HPE Ezmeral Data Fabric Review
It provides bundled services and UI driven configurations which would otherwise take a long time to understand and implement, but the setups are still a bit cryptic and can be improved.
The fact that the heavy computation is required on Big Data can be distributed across many nodes in a cluster, makes this solution a winner. Of course the same concept is available across any solution based on the Hadoop architecture, but MapR provides bundled services and UI driven configurations which would otherwise take a long time to understand and implement.
Improvements to My Organization:
We implemented this for our client where data from sensors was to be analyzed and sense to be made of this data. Sensor data is huge, and earlier there was no meaningful early warning raised against the discrepancies observed on Sensor’s Data. With the help of our Big Data solution, we have been consistently raising alerts as required.
Room for Improvement:
- Installations and setups are still a bit cryptic and can be improved.
- Skilled resource base in Big Data Tools is generally low and hence project costing is that much higher.
Please make sure following answers are clearly known:
- Define the Objectives, and what you are not currently able to achieve without Big Data Tools.
- Why is the data big - because of velocity of data pouring in. th number of years of data, or a sudden increase in business operations
- Define a sample use case / example of the expectations from Big Data analytics
- Carry out a POC & review the objectives.
**Disclosure: I am a real user, and this review is based on my own experience and opinions.
Last updated: Oct 06 2016
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