The Dataiku platform unifies all data work, from analytics to Generative AI. It can modernize enterprise analytics and accelerate time to insights with visual, cloud-based tooling for data preparation, visualization, and workflow automation.
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Splunk Cloud Platform
Score 8.3 out of 10
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Splunk Cloud Platform is a data platform service thats help users search, analyze, visualize and act on data. The service can go live in as little as two days, and with an IT backend managed by Splunk experts.
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.
Splunk is excellent when all your data is in one location. Its ability to correlate all that data is intuitive (once the hurdle of learning the query language is overcome). It is also easy to standardize the presentation of information to the company. When data is siloed/standalone, other systems can be cheaper and faster to implement.
This SIEM consolidates multiple data points and offers several features and benefits, creating custom dashboards and managing alert workflows.
Splunk Cloud provides a simple way to have a central monitoring and security solution. Though it does not have a huge learning curve, you should spend some time learning the basics.
Splunk Cloud enables me to create and schedule statistical reports on network use for Management.
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.
Once you hit a certain threshold of automated processes via whatever tool you are choosing (or multiple tools), you really cannot go around a monitoring solution like Splunk Cloud Platform. I have seen many efforts to automate monitoring inside of the automation tools themselve, which does not only block resources but you cannot monitor a system by the system itself. Splunk Cloud Platform has really made us clear about that.
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.
Splunk Cloud support is sorely lacking unfortunately. The portal where you submit tickets is not very good and is lacking polish. Tickets are left for days without any updates and when chased it is only sometimes you get a reply back. I get the feeling the support team are very understaffed and have far too much going on. From what I know, Splunk is aware of this and seem to be trying to remedy it.
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.
Splunk Cloud blows Sumo Logic out of the water. The experience is night and day. We went from several highly stressed IT security professionals who were unsure if the data they were getting was valuable, to very happy IT security professionals who can now be more proactive and get all the information they need.
