Over the past several years, many technologies have emerged to help those building analytics projects do more with their data. Tools like Alteryx have helped users change the way they work for the better by making analytics accessible to more people.
In fact, I used Alteryx every day for years. But I’ve spent the last few here at Dataiku helping companies realize the promise of modern analytics. Dataiku marries the same ease of use users expect with:
- Cloud-first scale to tackle any data
- A focus on collaboration and reuse to save time
- Flexible guardrails to automate safely
- New insights through machine learning and Generative AI
In this blog series, I’ll share my path to help new and experienced Dataiku users alike map familiar concepts in Alteryx to how Dataiku does things.
At the end of the day, I hope you’ll accelerate your own journey with Dataiku and see how a cloud-first approach built on governed collaboration can take you from data prep to advanced analytics and beyond.
What's It Like in Alteryx: The Workflow
In Alteryx, your workflow is your key asset. It’s where you drag out tools to point at, explore, and transform your data. Each individual step is usually its own particular icon — whether you’re aggregating some transactions, looking at data quality, or adding some documentation.
If you’d like someone else to review and understand your flow, most commonly they’ll be downloading that file from a network drive or Alteryx Server to open in their own local Alteryx Designer. Same idea if you’d like to reuse some logic in a new process or understand where something may have broken down in the last overnight run.
💡Explore product features in the Alteryx to Dataiku Quick Start
How Dataiku Does It Differently: Visual Flows and Projects
The good news is any Alteryx user working with Dataiku should feel right at home once they log in. Dataiku has its own visual Flow (Alteryx workflow) with Recipes (Alteryx tools) to manipulate data. But once you start to peel back the layers, there are some key exciting differences.
💡Explore Data Preparation in Dataiku
It's All About Projects — and the Flow Is the Star
Dataiku recognizes that analytic processes aren’t simple. Of course, there’s data and how it’s transformed, but there’s also collaborative dashboards, applications, automation, wiki’s and more. So, Dataiku created the concept of a project. The Flow is the star of the show and is where you’ll connect to data and create Recipes to do any kind of data preparation. But it’s just one piece of a larger project with specific areas for all of those other puzzle pieces built right in. And they’re all available right in the browser, with shareable links all backed by permissions.
Dataiku can also help make sure even your most complex work is simple and easy to understand. With just a click, descriptions of Dataiku Flow Zones (Alteryx Containers) or even an entire project can be generated with the help of Generative AI.
Create a Map for Any Journey
Similar to Alteryx, you can get a general idea of what’s happening in a process just by exploring the Flow itself. Clicking on Recipes or Datasets shows previews of the logic and data underneath in either the Preview button near the bottom or the Details pop out pane on the right. Where things get a little more interesting is Dataiku’s ability to let you embed a huge variety of visuals, analyses, and context at any point in the Flow.
For example, if I wanted to deep dive into a new piece of business logic I created in a Recipe, I might create some quick visual charts or interactive statistics right on that Recipe’s output. Or I could use Dataiku’s Explore pane to find outliers and understand data quality (similar to Alteryx’s Browse tool). From there, I could add a quick comment to ask a team member to verify my findings which gives them a link right to where they need to be in my process.
Ridiculously Easy Reuse
Once you’ve created Flows in your projects, Dataiku also makes it simple to find, understand, and reuse that great work. Every piece of logic and every dataset is automatically logged in Dataiku’s catalog with tagging to make searching easy. Once you’ve found something useful, it’s one click to see its parent project, understand how it was created, and share it to your own Flow. Shared datasets will even keep lineage to make troubleshooting linked projects a breeze.
All of this sharing is backed up by automatic version control so you never have to wonder which version of a Flow is the right one. It’s even easy to see which colleague made a change while you were away at lunch and to limit who can see what with project permissions.
This article is just the first step in the journey. In future articles, we’ll be diving in depth into Dataiku functionality, mapping it to Alteryx features and showing how Dataiku can help you take self-service analytics to the next level.
Chris has spent over a decade helping organizations ask questions of data at scale. He is currently a technical expert at Dataiku with a focus on democratizing analytics and machine learning.