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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Sigma
Score 9.1 out of 10
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Sigma Computing headquartered in San Francisco provides a suite of data services such as code free data modeling, data search and explorating, and related BI and data visualization services.
Sigma beats them all in terms of ease of use and interface. Tableau is more customizable than Sigma, particularly with custom graphics. Sigma is far more feature-rich than Metabase as a basic reporting tool. Sigma makes PowerBI look like a 1980s desktop tool. Dataiku is more …
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.
We were able to set up client-facing embedded reports with ease and security. The interface is not difficult to learn, although we may not be aware of or lack the necessary expertise to utilize more advanced features that would likely benefit us.
Sigma Computing does not allow custom ordering of pivot fields in pivot tables easily
Sigma Computing lacks functionality for creating tables or sections that dynamically adjust to the browser window's height while maintaining a fixed height textbox at the bottom
Sigma Computing does not provide straightforward options for formatting totals in tables, such as renaming 'Total' to 'Average', 'Team Total', etc
Sigma Computing does not support searching by individual tab names within a workbook
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
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.
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.
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.
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.
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.
With Looker, to be effective, a substantial amount of coding & modeling needs to happen in LookML. Being another language to learn, users have to context switch again from at a minimum either SQL or Python into LookML. The concept of being able to source control, code review, and deploy your models is a plus though.
Tableau is the gold standard for data visualization, no question. Power users will be able to create dazzling content that Sigma won't necessarily be able to easily match. However, since development usually happens via an extract, helping other users troubleshoot is an arduous process. Trying to re-do or un-do all the transformations and calculations that cause a certain number is very difficult.
With Sigma, all the queries happen directly against Snowflake and you can see the query logs. The data modeling happens right in a tabular, spreadsheet-like manner, so within only a few minutes, substantial transformations can happen, with visualizations just a few more clicks away.
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.
