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: http://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:
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.
http://sandyclassic.wordpress.com/2013/06/18/bigdatacloud-business-intelligence-and-analytics/
(Adapters use-case for product offering Read:http://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 inInformatica for fetching data or Extract Phase we can use fastsource-qualifier transformation OR use Joiner transformationwhen 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. http://sandyclassic.wordpress.com/2013/07/02/data-warehousing-business-intelligence-and-cloud-computing/
2. http://sandyclassic.wordpress.com/2013/06/18/bigdatacloud-business-intelligence-and-analytics/
Cloud Computing and Unstructured Data Analysis Using
Apache Hadoop Hive
Read: http://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:
http://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.
http://sandyclassic.wordpress.com/2012/10/23/future-cloud-will-convergence-bisoaapp-dev-and-security/
Datawarehouse View 