Dataiku vs. Sigma Computing

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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Scenarios where Sigma Computing is well suited: - Data Reporting and Visualisation : It is suitable for dashboards that integrate data from multiple back-office systems - Search and Filtering Capabilities: It provides a robust platform for searching through datasets and visualisations. Scenarios where Sigma Computing is less appropriate: Handling of null values and dynamic table adjustments

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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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• Allows end users to easily dive into the data without having direct access to the table in our database management software.
• Can easily turnaround dashboards that are detailed and visually pleasing.
• Sigma is intuitive and as new features are rolled out it is easy to adopt and incorporate them into new and existing dashboards.

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• End product deployment.

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• Viewer level license is quite limited. These users can't download data or even add filters on datasets. Something to keep in mind.
• Directly querying the underlying data warehouse will lead to increased usage. Not a big deal on something like Redshift, but your Snowflake consumption will increase, potentially by a lot.

Sigma has helped us a lot and has become an integral part of our daily workflow. It would be difficult to switch to another platform and have to rebuild the numerous metrics and performance reports that we have already established

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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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It has a clean and modern interface. However, it is not completely intuitive. I think it would be better and easier to navigate with more Windows style drop down menus and/or tabls. There is a significant learning curve, but that may be due in part to the technical nature of this type of software tool.

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Yes, as long as you don’t conduct user error sigma is always up and running and waiting for you to complete your dashboards

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It depends, it loads quickly for smaller dashboards but when loading larger amounts of data it takes more time to do so

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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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They are very friendly and informative. They are quick in resolving our queries and help us understand very minute things as well. They are quick in creating feature tickets based on our custom requirements, and they would also create a bug ticket if there is any discrepancy and get that checked on time.

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Was not involved in implementation

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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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Sigma is by far the best. It is easiest to learn and easiest to use on a day to day basis. I never have to wait for dashboards to load and it's very easy to understand the variables that are going into my visualizations. Best of all I can manipulate the data within Sigma very easily. In these other platforms data manipulation is difficult or must be done in the data warehouse

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It is a cloud service offering that is able to expand based on your usage

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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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• Monitoring health of cloud platform has allowed the company to anticipate issues before they affect customers – Sigma prompted us building a canary monitoring process that provides customer container health.
• Customer success has used an activity report to discover customers running runaway processes that they were unaware of, creating an alert to contact the customer and prevent an embarrassing situation.
• Customer success uses the activity report to prompt conversations regarding increases or declines in behavior that led to increasing contract limits or addressing churn concerns.

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