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 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.
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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.
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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!
Interested in learning Cassandra, go through our blog on Cassandra Data Model.