IBM Watson Studio on Cloud Pak for Data vs. Splunk Enterprise

It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.

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Pros: Splunk is very well suited if you have multiple log sources of related data. All of them can be correlated and tasks can be automated based on the requirement. Other than alerts, Splunk can also run a specific script of your choice, based on some defined conditions. Cons: If you have a few logs but a large number of log sources, Splunk can be very expensive.

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• Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
• SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
• Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
• Estimator validation lets data scientists test and prove different models.

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• Real-time + Scheduled alerts - i-e you can set up alerts which are actively monitoring your logs
• Pretty good response time for search results. With our key/value logging, Splunk makes it blazing fast to query the data.
• Dashboards provide insights into historical data
• Love how Splunk indexes all of the data and provides keys to search on

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• The cost is steep and so only companies with resources can afford it
• It will be nice to have Chinese versions so that Chinese engineers can also use it easily
• It takes a while to learn how to input different kinds of skin defects for detection

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• At times some queries can run slowly if indices are not on a portion of the query you use.
• Setup time initially can be difficult if your logs aren't stored in common locations or in a common way to write the log.
• Ability to ingest logs from different locations without having to change code to put logs in a certain place (pro and con).
• Searches can be a bit more difficult to look through if your log isn't pulled in a manner that is easy to read through splunk.

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because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)

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We are using Splunk extensively in our projects and we have recently upgraded to Splunk version 6.0 which is quite efficient and giving expected results. We keep track of updates and new features Splunk introduces periodically and try to introduce those features in our day to day activities for improvement in our reporting system and other tasks.

The UI flawlessly merges this offering by providing a neat, minimal, responsive interface

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You can literally throw in a single word into Splunk and it will pull back all instances of that word across all of your logs for the time span you select (provided you have permission to see that data). We have several users who have taken a few of the free courses from Splunk that are able to pull data out of it everyday with little help at all.

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From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected

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When properly setup and configured, Splunk is extremely reliable.

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Never had slow response even on our very busy network

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I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path

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Splunk maintains a well resourced support system that has been consistent since we purchased the product. They help out in a timely manner and provide expert level information as needed. We typically open cases online and communicate when possible via e-mail and are able to resolve most issues with that method.

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The trainers on the job are very smart with solutions and very able in teaching

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The Platform is very handy and suggests further steps according my previous interests

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The online course was simple clear and described the main capabilities of the solution. There is also an initial module that can be done for free so anyone can familiarize themselves with the functionality of this solution. On the other hand, however, there could be more free online courses. Maybe even with a certificate, this would broaden the group of people who are familiar with the platform while increasing familiarity with the solution itself.

It surprised us with unpredictable case of use and brand new points of view

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Smooth without too many major issues.

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The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.

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I wanted to learn a new language that I can quickly master and implement. Splunk is easy, fun to use and best of all, it can be developed in hours not days or weeks. Splunk is fundamentally a programming language that is minimal but yet powerful enough to collect, analyze and visualize data.

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It helped us in getting from 0 to DSX without getting lost

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Splunk can scale in to the petabyte per day range which of course is awesome

• Could instantly show data driven insights to drive 20% incremental revenue over existing results
• Still don't have a real use case for unstructured data like twitter feed
• Some of the insights around user actions have driven new projects to automate mundane tasks

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• Overall very positive. It has provided visibility to what is going on within our network.
• One drawback is the time it takes to get up to speed with the application, but this is up to the user, and Splunk education is excellent.
• In my field, IT Security, there are few other friends to have in your back pocket better than Splunk. They are just that good.

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