Unilever is getting the source data from different source systems. As part of the same business each customer would be offered with different types of products based on their needs. Customer might have different retail type of products etc. To maintain this much of huge volumes of, different varieties of data in traditional databases is a very tedious process. To meet the scaling needs of data ofUnilever, re-plat forming of current data warehouse system to hadoop solution in a cost effective solution.

T-Mobile is getting the source data from different source systems. As part of the same business each customer would be offered with different types of products based on their needs. Customer might have retail type of products etc. To maintain this much of huge volumes of, different varieties of data in traditional databases is a very tedious process. To meet the scaling needs of data of T-Mobile, re-plat forming of current data warehouse system to hadoop solution in a cost effective solution

This Project is all about the rehousting of their (Best Buy) current existing project into Hadoop platform. Previously Best Buy was using MySql DB for storing their competitor’s retailer’s information. [The Crawled web data]. Early Target use to have only 4 competitor retailers namely Amazon.com, walmart.com etc….

But as and when the competitor retailers are increasing the data generated out of their web crawling is also increased massively and which cannot be accomodable in a mysql kind of data box with the same reason Best Buy wants to move it Hadoop, where exactly we can handle massive amount of data by means of its cluster nodes and also to satisfy the scaling needs of the Best Buy business operation.

The Australian division caters for the bank's retail, commercial and wealth management customers in Australia. The Retail businesses are responsible for delivering a range of banking products and services to retail customers, while Commercial services small to medium enterprises through to smaller corporate. The division also has a dedicated Merchant analytics management business designed to meet the needs of high net worth individuals. This Solution is concerned with the development of a cost-effective Data Warehouse using Hadoop and Hive for storage of large amount of historical data and log data. The raw data will be coming from various sources and dumped directly into Hadoop file system through Sqoop (data extracting tool used to extract data from RDBMS (Oracle, Db2, Teradata, etc.)). Then, data is processed (like un-normalization, partitioning, bucketing, etc.) using hive queries. After that, the data is updated (using customized and optimized queries) into hive and ad-hoc queries can be run to get any form of data.