Dataiku vs. IBM ILOG CPLEX Optimization Studio

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

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

It is well suited for solving large-sized, mixed-integer, and integer programming problems. Now, the new version supports for Multi-Objective optimization along with some new algorithms such as Benders Decomposition. It is less appropriate for quadratic programming problems where the objective function is the product of multiple variables. However, it's very easy to code any problem.

• 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

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Linear Programming
• Mixed-Integer Linear Programming
• Non-Linear Convex-Optimization
• Visualization
• Shadow Price Analysis
• Parameter Tuning

• End product deployment.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Data handling from different sources like Note Pad, etc.
• Large size of MILP problems.
• Various parameters to set.

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

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

It's nice to use and with good optimization.

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

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

Honestly, to say, I never contacted CPLEX but used its forum to know/clarify any issues I faced.

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

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

IBM CPLEX Optimization Studio covers wide range of problems in comparison to Gurobi and also offers a number of visualization tools for results analysis. It has better customization and parameter tuning options in comparison to Gurobi. It offers various API integrations such as Python, Java and C++ which is not the case with Gurobi.

• 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

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Faster computation leading to better internal customer relations
• Able to solve high variable problems with ease
• Anomaly detection became easier within business