A Day in Life of Business Intelligence (BI) Architect- part 1

BI Architect most important responsibility is maintaining semantic Layer between Datawarehouse and BI Reports.
There are basically Two Roles of Architect: BI Architect or ETL Architect in data warehousing and BI. (ETL Architect in Future posts).
Semantic Layer Creation
Once data-warehouse is built and BI reports Needs to created. Then requirement gathering phase HLD High level design and LLD Low Level design are made.
Using HLD and LLD BI semantic layer is built in SAP BO its called Universe, in IBM Cognos using framework manager create Framework old version called catalogue, In Micro strategy its called project.
Once this semantic layer is built according to report data SQL requirements.
Note: Using semantic layer saves lot of time in adjustment of changed Business Logic in future change requests.
Real issues Example: Problems in semantic Layer creation like in SAP BO: Read
https://sandyclassic.wordpress.com/2013/09/18/how-to-solve-fan-trap-and-chasm-trap/
Report Development:
Reports are created using objects created by semantic layer.Complex reporting requirement for
1. UI require decision on flavour of reporting Tool like within
There are sets of reporting tool to choose from Like in IBM Cognos choose from Query Studio, Report Studio, Event Studio, Analysis Studio, Metric Studio.
2. Tool modification using SDK features are not enough then need to modify using Java/.net of VC++ API. At html level using AJAX javascript API or integrating with 3rd party API.
3. Report level macros/API for better UI.
4. Most important is data requirement my require Coding procedure at database or consolidations of various databases. Join Excel data with RDBMS and unstructured data using report level features. Data features may be more complex than UI.
5. user/data level security,LDAP integration.
6. Complex Scheduling of reports or bursting of reports may require modification using rarely Shell script or mostly Scheduling tool.
List is endless
Read More:
details of
https://sandyclassic.wordpress.com/2014/01/26/a-day-in-life-of-bi-engineer-part-2/

Integration with Third party and Security

After This BI’s UI has to fixed to reflect customer requirement. There might be integration with other products and seamless integration of users By LDAP. And hence Objects level security, User level security of report data according to User roles.
Like a Manager see report with data The same data may not be visible to clerk when he sees same report. Due filtering of data by user roles using User Level security.

BI over Cloud
setting BI over cloud Read blog.
Cloud Computing relation to Business Intelligence and Datawarehousing

Read :
1. https://sandyclassic.wordpress.com/2013/07/02/data-warehousing-business-intelligence-and-cloud-computing/

2. https://sandyclassic.wordpress.com/2013/06/18/bigdatacloud-business-intelligence-and-analytics/

Cloud Computing and Unstructured Data Analysis Using
Apache Hadoop Hive
Read: 
https://sandyclassic.wordpress.com/2013/10/02/architecture-difference-between-sap-business-objects-and-ibm-cognos/
Also it compares Architecture of 2 Popular BI Tools.

Cloud Data warehouse Architecture:
https://sandyclassic.wordpress.com/2011/10/19/hadoop-its-relation-to-new-architecture-enterprise-datawarehouse/

Future of BI
No one can predict future but these are directions where it moving in BI.
https://sandyclassic.wordpress.com/2012/10/23/future-cloud-will-convergence-bisoaapp-dev-and-security/

A day in life of BI Engineer part 2

Read Part1:
https://sandyclassic.wordpress.com/2014/01/26/a-day-in-life-of-business-intelligence-engineer/
Part 2:
First few days should understand business otherwise cannot create effective reports.
9:00 -10am Meet customer to understands key facts which affect business.
10-12 prepare HLD High level Document containing 10,000 feet view of requirement.
version 1. it may refined later subsequent days.
12-1:30 attend scrum meeting to update status to rest of team. co-ordinate with Team Lead, Architect and project Manager for new activity assignment for new reports.
Usually person handling one domain area of business would be given that domain specific reports as during last report development resource already acquired domain knowledge.
And does not need to learn new domain..otherwise if becoming monotonous and want to move to new area. (like sales domain report for Chip manufactuers may contain demand planning etc…)
1:30-2:00 document the new reports to be worked on today.
2:00-2:30 Lunch
2:30-3:30 Look at LLD and HLD of new reports. find sources if they exist otherwise Semantic layer needs to modified.
3:30-4:00 co-ordinate with other resource reports requirement with Architect to modify semantic layer, and other reporting requirements.
4:00-5:00 Develop\code reports, conditional formatting,set scheduling option, verify data set.
5:00-5:30 Look at old defects rectify issues.(if there is separate team for defect handling then devote time on report development).
5:30-6:00 attend defect management call and present defect resolved pending issue with Testing team.
6:00-6:30 document the work done. And status of work assigned.
6:30-7:30 Look at report pending issues. Code or research work around.
7:30-8:00 report optimisation/research.
8:00=8:30 Dinner return back home.
Ofcourse has to look at bigger picture hence need to see what reports other worked on.
Then Also needed to understand ETL design , design rules/transformations used for the project. try to develop frameworks and generic report/code which can be reused.
Look at integration of these reports to ERP (SAP,peopesoft,oracle apps etc ), CMS (joomla, sharepoint), scheduling options, Cloud enablement, Ajax-fying reports web interfaces using third party library or report SDK, integration to web portals, portal creation for reports.
So these task do take time as and when they arrive.

Next generation Application development

The Next generation application development will not only take care of utilizing 50 or 100+ processors which will be available in you laptop or desktop or mobile but by using parallel processing available at clients
https://sandyclassic.wordpress.com/2012/11/11/parallel-programming-take-advantage-of-multi-core-processors-using-parallel-studio/
I covered 7 points last article this is part -2 of
https://sandyclassic.wordpress.com/2013/09/18/new-breed-of-app-development-is-here/
also Next genration ERP read first: https://sandyclassic.wordpress.com/2013/09/16/new-age-enterprise-resource-planning-systems/
8. More pervasive BI eating App: Business Intelligence application development will go deeper in organisation Hierarchy
Oraganisation Hirearchyfrom more strategic level BI  and Middle management level to more pervasive  transactional processing level , and Office automation System level BI (shown in diagram as knowledge level or operational level.)

How it will affect architecture of Enterprise product Read SAP HANA
https://sandyclassic.wordpress.com/2011/11/04/architecture-and-sap-hana-vs-oracle-exadata-competitive-analysis/
Understanding Management aspect to little contrary view but related.. there will be need for more deeper strategic Information system to make more unstructured decision making.
https://sandyclassic.wordpress.com/2013/01/31/strategic-information-systems-will-be-in-focus-again-next-5-yrs/

pervasive BI bound to eat up Application development market also fulled by in-memory products like cognos TM1, SAP HANA etc..but also changes, cross functional innovation happening at enterprise level.
read :https://sandyclassic.wordpress.com/2013/09/18/new-breed-of-app-development-is-here/

As with these products no need for separate Database for datawarehouse and for operational systems. This unification of Operational data store ODS and data warehouse DW. on reporting level both Business intelligence BI and operational reporting will be accessing same database and that will be using in Memory technology.

9. Bigdata as everyone knows is Hot: more unstructured data than structured data today present for you is like open laboratory to experiment. More of it will find place in strategic management system and Management Information system.
read more details: https://sandyclassic.wordpress.com/2013/06/18/bigdatacloud-business-intelligence-and-analytics/

Read Application in security for metadata analysis : https://sandyclassic.wordpress.com/2013/06/18/how-to-maintain-privacy-with-surveillance/

10. Application security will be important as never before: its already there .
The intensity can be gauged from fact that changes in top 10 OWASP list is happening as never before and positions are changing in terms of top most risk ranking.
https://www.owasp.org/index.php/Top_10_2013-Top_10

list before:

https://www.owasp.org/index.php/Top_10_2010-Main

2010 A2 was Cross site Scripting XSS but 2013 at ranking to of perceived risk is Broken Authentication and session management. Changes do happen but here ranking and no of incident changing fast because momentum is fast.
11. More will continue when I find time next time….

New Breed of App development is here

Here are reasons Why next generation app will be totally different:
1. – In few years we will be seeing ending dominance of physical routers, switches , firewall to virtual soft switches, virtual routers , software defined routers and switches. More open routing technology would be program driven rather than configuration on boxes.
Companies like application firewall maker Palo Alto Networks and virtual programmable router maker nicira have huge role to play.
https://sandyclassic.wordpress.com/2012/07/16/cloud-innovation-heating-up-network-protocol-stack-and-telecom-stack/

its also affected by trends in Network technology
https://sandyclassic.wordpress.com/2012/09/11/trends-in-computer-networking-and-communication-2/
2. – in next year we will see 20+ processors on single machine making parallel processing one of important requirement. Huge software would be re written to meet this requirement.
https://sandyclassic.wordpress.com/2012/11/11/parallel-programming-take-advantage-of-multi-core-processors-using-parallel-studio/

3. The changes in business and systems are occurring very fast as system and getting more understood and cross functional due to intense competition Where only innovation can make you stay ahead of curve: Read more reasons why?
https://sandyclassic.wordpress.com/2013/09/16/new-age-enterprise-resource-planning-systems/

4. Cloud will increase innovation to change way we think about software:
Software As service SAAS, PAAS, IAAS going to make more deeper innovation as defined in above article (https://sandyclassic.wordpress.com/2013/07/02/data-warehousing-business-intelligence-and-cloud-computing/).
How innovation on cloud will be much quicker read :
https://sandyclassic.wordpress.com/2013/07/02/data-warehousing-business-intelligence-and-cloud-computing/

5. Laptop will never go (large screen requirement) but Mobile will be mass platform:
As we can move we can see virtually wearable shirts made of graphene with storage and data streamed on walls .. as when we want we can just grab wall data to graphene shirts..
Read more about Graphene: https://sandyclassic.wordpress.com/2013/01/18/graphene-the-wonder-material-foldable-cell-phones-wearable-computerbionic-devices-soon-reality/
surfaces will keep emerging we would see virtually display in air without any device but what it would be added with augmented reality and virtual reality.
https://sandyclassic.wordpress.com/2012/06/27/future-of-flex-flash-gamification-of-erp-enterprise-software-augmented-reality-on-mobile-apps-iptv/
we can in future just stream data to wall and program on wall outside our house.
6. Internet of things : where Machine to machine transfer of information and data and semantic web will make possible more intelligent feedback to user by all devices based on user need. so when you pick up milk from shelf next time. your fridge will search for you and alert you on latest offer of cheapest milk from various retailer.
And it will be displayed on fridge itself.. not only that it would order for you when its empty if you configure so. it will calculate you calorie consumed by family of fridge item and send updates to doctor monitoring you and display return messages from doctors.
More: https://sandyclassic.wordpress.com/2013/05/03/classifying-ubiquitious-data-images-into-emotion-for-target-advertisement-campaign/
7. Sensors will be everywhere and huge and Ubiquity will rule :
https://sandyclassic.wordpress.com/2012/10/28/ubiquity-the-most-crucial-challenge-in-business-intelligence/

Why Online Courses are Killer App?

Some courses give introduction to subject.it was wonderful experience going through course. I wanted to suggest Online Education platform can be used for really creative courses for which may be hard to find students in university..
suppose: “Business Strategy Case Study course”
Why Online education is important for making world more skilled and competitive to human needs?

#1. It would add lot of value to the over and above university system. For courses where less student comes up as they are difficult..this is right platform since u can find even 5 student each country will make it class of 1000 students who are really interested.

#2https://sandyclassic.wordpress.com/2013/02/17/countries-adopting-e-learning-will-win-next-war-of-education-and-competitiveness/
#3. If leader board of people which highest score Quiz wise and final Exam it would motivate people lot to score more and take challenging assignments.
quiz questions should be structured in  a way:
1. 50% conceptual
2. 30% hard
3, 20% very hard
And people can see leader board like games with points

#4 come to discussion about really puzzling questions for real mastery certificate. Some R&D based questions.

#5. In video Quiz can help to rank and profile student its huge data of value in hand of Online Education providers. Lots of analytics can be used to show 3D map or cloud of topics successfully covered in first attempt, second attempt etc..etc… or combined all.

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.

Bigdata,cloud , business Intelligence and Analytics

There huge amount of data being generated by BigData Chractersized by 3V (Variety,Volume,Velocity) of different variety (audio, video, text, ) huge volumes (large video feeds, audio feeds etc), and velocity ( rapid change in data , and rapid changes in new delta data being large than existing data each day…) Like facebook keep special software which keep latest data feeds posts on first layer storage server Memcached (memory caching) server bandwidth so that its not clogged and fetched quickly and posted in real time speed the old archive data stored not in front storage servers but second layer of the servers.
Bigdata 3V characteristic data likewise stored in huge (Storage Area Network) SAN of cloud storage can be controlled by IAAS (infrastucture as service) component software like Eucalyptus to create public or private cloud. PAAS (platform as service) provide platform API to control package and integrate to other components using code. while SAAS provide seamless Integration.
Now Bigdata stored in cloud can analyzed using hardtop clusters using business Intelligence and Analytic Software.
Datawahouse DW: in RDBMS database to in Hadoop Hive. Using ETL tools (like Informatica, datastage , SSIS) data can be fetched operational systems into data ware house either Hive  for unstructured data or RDBMS for more structured data.

BI over cloud DW: BI can create very user friendly intuitive reports by giving user access to layer of SQL generating software layer called semantic layer which can generate SQL queries on fly depending on what user drag and drop. This like noSQL and HIVE help in analyzing unstructured data faster like data of social media long text, sentences, video feeds.At same time due to parallelism in Hadoop clusters and use of map reduce algorithm the calculations and processing can be lot quicker..which is fulling the Entry of Hadoop and cloud there.
Analytics and data mining is expension to BI. The social media data mostly being unstructured and hence cannot be analysed without categorization and hence quantification then running other algorithm for analysis..hence Analytics is the only way to get meaning from terabyte of data being populated in social media sites each day.

Even simple assumptions like test of hypothesis cannot be done with analytics on the vast unstructured data without using Analytics. Analytics differentiate itself from datawarehouse as it require much lower granularity data..or like base/raw data..which is were traditional warehouses differ. some provide a workaround by having a staging datawarehouse but still  data storage here has limits and its only possible for structured data. So traditional datawarehouse solution is not fit in new 3V data analysis. here new Hadoop take position with Hive and HBase and noSQL and mining with mahout.