Dataiku vs. IBM Cloud Pak for Data

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

IBM Cloud Pak for Data with Netezza is well suited for clients who require fast, economical analytics processing. It is not designed to be used as a transactional processing environment. For example, a large customer is using it during the point of sale process. That makes little sense in that business case. However, to take analysis to market faster, it excels well in that space.

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

• I really like the AI and ML which enables us to source data in different sources for easy data-driven decisions.
• It's a cloud tool that keeps all our data safe, backed up ahs obtainable at any time without being exposed to any kind of risks or loss.
• I like the fact that ICP is main based on open source stack which adds value to products like VA or MCM.
• IBM support service is great and top-class.

Incentivized

• End product deployment.

Incentivized

• This offering is currently available on prem, in Azure, and soon in AWS. GCP availability will be in the future as there is demand in the marketplace
• The on premise offering starts with a Base + 0 model, which is a significant appliance. There are no 'mini' offerings as there were in the past.
• At Destiny, we work closely with IBM to help our clients perform budgetary planning.

Incentivized

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

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.

Incentivized

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.

Incentivized

Generally this tool has been very helpful and innovative because increase our workflow and collaboration using integrated multi-cloud platform. It also enables us to deploy in any flexible way like on-premises or cloud which saves time and hard disk space. It also enables us to connect, catalog, govern, transform and analyze data regardless of the area.

Incentivized

• 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

• IBM Cloud helps us to manage data speed across every distributed stores and clouds.
• Acts as a single unified tool which brings all our data in one place where it's safe and easy to access.
• Enables all of our data users to collaborate from a single, unified interface that supports many services that are designed to work seamless.

Incentivized