Cloudera Distribution Hadoop (CDH) vs. IBM Analytics Engine

Cloudera Distribution Hadoop (CDH) does a lot of things really well - especially on the analytical front. That being said the product is quite expensive. There are seemingly numerous applications that do the same thing on the functional level that are much more cost effecient for enterprise teams. If I were recommending this to a colleague I would let them know the product will absolutely be able to get the job done for their use case, but there are more efficient options

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

• Solid and robust set of integrations
• Easy to use and easy to deploy across the enterprise
• Reliability - never lost any info
• Simple and clean interface

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• 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.

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• The price is quite high competitively speaking
• Hard to learn more robust functions and custom options without experience

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• 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

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In terms of functionality there's not much difference, both get the job done. Amazon was more cost-efficient for our team, but this could vary depending on the size of the business. One thing I did notice was that Cloudera seemed to management and spit out our deployments faster than AWS.

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

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• Saves time by automating typically manual processes (data management, lifecyle AI etc)
• Quick deployments and analytics allow for faster time-to-value

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• 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