Modes of Big Data Analysis

We can look at analysis in three modes based on trigger for analysis.

• Offline/Batch Mode
• Analytics performed and results are made available for applications to use
• Ex: Clinical Trails, Voice of Customer
• Real Time – OnDemand
• Analysis done and results are presented when requested.
• Ex: Up-sell/Cross-sell
• Real Time – Stream based
• Monitor streaming data (Twitter messages, Transaction logs, data from Sensors) and trigger analysis based on event/data.
• Ex: Monitor and analyze online transactions for Fraud, Monitor social media messages for serious incidents.

 



And below are the implementation approaches:

• Massive Parallel Programming (Data Bases and Programming)Hadoop MapReduce
• Scalable Database – NoSQL databases and Databases with ability to store huge data (Ex Oracle ExaData) and to perform operations on data.
• In-memory Analytics - an approach to querying data when it resides in random access memory (RAM), as opposed to querying data that is stored on physical disks.
• Big Data Appliance - combination hardware and software products designed specifically for analytical processing.
• Processing in Memory (PIM) - a chip architecture in which the processor is integrated into a memory chip to reduce latency.
• In-Database Analytics - a technology that allows data processing to be conducted within the database by building analytic logic into the database itself.
• Real-time Stream Processing & CEP

Combination of above approaches need to implement the Analytic Apps

 

Almost 2 years back, for couple of months, I had my first stint with Big data and Hadoop before moving on to Social Analytics. As I resumed my interest into Big data I was looking at my old work and above are from one of my early presentations.