Dataiku vs. Splunk Enterprise

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

Incentivized

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.

Incentivized

• The intuitiveness of this tool is very good.
• Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
• The way you can control things, the set of APIs gives a lot of flexibility to a developer.

Incentivized

• 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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• End product deployment.

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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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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.

As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

Incentivized

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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When properly setup and configured, Splunk is extremely reliable.

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The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.

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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 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.

Smooth without too many major issues.

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Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

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

• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

Incentivized

• 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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