Altair SLC vs. IBM Analytics Engine

For now coders, I would emphasize them to use drag and drop functionality. Otherwise, WPS Analytics is a robust tool for app development and data visualization that offers multiple languages such as SAS, SQL, R, and Python.

Incentivized

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

• Data preparation and advanced analytics help us to gain insights on company data.
• Data sharing and distributing securely.
• Visualization of data with WPS Analytics saves time when making business decisions.

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

• SAS functionality has to lack.
• There's no online guideline, making it hard to solve problems.

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

To better understand the data I work with, I extensively use the data discovery and visualization tools available on my workstations, and I always go for the best. Unfortunately, some other tools does not offer any tutorials for the features, making it difficult and time-consuming to analyze data, mainly after it has been implemented. In addition to videos and articles, WPS Analytics provides comprehensive tutorials for each feature.

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

• Reduction in operational costs since no data scientists are involved.
• Time and effort have been reduced when analyzing data by using machine learning capabilities and natural language.
• We have reduced time to value by bridging data processing to taking actions gap.

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