Altair Monarch vs. Dataiku

The product is especially useful when you have real-time and/or time series data to analyze. If you have more mundane, simpler requirements, other products might do the job you need for less money (there are even some decent open source visualization tools you can find.) I know the product is very widely used in capital markets applications to monitor and analyze risk and price and volume changes; if you're working in that area, I don't think there's a better tool to use.

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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.

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• Creating a basic model to extract data from a report is very easy.
• Advanced features like Calculated Fields and External Lookups allow you to augment the raw data.
• You can create a "project" to automate the data extraction. Combined with Datapump (a separate DW app), you can fully automate the process once the raw report is generated.

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• 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.

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• Recently, we had some major sticker-shock when we wanted to upgrade Data Pump. It is an exceptional product, but when the price jumped from $6,000 to over $60,000, it was impossible to get the funds approved internally for the upgrade.
• We also paid for yearly maintenance contracts which included Professional Services, but rarely found those services beneficial. However, we did receive all software upgrades for Datapump as part of the contract which we found to be very beneficial. However, with the new pricing, that is not longer the case.

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• End product deployment.

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Even though we do not utilize it on a daily basis, I do hope my current company renews it's license. If not, I intend to purchase myself.

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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.

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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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Datawatch is very good value of money compared to QlikView; QlikView is really more of a BI tool and has a lot of functions that I didn't need. Datawatch is very strong in the real-time area where Tableau, Panorama, and Qlik don't do very well. If you need to set up a visual monitoring dashboard, Datawatch is the best product I've seen for that. if you want to do a lot of in depth statistical analysis of large databases, Tableau is probably a good option.

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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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• Data Pump reduces complexity of report solutions by offering a standardized approach for organizing and scheduling
• 97% service level for past 5 years for all of our jobs going through Data Pump

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• 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.

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