The Technology Evaluator s Cheat Sheets. Business Intelligence & Analy:cs
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1 The Technology Evaluator s Cheat Sheets Business Intelligence & Analy:cs
2 Summary So1ware Stacks Full Stacks (DB + ETL Tools + Front- End So1ware) Back- End Stacks (DB and/or ETL Tools Only) Front- End Stacks (Front- End So1ware Only) Technologies Data Warehouse Class ( Big Scale ) Data Mart Class ( Small Scale )
3 So1ware Stacks ETL DW ETL Query/Import BACK- END STACK ETL Features Data Warehouse Features Data Mart Features FRONT- END STACK Data VisualizaLon Features Data Analysis & Discovery Features FULL- STACK
4 The Full Stack. When? Centralized data management and storage To deliver a single version of crilcal data To make data easier for non- techies to access, query and share To simplify on- going or ad- hoc data management tasks ETL Func:onality Is Needed MulLple data sources, or mullple tables where views are too complex/slow The volume of data is expected to cause slow performance Data needs to be restructured before being delivered to users Data is dirty (entry errors, value mismatches) Required metrics are in different tables or sources To protect the opera:onal systems from rogue queries To access non- queryable data sources
5 Full Stack: TradiLonal Architectures Data Warehouse + Data Marts End- Users (Business) Data Extracts (No DW) End- Users (Business) Front- end Tools Front- end Tools OLAP Cubes, or In- Memory Marts In- Memory Marts Excel/CSV DW Data Warehouse ETL / Mash- up Data Sources ETL / Mash- up Data Sources IT Department IT Department
6 Data Warehouse: Pros & Cons DW + Data Marts Data Extracts (No DW) Approach SoluLon- oriented Project- specific Data Quality & Accuracy Higher Lower Scalability Higher Lower Single Version of the Truth Yes No IniLal Investment Higher Lower Level of Detail Summarized Granular Owner IT IT or Business (oplonal) ImplementaLon Time Longer Shorter Technical Complexity Higher Lower Advantage / Disadvantage
7 Technologies In The Space
8 Backend Technologies Data Mart- Class, we call it Small Scale Online AnalyLcal Processing (OLAP) In- Memory Databases (IMDB) Data Warehouse- Class, we call it Big Scale Database So1ware Appliances Database Computer Appliances Distributed Databases
9 Small Scale. When? When there is only a single data source, which means the data doesn t need to be consolidated (ETL) prior to being delivered for business analylcs When there aren t many different abributes and metrics to cross- reference (the Data Mart doesn t need to have many fields) For a one- Lme project (e.g. one dashboard), with no added requirements, new data sources or other changes expected in the future
10 Big Scale. When? For a single centralized data store to serve mullple users and mullple business scenarios (single version of the truth) When data volumes are large, are rapidly growing or may unpredictably spike Big Scale Small Scale Max. Data Mart Size Terabyte - Petabytes Gigabytes Max. Number of Fields (1 mart) PracLcally Unlimited Limited Max. Number of Records (1 table) Billions Millions
11 Data Mart- Class Technologies ( Small Scale )
12 In- Memory Databases (IMDB) Achieves fast performance by loading the enlre data mart into RAM, thus avoiding slow disk- reads ( I/O Boblenecks ) Categorized as Small Scale because the size of data mart is effeclvely limited by the size of RAM, placing in the Gigabyte scale category In some IMDB technologies, RAM consumplon is also draslcally affected by concurrent use.
13 Online AnalyLcal Processing (OLAP) Achieves fast performance by pre- calculalng metrics (field aggregalons) for all sets and subsets of unique values in all dimensions (fields) over- night. This avoids performing these slow operalons in real- Lme during the work- day. Categorized as Small Scale because storing the results of these pre- calculalons ( The Cube ) takes exponenlally more storage resources than the actual raw data does, limilng the actual size of raw data that can make up a cube to GB scale. The query engines behind most OLAP technologies are based on RDBMS technology, whose own scale and performance limitalons OLAP cannot overcome (e.g. joining, grouping).
14 Data Warehouse- Class Technologies ( Big Scale )
15 So1ware Appliances A so1ware appliance is a souware applica:on that might be combined with just enough operalng system (JeOS) for it to run op:mally on industry standard hardware (typically a server) or in a virtual machine.
16 Computer Appliances A computer appliance is generally a separate and discrete hardware device with integrated so1ware (firmware), specifically designed to provide a specific compulng resource. Computer appliances are generally not designed to allow the customers to change the so1ware, or to flexibly reconfigure the hardware.
17 Distributed Databases A distributed database may be stored in mullple computers, located in the same physical localon; or may be dispersed over a network of interconnected computers. A distributed database system consists of loosely- coupled sites that share no physical components (such as disk, RAM and CPU)
18 Big Scale Technologies, Compared SoUware Appliance Computer Appliance Distributed Databases Hardware Class Commodity Proprietary Commodity Best Architecture 1 Server 1 Server N Servers Capacity Terabytes Terabytes Petabytes Hardware Cost 4-5 figures 6-7 figures 5-6 figures
19 Full- Stack Vendors SiSense Microso1 Oracle IBM SAP Microstr. QlikView ETL Elas:Cube Manager SSIS Oracle ETL Features InfoSphere DataStage NetWeaver BW ETL SoUware Appliance Elas:Cube (Columnar) SQL Server (RDBMS) Oracle DB (RDBMS) DB2 (RDBMS) - Hardware Appliance OLAP IMDB In- Chip Elas:Cube (Columnar) - Analysis Services PowerPivot - ExaData Hyperion ExalyLcs - Netezza Cognos Cognos - HANA Columnar In- Mem MSTR ETL Features - - BW Business Objects Microstrategy In- Mem OLAP QlikView Expressor HANA Columnar In- Mem Microstrategy In- Mem OLAP QlikView AssociaLve In- Mem - - -
20 Thank You! Visit us at
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