Big Data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make your business more agile, and to answer questions that were previously considered beyond your reach. Until now, there was no practical way to harvest this opportunity. Today, IBM's platform for big data uses state of the art technologies including patented advanced analytics to open the door to a world of possibilities.
DATA WAREHOUSE AUGMENTATION
Data Warehouse Augmentation is about building on an existing data warehouse infrastructure, leveraging big data technologies to ‘augment’ its capabilities. There are three key types of Data Warehouse Augmentation:
Pre-Processing - using big data capabilities as a “landing zone” before determining what data should be moved to the data warehouse
Offloading - moving infrequently accessed data from data warehouses into enterprise-grade Hadoop
Exploration - using big data capabilities to explore and discover new high value data from massive amounts of raw data and free up the data warehouse for more structured, deep analytics.
ANNOUNCING BLOGFERENCE CONTEST!!
From either your experience or situations you have come across/heard-of, come up with a Data Warehouse Augmentation use-case or business scenario, one that requires analytics on Hadoop storage using SQL (like InfoSphere BigInsights Big SQL )
Describe the high level business use case and existing architecture if any.
Describe the need for Big Data based analytics ; the need for warehouse augmentation using Hadoop and type of augmentation needed.
Describe the challenges faced in adopting Hadoop to the enterprise and proposed high level solution approach
Along with evaluation by our panelists, most views, likes and comments on the blog carries weightage! The most highly rated Blog wins an exciting prize!!!
HOW DO I PARTICIPATE?
Write Up can be upto a length of 1500 words (roughly 2 pages in Word Document), Use Pictures/Diagrams to explain the use case better.
- Step 1: Join & login on IBM Big Data India community
Step 2: Get your Write Up published on any website or post it on any blog of your choice and post it as New Entry (You will see it under Blog Actions on the top right corner.
Visit the Community BLOG section and click on New Entry and directly post the contents
- Step 3: Ensure to title the blog as, " Bigdata Post: <TITLE>"
- Step 4: Tell your friends to recommend, like your Blog and participate!
THIS ACTIVITY ENDS On Oct 28th, 2013. Please read the Terms & Conditions before participating.
EXAMPLE WAREHOUSE OFF-LOADING SCENARIO
Highlights of one such use-case surrounding Warehouse Offloading in combination with Click Stream Analysis and/or Market Basket Analysis is described below.
- A retail chain company currently uses conventional web log analytics, warehouse and BI tools and techniques to analyze customer behaviour patterns.
- The analysis of customers' purchase history and online browsing patterns - broadly known as Market Basket Analysis & Click Stream Analysis helps the retail chain to gain insights which allow them to plan effective cross-sell/up-sell campaigns, promotional combination offers and discount offers - leading to overall increase in customer satisfaction.
- As the customer base increased from 10000 to 2 million over the last 10 years, it was getting increasingly difficult to scale the infrastructure of warehouse - both for technical and monetary reasons.
- The proposed revised architecture envisioned adopting Hadoop into the enterprise. "Cold" data or historical data was offloaded to Hadoop as HBase or Hive tables.
- Web Log Analytics can be performed on Hadoop using InfoSphere BigInsights component Machine Data Accelerator (MDA).
- Using InfoSphere BigInsights Big SQL, existing BI tools can continue to run its reports for Market Basket Analytics without any change to its interface to DWH.
- Download InfoSphere BigInsights Quick Start Edition
- Big SQL Technology on a Cloud
- InfoSphere BigInsights Big SQL - Info Center