giant

BUSINESS BACKGROUND Amazon was found on July 5, 1994 by Jeff Bezos. The Amazon Way More than Disruption Swimming with the Shark The Game Mind Some Points to know: Amazon has around $7.7 billion in cash. If you stacked every Himalayan Crystal Lamp purchased by Amazon customers this holiday season, the height would reach the top of Himalaya’s highest peak -- Mt. Everest. Amazon has the largest warehouses around the world, some almost equal to four football ground.

BUSINESS NEED AMAZON PRINCIPLES: CUSTOMER OBSESSION OWNERSHIP INVENT AND SIMPLIFY INSIST ON THE HIGHEST STANDARDS EARN TRUST OF OTHERS DELIVER RESULTS

WHY DATA WAREHOUSE? AMAZON IS A HUGE ORGANIZATION AND TO MEET THE PRINCIPLES, IT NEEDS A DATA WAREHOUSE TO FACILITATE IT’S CURRENT STANDING AND IMPROVISING STRATEGIES IN THE MARKET. IT ACTS AS DECISION SUPPORT SYSTEM.

SALES GRAPHS THE GRAPHS SHOWS THE SALES DETAILS IN THE FOLLOWING TERMS: REVENUE PROFIT/LOSS EARNINGS PER SHARE

FACTS AND DIMENSIONS AMAZON FACTS: SALES QUANTITY DIMENSIONS: PRODUCT CUSTOMER WAREHOUSE SHIPPER TIME

OLAP CUBE OLAP – ONLINE ANALYTICAL PROCESSING IS A COMPUTER-BASED TECHNIQUE USED FOR ANALYZING BUSINESS DATA IN SEARCH OF BUSINESS INTELLIGENCE A CUBE CAN BE CONSIDERED A MULTI-DIMENSIONAL GENERALIZATION OF A TWO- OR THREE-DIMENSIONAL SPREADSHEET EACH CELL OF THE CUBE HOLDS A NUMBER THAT REPRESENTS SOME MEASURE OF THE BUSINESS MEASURES ARE DERIVED FROM THE RECORDS IN THE FACT TABLE AND DIMENSIONS ARE DERIVED FROM THE DIMENSION TABLES OLAP DATA IS TYPICALLY STORED IN A STAR SCHEMA OR SNOWFLAKE SCHEMA IN A RELATIONAL DATA WAREHOUSE

OPERATIONS  OLAP OPERATIONS ARE FOUNDATION FOR MOST USER INTERFACES AND FUNCTIONALITY USED BY DATA VISUALIZATION TOOLS.  THE DIFFERENT TYPES OF OLAP ACTIVITIES ARE:  SLICE  DICE  DRILL DOWN/UP  ROLL-UP  PIVOT

SLICE PICK A RECTANGULAR SUBSET OF A CUBE BY CHOOSING A SINGLE VALUE FOR ONE OF ITS DIMENSIONS AND CREATE A NEW CUBE WITH ONE FEWER DIMENSION IN OTHER WORDS, “SLICING” A PART OF THE CUBE

DICE  PRODUCES A SUB-CUBE BY ALLOWING THE ANALYST TO PICK SPECIFIC VALUES OF MULTIPLE DIMENSIONS.  “DICING” OF A CUBE

DRILL DOWN/UP  ALLOWS THE USER TO NAVIGATE AMONG LEVELS OF DATA RANGING FROM THE MOST SUMMARIZED (UP) TO THE MOST DETAILED (DOWN)  E.G.: SALES FIGURES FOR THE INDIVIDUAL PRODUCTS (DETAILED PART)

ROLL-UP  AGGREGATE, CONSOLIDATE  INVOLVES COMPUTING ALL OF THE DATA RELATIONSHIPS FOR ONE OR MORE DIMENSIONS. TO DO THIS, A COMPUTATIONAL RELATIONSHIP OR FORMULA MIGHT BE DEFINED.

PIVOT  ALSO CALLED ROTATE OPERATION.  IT ROTATES THE DATA IN ORDER TO PROVIDE AN ALTERNATIVE PRESENTATION OF DATA – THE REPORT OR PAGE DISPLAY TAKES A DIFFERENT DIMENSIONAL ORIENTATION.

REPORT VARIOUS REPORTS CAN BE GENERATED BY PERFORMING OLAP ACTIVITIES ON THE DATA WAREHOUSE: SALES BY CUSTOMER AGE/GENDER BY REGION SALES BY PRODUCT BY CATEGORY SALES BY PRODUCT BY WAREHOUSE DELIVERY TIME BY PRODUCT BY WAREHOUSE

REFERENCES FLOOD#.VC2UWRGZH8E FLOOD#.VC2UWRGZH8E GLIMPSE-WHAT-CUSTOMERS-BOUGHT-MOST GLIMPSE-WHAT-CUSTOMERS-BOUGHT-MOST RECORDS-SECOND-STRAIGHT-LOSS/ RECORDS-SECOND-STRAIGHT-LOSS/

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