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Why Analytics? BI Reporting and Ad-Hoc Analytic What happen? When and Where it happened? How much? Predictive Analytics What will happen? Where will the impact be? Optimization  What is the best choice? Challenges of Analytics Expensive Inefficient Process Limited analytic complexity Inability to react to market conditions Time consuming Inability to experiment Processing on limited and stale data

Netezza: ======== Netezza is rows base database using colummar compression Hash algorithm is used S-Blade / Snippet Processing Unit (SPU): ======================================= Blade Server Card and Netezza DB Accelerator (FPGA) 1 Blade Server Card = 8 Core CPU (2 Intel Quad-Core) 1 Blade Server Card = 16 GB DRAM 1 Netezza DB Accelerator = 8 Core FPGA (4x Dual-Core) 1 Snipet Processing Unit = 8 Disks (responsible for) 1 Snipet Processing Unit = 8 Snippet Processor 1 Snippet Processer = 1 Core CPU, 1 Core FPGA and 2 GB DRAM 1 S-Blade = 6 sisks and 2 spare disks Snippet Processing Array (SPA): =============================== 1 Storage Processing Array (SPA) = 4 Disk Encloures 1 Storage Processing Array (SPA) = 2 Spares Disks 1 Disk Encloures = 12 Disks 1 Disk = 1 TB SAS Drive 1 Disk = data on drive call data slice (table are splits across data slides 1 Disk = Devided into three partion - Primary, Mirror and Temp Failed drive is automatically regenerated on a ...

http://www.pentaho.com/Optimize-the-Data-Warehouse

Why Big Data? Today, An Application without data analytics is like a car without a steering wheel. It will go, but there’s no controlling its direction. Data Management has become extremely complex in recent years, because day by day data is growing exponentially and it is biggest challenge for IT industry to store and analyze those data. One-third of organizations experiencing increase of 25 percent or more annually. Healthcare is one of the fastest growing segments and growing at 48 percentage yearly compared to 40 percent of overall digital universe. Data which fulfill three criteria is call “Big Data”: Volume: How much data Variety: The various types of data Velocity: How fast data is processed Volume: Big data implies enormous volumes of data (petabytes of data), it is the most immediate challenge of big data. It is used to be employees created data, now that data is generated by human, network and machines interaction on systems like social media. It requ...

SUM(     LastPeriods(         3,         PARALLELPERIOD(             [Date].[CalenderHierarchy].[Month],             1,    Ancestor(                 [Date].[CalenderHierarchy].CURRENTMEMBER                ,[Date].[CalenderHierarchy].[Month]             )         )     )     ,[Measures].[Count] )

SUM(     PARALLELPERIOD(         [Date].[CalenderHierarchy].[Month],         1, Ancestor(             [Date].[CalenderHierarchy].CURRENTMEMBER            ,[Date].[CalenderHierarchy].[Month]         )     ),     [Measures].[Count] )