Every data project, whether it’s analytics, machine learning, or AI, starts with the work of data cleaning. This is a critical step and benefits from being accessible to the domain experts. Trifacta is a platform for managing your data engineering workflow to make curating, cleaning, and preparing your information more approachable for everyone in the business. In this episode CEO Adam Wilson shares the story behind the business, discusses the myriad ways that data wrangling is performed across the business, and how the platform is architected to adapt to the ever-changing landscape of data management tools. This is a great conversation about how deliberate user experience and platform design can make a drastic difference in the amount of value that a business can provide to their customers.
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- Your host is Tobias Macey and today I’m interviewing Adam Wilson about Trifacta, a platform for modern data workers to assess quality, transform, and automate data pipelines
- How did you get involved in the area of data management?
- Can you describe what Trifacta is and the story behind it?
- Across your site and material you focus on using the term "data wrangling". What is your personal definition of that term, and in what ways do you differentiate from ETL/ELT?
- How does the deliberate use of that terminology influence the way that you think about the design and features of the Trifacta platform?
- What is Trifacta’s role in the overall data platform/data lifecycle for an organization?
- What are some examples of tools that Trifacta might replace?
- What tools or systems does Trifacta integrate with?
- Who are the target end-users of the Trifacta platform and how do those personas direct the design and functionality?
- Can you describe how Trifacta is architected?
- How have the goals and design of the system changed or evolved since you first began working on it?
- Can you talk through the workflow and lifecycle of data as it traverses your platform, and the user interactions that drive it?
- How can data engineers share and encourage proper patterns for working with data assets with end-users across the organization?
- What are the limits of scale for volume and complexity of data assets that users are able to manage through Trifacta’s visual tools?
- What are some strategies that you and your customers have found useful for pre-processing the information that enters your platform to increase the accessibility for end-users to self-serve?
- What are the most interesting, innovative, or unexpected ways that you have seen Trifacta used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Trifacata?
- When is Trifacta the wrong choice?
- What do you have planned for the future of Trifacta?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- UC Berkeley
- Stanford University
- Stanford Data Wrangler
- SDLC == Software Delivery Life-Cycle
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