Data Integration , map Reduce algorithm , virtualisation relation and trends

In year 2011 This reply i did to a discussion. would later structure it into proper article.

As of 2010 data virtualization had begun to advance ETL processing. The application of data virtualization to ETL allowed solving the most common ETL tasks of data migration and application integration for multiple dispersed data sources. So-called Virtual ETL operates with the abstracted representation of the objects or entities gathered from the variety of relational, semi-structured and unstructured data sources. ETL tools can leverage object-oriented modeling and work with entities’ representations persistently stored in a centrally located hub-and-spoke architecture. Such a collection that contains representations of the entities or objects gathered from the data sources for ETL processing is called a metadata repository and it can reside in memory[1] or be made persistent. By using a persistent metadata repository, ETL tools can transition from one-time projects to persistent middleware, performing data harmonization and data profiling consistently and in near-real time.

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– More then colmunar databases i see probalistic databases : link:http://en.wikipedia.org/wiki/Probabilistic_database

probabilistic database is an uncertain database in which the possible worlds have associated probabilities. Probabilistic database management systems are currently an active area of research. “While there are currently no commercial probabilistic database systems, several research prototypes exist…”[1]

Probabilistic databases distinguish between the logical data model and the physical representation of the data much like relational databases do in the ANSI-SPARC Architecture. In probabilistic databases this is even more crucial since such databases have to represent very large numbers of possible worlds, often exponential in the size of one world (a classical database), succinctly.

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For Bigdata analysis the software which is getting popular today is IBM big data analytics
I am writing about this too..already written some possible case study where and how to implement.
Understanding Big data PDF attached.
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There are lot of other vendors which are also moving in products for cloud computing..in next release on SSIS hadoop feed will be available as source.
— Microstraegy and informatica already have it.
— this whole concept is based on mapreduce algorithm from google..There are online tutorials on mapreduce.(ppt attached)
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Without a doubt, data analytics have a powerful new tool with the “map/reduce” development model, which has recently surged in popularity as open source solutions such as Hadoop have helped raise awareness.

Tool: You may be surprised to learn that the map/reduce pattern dates back to pioneering work in the 1980s which originally demonstrated the power of data parallel computing. Having proven its value to accelerate “time to insight,” map/reduce takes many forms and is now being offered in several competing frameworks.

If you are interested in adopting map/reduce within your organization, why not choose the easiest and best performing solution? ScaleOut StateServer’s in-memory data grid offers important advantages, such as industry-leading map/reduce performance and an extremely easy to use programming model that minimizes development time.

Here’s how ScaleOut map/reduce can give your data analysis the ideal map/reduce framework:

Industry-Leading Performance

  • ScaleOut StateServer’s in-memory data grids provide extremely fast data access for map/reduce. This avoids the overhead of staging data from disk and keeps the network from becoming a bottleneck.
  • ScaleOut StateServer eliminates unnecessary data motion by load-balancing the distributed data grid and accessing data in place. This gives your map/reduce consistently fast data access.
  • Automatic parallel speed-up takes full advantage of all servers, processors, and cores.
  • Integrated, easy-to-use APIs enable on-demand analytics; there’s no need to wait for batch jobs.

A Day in Life of Datawarehouse Architect part 1

A data warehouse Architect generally help to design datawarehouse , requirement gathering in ETL Low level design LLD, and HLD high level design, setting up database infrastructure design for datawarehouse like Storage Area Network requirements, Rapid application Clusters for database of datawarehouse more details read
Datawarehousing consists of three main area :
1. ETL(data migration, data cleansing, data scrubbing, data loading )
2. Datawarehouse design
3. Business Intelligence (BI) Reporting infrastructure.
BI
Read These Two part article for BI
– https://sandyclassic.wordpress.com/2014/01/26/a-day-in-life-of-bi-engineer-part-2/
– https://sandyclassic.wordpress.com/2014/01/26/a-day-in-life-of-business-intelligence-engineer/
And Architect
https://sandyclassic.wordpress.com/2014/02/02/a-day-in-life-of-business-intelligence-bi-architect-part-1/

Design : Now Coming to part 2 (is generally work of Data warehouse architect)
Read Some details More would be covered in future articles
https://sandyclassic.wordpress.com/2013/07/02/data-warehousing-business-intelligence-and-cloud-computing/
9:00-9:30 Read and reply mails.
9:30-10:30 Scrum Meeting
10:30-11:30 update documents According to Scrum meeting like burn down chart etc..update all stake holders.
11:30-12:00 Meeting with Client to understand new requirements. create/update design specification from requirement gathered.
12:00-13:30 create HLD/LLD from the required user stories according to customer Landscape of technology used.
13:30-14:00 Lunch Break.
14:00-14:30 Update the Estimations ,coding standards , best practises for project.
14:30-15-30 Code walk through update team on coding standards.
15-30-16:30Defect call with Testing and development Team to understand defects, reasons of defects, scope creep, defect issuse with defect manager, look at issue/defect register
16:30-17:30 Work on specification of Design of datawarehouse modelling Star or Snow flake schema design according to business requirements granularity requirements.
17:30-18:30 Look at Technical Challenges requiring Out of Box thinking, thought leadership issue, Proof of concept of leading Edge and Breeding Edge technologies fitment from project prospective.
18:30-19:30  onwards Code for POC and Look a ways of tweaking , achieving technology POC code.
19:30- 20:30 onwards Forward thinking issue might be faced ahead by using a particular technology is continuous never ending process as there can be multiple combination possible to achieve as well as using particular component or technology should not create vendor lock in, cost issues, make/buy cost decisions, usability, scalability, security issues (like PL/SQL injection, SQL injection using AJAX or web services may be affected by (XSS attack or web services Schema poisoning), Environmental network scalability issues. Affect due to new upcoming technology on Existing code.
20:30 Dinner
Available on Call.. for any deployment, production emergency problems.

A day in Life of datawarehousing Engineer Part-2

Read Previous part
https://sandyclassic.wordpress.com/2014/02/19/a-day-in-life-of-datawarehousing-engineer/
Normal Schedule for development role :
9:00-9:30 Check all mail communications of late night loads Etc.
9:30-10:30 Attend Scrum meeting to discuss update status of completed task mappings and mapping for New user stories requirements, understand big picture of work completed by other staff status.
10:30 am -1:30 pm Look at LLD, HLD to create source to target transformations after understanding business logic and coding that in transformations available with tool.
1:30-2:00 Lunch break
2:00-3:00 Unit test data set to validate as required between source and target.
3:00-3:30 Documentation requirements of completed work.
3:30-4:30 Attend defect Call To look into new defects in code and convey back if defects not acceptable as out of scope or not according to specifications.
4:30-5:00 Status update daily work to Team Lead.
5:00-5:30 sit with Team lead, architect code walk through and update with team.
5:30-6:30 Take up any defects raised in Defect meting and Code walk through.

Coke Vs Pepsi of :Datawarehousing ETL Vs ELT

The Coke and Pepsi are always fighting to have bigger pie in international drinks market.
Both are present in 180+ countries agressively pursuing the pie of market share.

The Datwarehouses are different animals on block. They are databases But they are not normalized. They do not follow all 12 Codd Rules. But yet source and Target are RDBMS.
The Structure Where its saved Whether in Star Schema or Snow-flake is denormalized as possible like flat file structures. More Constraints slows down the join process.
Read more: https://sandyclassic.wordpress.com/2014/01/26/a-day-in-life-of-business-intelligence-engineer/
So there are less restrained much Faster file based alternatives for databases which Emerged for need to store unstructured data and achieve 5V massive volume, variety, velocity etc.. Read below links:
https://sandyclassic.wordpress.com/2013/07/02/data-warehousing-business-intelligence-and-cloud-computing/
Which are have also found favour in ETL world with Hadoop. Now Every ETL allows hadoop connector or adapter to Extract data from hadoop HDFS so service in HDFS and similar.
https://sandyclassic.wordpress.com/2013/06/18/bigdatacloud-business-intelligence-and-analytics/
(
Adapters use-case for  product offering Read:https://sandyclassic.wordpress.com/2014/02/05/design-pattern-in-real-world/)
ETL process

ETL Extract-Transform-Load
ETL where transformation happens in staging area.
Extract data from sources , put in staging area cleanse it, transform data and Then Load in Target Datawarehouse. So popular Tools like informatica, datastage or ab-initio use this approach. Like in Informatica for fetching data or Extract Phase we can use fast source-qualifier transformation OR use Joiner transformation when we have multiple different databases like both SQL Server and Oracle although may be slow but can take both input but Source qualifier may require single vendor but is fast.
After Extracting We can use Filter transformation to filter out unwanted rows in staging area. Then load into target Databases.

ELT Extract Load and then Transform.
Extract data from disparate sources , Load the data into RDBMS engine first after . Then use RDBMS facility to Cleanse and Transform Data. This Approach was popularised By Oracle because Oracle Already had Database Intellectual property and was motivated to increase its usage.So Why does cleansing and Transformation outside the RDBMS into staging area rather within RDBMS engine. Oracle ODI Oracle Data integrator uses this concept of ELT not ETL bit reversal from routine.

So like Pepsi Vs Cola wave of Advertisement and gorilla marketing or To showcase Each other products strengths and hide weakness Games continue here Also in ETL world of data warehousing. Each one has its own merits and demerits.

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 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.