Dataiku vs. Looker

Dataiku is an awesome tool for data scientists. It really makes our lives easier. It is also really good for non technical users to see and follow along with the process. I do think that people can fall into the trap of using it without any knowledge at all because so much is automated, but I dont think that is the fault of Dataiku.

Incentivized

When data drives potential for new orders, Looker earns its place in our tech stack. If, on the other hand, we are hoping for pipeline generation, Looker is useful if you are willing to repeatedly go check customer utilizations .... it is not appropriate if you are hoping to automate data analysis for this purpose.

Incentivized

• Allows users to collaborate and monitor individual tasks
• Caters to both types of analysts, coders and non-coders, alike
• Integrate graphs and plots with visualization tools such as Tableau

Incentivized

• Show visited pages - sessions, pageviews - which programs are viewed the most.
• Displays session source/medium views to see where users are coming from.
• It shows the video titles, URLs, and event counts so we can monitor the performance of our videos.
• It gives a graphic face to the numbers, such as using bar charts, pie graphs, and other charts to show user trends or which channels are driving engagement.
• Our clients like to see the top pages visited for a month.
• I like the drop-and-drag approach, and building charts is a little easier than it was before.

Incentivized

• The integrated windows of frontend and backend in web applications make it cumbersome for the developer.
• When dealing with multiple data flows, it becomes really confusing, though they have introduced a feature (Zones) to cater to this issue.
• Bundling, exporting, and importing projects sometimes create issues related to code environment. If the code environment is not available, at least the schema of the flow we should be able to import should be.

Incentivized

• Documentation is scarce, and very difficult to find when you need it.
• Pricing is unclear, particularly as you look to scale your reports across the business.
• Data from other sources is not represented in the system as well as first party Google services.

Incentivized

I give it this rating because it deems as effective, I am able to complete majority of my tasks using this app. It is very helpful when analyzing the data provided and shown in the app and it's just overall a great app for Operational use, despite the small hiccups it has (live data).

Incentivized

The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level

Incentivized

Looker is relatively easy to use, even as it is set up. The customers for the front-end only have issues with the initial setup for looker ml creations. Other "looks" are relatively easy to set up, depending on the ETL and the data which is coming into Looker on a regular basis.

Incentivized

No objections

Incentivized

Somehow resources heavy, both on server and client. I recommned at least 50Mbs data rate and high performance desktop comouter to be abke to run comolex tasks and configure larger amount of data. On the other hand, the client does not need to worry when viewing, the performance is usually ok

Incentivized

The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.

Incentivized

Never had to work with support for issues. Any questions we had, they would respond promptly and clearly. The one-time setup was easy, by reading documentation. If the feature is not supported, they will add a feature request. In this case, LDAP support was requested over OKTA. They are looking into it.

Incentivized

Very satisfied, easy to implement

Incentivized

Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by even other kinds of users.

Incentivized

Looker Studio, you can easily report on data from various sources without programming. Looker Studio is available at no charge for creators and report viewers. Enterprise customers who upgrade to Looker Studio Pro will receive support and expanded administrative features, including team content management. So it's good.

Incentivized

Perfect price to performance

Incentivized

• Customer satisfaction
• Timely project delivery

Incentivized

• Looker has a poignant impact on our business's ROI objectives. As an advertising exchange we have specific goals for daily requests and fill, and having premade Looks to monitor this is an integral piece of our operational capability
• To facilitate an efficient monthly billing cycle in our organization, Looker is essential to track estimated revenue and impression delivery by publisher. Without the Looks we have set up, we would spend considerably more time and effort segmenting revenue by vertical.
• Looker's unique value proposition is making analytical tools more digestible to people without conventional analytical experience. Other competing tools like Tableau require considerably more training and context to successfully use, and the ability to easily plot different visualizations is one of its greatest selling points.

Incentivized