Dataiku vs. Fivetran

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

Fivetran's business model justifies the use-case where we require data from a single source basically a lot of data but if the requirement is not on the heavier side, Fivetran comes to costly operation when compared to its peers. Otherwise, I'll recommend Fivetran for stability and update and seamless service provider.

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

• Easily connects to source data using delivered connectors
• Transforms data into standard models and schemas
• Has very good documentation to help quickly setup connectors

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

• Very difficult to get connectors enhanced if a specific needed object is not supported by them
• Depending on the edition needed and the data volumes, can get quite expensive

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

Very easy and intuitive to setup and maintain as there usually are not that many options. Very well documented (e.g. how to setup each connector, how the schema looks like, any specific features of this connector etc.). Also the operation is intuitive, e.g. you have status pages, log pages, configuration pages etc. for each connector.

Incentivized

It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.

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

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

We never seriously considered using anything else. Our data engineers had used Fivetran extensively in previous roles so when it came time to make a decision, there wasn't much of a process. They gladly signed the contract with Fivetran pretty quickly.

Incentivized

• Customer satisfaction
• Timely project delivery

Incentivized

• It has been very positive in serving BI team with new source requests
• It has been OK at scaling to match as data volumes as source data size grows

Incentivized