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.
