Differentiation between Operational vs. Analytical Systems

OperationalAnalytical
Latency1 ms to 100 ms1 min to 100 min
Concurrency1000 to100,0001 to 10
Access PatternWrites and ReadsReads
QueriesSelectiveUnselective
Data ScopeOperationalRetrospective
End UserCustomerData Scientist
TechnologyNoSQL DatabaseMapReduce, MPP Database

Traditional Enterprise Approach

This approach of enterprise will use a computer to store and process big data. For storage purpose is available of their choice of database vendors such as Oracle, IBM, etc. The user interacts with the application, which executes data storage and analysis.

Limitation

This approach are good for those applications which require low storage, processing and database capabilities, but when it comes to dealing with large  amounts of scalable data, it imposes a bottleneck.

Watch this video on ‘Hadoop Tutorial for Beginners’:

Big Data Solutions Differentiation between Operational vs. Analytical Systems Operational Analytical Latency 1 ms to 100 ms 1 min to 100 min Concurrency 1000 to100,000 1 to 10 Access Pattern Writes and Reads Reads Queries Selective Unselective Data Scope Operational Retrospective End User Customer Data Scientist Technology NoSQL Database MapReduce, MPP Database Traditional

Solution

Google solved this problem using an algorithm based on MapReduce. This algorithm divides the task into small parts or units and assigns them to multiple computers, and intermediate results together integrated results in the desired results. Intellipaat’s Big Data Hadoop training will really help you get a better understanding the concepts of Big Data Solutions in Open Data Platform!

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