IBM Analytics Engine vs. Kognitio

We are at present utilizing IBM Analytics Engine and it works incredible. Following are the things that I like the most about this product is:- - Simple to Utilize - Reasonable Cost - With only a couple seconds you can ready to fabricate and convey groups - you can without much of a stretch break down information through different applications

Incentivized

What I like most is the IN Memory capability it is doing, as we all know RAM is faster than disk. The capability to connect different databases. Beginners should also take note of the Data Disk Management because anytime it could go wrong, you should have experience in dealing with this kind of event.

Incentivized

• We are able to build and deploy clusters within minutes to simplify user experience and increase scalability and reliability.
• We are able to scale and compute on-demand to handle newer workloads like machine learning.
• We really like that we are able to access and administer the application via multiple interfaces.

Incentivized

• Ultra fast query results.
• IN Memory Database.
• Easy integration to reporting services.

Incentivized

• I would like to see a more robust version of their online help
• The speed of their business support is adequate, but I kind of expect more from such a powerhouse.
• Problems with duration of cluster life

Incentivized

• Problems Could Be Encountered is particularly pronounced in more complex analyses.
• Categorical variables are often not precise enough

Incentivized

• I have been using Azure for my previous analysis, I had a difficult time in understanding the Analytics engine rather IBM provided step by step tutorial for setup.
• Also turning off a machine was not an option in Azure for some of the services so I had to pay for the service whether I use it or not

Incentivized

The understandable and complete tables and graphs, the cleaning methods and the way of encrypting the data are quite feasible, which does not help to prepare our data, it helps that the data that is thrown as results is separated from each other, the process prior to structuring requires high-level advice and is somewhat time-consuming, there is a risk that they overwrite the data themselves by accident at a later time

Incentivized

• It has saved us quite a bit of time managing our catalog of clusters and keeping things organized.
• Since we had a division we acquired running IBM Cloud, it was easy to get it running and try it out, but we found we prefer our Azure configuration better simply to keep our technology in alignment across corporate functions.
• I definitely see some cost savings by separating out the storage and compute. It helps you start to put an appropriate price tag on certain instances of big data.

Incentivized

• The implementation of the formats to integrate the users we have and the program is also good.
• I also improve the control of aspects related to the work environment

Incentivized