Architecture Difference between SAP Business Objects and IBM Cognos part1

Lets understand how Cognos product works internally

Most of BI product Architecture are almost similar internally.
BI Bus: Enterprise service Bus which surrounds all the services/servers which tool provide.
Typical ESB from Oracle BEA Aqualogic Stack engulfing many Web services looks like:
ESB_archNow you can compare this popular ESB with BI internal Architecture.
you can read more about ESB at : http://docs.oracle.com/cd/E13171_01/alsb/docs20/concepts/overview.html
Under 4 tier system: A client connects the Web server  (which is protected by firewall) using dispatcher. Dispatcher connects to Enterprise Service Bus (ESB) which surrounds all the application server services (Web services). ESB in case of cognos is Cognos BI Bus surrounds Web services Servers (like Report Server, Job server, Content Management server etc ). Mediation Layer Cognos BI Bus interacts with Non Java , C++ code which could not to converted or purposefully kept in C++ for may be more flexibility and speed
Cognos BI Bushttp://pic.dhe.ibm.com/infocenter/cbi/v10r1m1/index.jsp?topic=%2Fcom.ibm.swg.ba.cognos.crn_arch.10.1.1.doc%2Fc_arch_themulti-tierarchitecture.html

In case of SAP Business Objects (BO) ESB was not properly developed so an intermediate layer was created which works for interfacing between multiple servers like Job server, report server, page server etc. BO XI R2 came in pervius version was more in C++ to C++ to java bridge was created in ESB layer. Since Java was preferred language for coarse grain interoperability  provided by web services. Each server was developed using web services.
interaction between web server was routed through BI Bus.
BO-xi-r3.1-infrastructureIn latest version here u find a pipe connecting all components call Business Objects XI 3.1 Enterprise Infrastructure. Earlier version had different names. here you can see its connecting all server like Crystal report server, IFRS input file repository server( storing template of reports), OFRS Output file repository services, Program Job server(storing all programs which can be published on Portal (Infoview) ). This ESB does mediation between different server and achieves interoperability yet control of different components of products. This is in competitor product Cognos is called Cognos BI Bus.
http://bobi.blog.com/2013/06/02/sap-business-object-architecture-overview-and-comparatice-analysis/
For latest BO uses in memory product SAP HANA more about its competitors follow:
https://sandyclassic.wordpress.com/2011/11/04/architecture-and-sap-hana-vs-oracle-exadata-competitive-analysis/

In Micro-strategy there are two important server Intelligent server which creates cubes

More I will cover in later issues:
Oracle BI Architecture:
http://www.rittmanmead.com/2008/02/towards-a-future-oracle-bi-architecture/

Implementation OF BI system is not related to these product Architecture :
A  typical BI system under implementation haveing componets of ETL, BI, databases, Web server, app server, production server, test/development server looks like:
typical BI ArchtectureMore details: http://www.ibm.com/developerworks/patterns/bi/product-s390-web.html
Big Data Architecture:
From components perspective of ETL to BI implementation Aspect is little different
bigdata-scalein-architecture

Hadoop Architecture layers:
hadoop-architecturehttps://sandyclassic.wordpress.com/2011/10/19/hadoop-its-relation-to-new-architecture-enterprise-datawarehouse/
http://codemphasis.wordpress.com/2012/08/13/big-data-parallelism-and-hadoopbasics/

Just like UDDI registry is repository of Web

Cloud Computing, 3V ,Data warehousing and Business Intelligence

The 3V volume, variety, velocity Story:

Datawarehouses maintain data loaded from operational databases using Extract Transform Load ETL tools like informatica, datastage, Teradata ETL utilities etc…
Data is extracted from operational store (contains daily operational tactical information) in regular intervals defined by load cycles. Delta or Incremental load or full load is taken to datwarehouse containing Fact and dimension tables which are modeled on STAR (around 3NF )or SNOWFLAKE schema.
During business Analysis we come to know what is granularity at which we need to maintain data. Like (Country,product, month) may be one granularity and (State,product group,day) may be requirement for different client. It depends on key drivers what level do we need to analyse business.

There many databases which are specially made for datawarehouse requirement of low level indexing, bit map indexes, high parallel load using multiple partition clause for Select(during Analysis), insert( during load). data warehouses are optimized for those requirements.
For Analytic we require data should be at lowest level of granularity.But for normal DataWarehouses its maintained at a level of granularity as desired by business requirements as discussed above.
for Data characterized by 3V volume, velocity and variety of cloud traditional datawarehouses are not able to accommodate high volume of suppose video traffic, social networking data. RDBMS engine can load limited data to do analysis.. even if it does with large not of programs like triggers, constraints, relations etc many background processes running in background makes it slow also sometime formalizing in strict table format may be difficult that’s when data is dumped as blog in column of table. But all this slows up data read and writes. even is data is partitioned.
Since advent of Hadoop distributed data file system. data can be inserted into files and maintained using unlimited Hadoop clusters which are working parallel and execution is controlled by Map Reduce algorithm . Hence cloud file based distributed cluster databases proprietary to social networking needs like Cassandra used by facebook etc have mushroomed.Apache hadoop ecosystem have created Hive (datawarehouse)
https://sandyclassic.wordpress.com/2011/11/22/bigtable-of-google-or-dynamo-of-amazon-or-both-using-cassandra/

With Apache Hadoop Mahout Analytic Engine for real time data with high 3V data Analysis is made possible.  Ecosystem has evolved to full circle Pig: data flow language,Zookeeper coordination services, Hama for massive scientific computation,

HIPI: Hadoop Image processing Interface library made large scale image processing using hadoop clusters possible.
http://hipi.cs.virginia.edu/

Realtime data is where all data of future is moving towards is getting traction with large server data logs to be analysed which made Cisco Acquired Truviso Rela time data Analytics http://www.cisco.com/web/about/ac49/ac0/ac1/ac259/truviso.html

Analytic being this of action: see Example:
https://sandyclassic.wordpress.com/2013/06/18/gini-coefficient-of-economics-and-roc-curve-machine-learning/

with innovation in hadoop ecosystem spanning every direction.. Even changes started happening in other side of cloud stack of vmware acquiring nicira. With huge peta byte of data being generated there is no way but to exponentially parallelism data processing using map reduce algorithms.
There is huge data out yet to generated with IPV6 making possible array of devices to unique IP addresses. Machine to Machine (M2M) interactions log and huge growth in video . image data from vast array of camera lying every nuke and corner of world. Data with a such epic proportions cannot be loaded and kept in RDBMS engine even for structured data and for unstructured data. Only Analytic can be used to predict behavior or agents oriented computing directing you towards your target search. Bigdata which technology like Apache Hadoop,Hive,HBase,Mahout, Pig, Cassandra, etc…as discussed above will make huge difference.

kindly answer this poll:

Some of the technology to some extent remain Vendor Locked, proprietory but Hadoop is actually completely open leading the the utilization across multiple projects. Every product have data Analysis have support to Hadoop. New libraries are added almost everyday. Map and reduce cycles are turning product architecture upside down. 3V (variety, volume,velocity) of data is increasing each day. Each day a new variety comes up, and new speed or velocity of data level broken, records of volume is broken.
The intuitive interfaces to analyse the data for business Intelligence system is changing to adjust such dynamism  since we cannot look at every bit of data not even every changing data we need to our attention directed to more critical bit of data out of heap of peta-byte data generated by huge array of devices , sensors and social media. What directs us to critical bit ? As given example
https://sandyclassic.wordpress.com/2013/06/18/gini-coefficient-of-economics-and-roc-curve-machine-learning/
f
or Hedge funds use hedgehog language provided by :
http://www.palantir.com/library/
such processing can be achieved using Hadoop or map-reduce algorithm. There are plethora of tools and technology which are make development process fast. New companies are coming  from ecosystem which are developing tools and IDE to make transition to this new development  easy and fast.

When market gets commodatizatied as it hits plateu of marginal gains of first mover advantage the ability to execute becomes critical. What Big data changes is cross Analysis kind of first mover validation before actually moving. Here speed of execution will become more critical. As production function Innovation gives returns in multiple. so the differentiate or die or Analyse and Execute feedback as quick and move faster is market…

This will make cloud computing development tools faster to develop with crowd sourcing, big data and social Analytic feedback.