One of the biggest obstacles to success in delivering data products is cross-team collaboration. Part of the problem is the difference in the information that each role requires to do their job and where they expect to find it. This introduces a barrier to communication that is difficult to overcome, particularly in teams that have not reached a significant level of maturity in their data journey. In this episode Prukalpa Sankar shares her experiences across multiple attempts at building a system that brings everyone onto the same page, ultimately bringing her to found Atlan. She explains how the design of the platform is informed by the needs of managing data projects for large and small teams across her previous roles, how it integrates with your existing systems, and how it can work to bring everyone onto the same page.
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Prukalpa Sankar about Atlan, a modern data workspace that makes collaboration among data stakeholders easier, increasing efficiency and agility in data projects
How did you get involved in the area of data management?
Can you start by giving an overview of what you are building at Atlan and some of the story behind it?
Who are the target users of Atlan?
What portions of the data workflow is Atlan responsible for?
What components of the data stack might Atlan replace?
How would you characterize Atlan’s position in the current data ecosystem?
What makes Atlan stand out from other systems for data cataloguing, metadata management, or data governance?
What types of data assets (e.g. structured vs unstructured, textual vs binary, etc.) is Atlan designed to understand?
Can you talk through how Atlan is implemented?
How have the goals and design of the platform changed or evolved since you first began working on it?
What are some of the early assumptions that you have had to revisit or reconsider?
What is involved in getting Atlan deployed and integrated into an existing data platform?
Beyond the technical aspects, what are the business processes that teams need to implement to be successful when incorporating Atlan into their systems?
Once Atlan is set up, what is a typical workflow for an individual and their team to collaborate on a set of data assets, or building out a new processing pipeline?
What are some useful steps for introducing all of the stakeholders to the system and workflow?
What are the available extension points for managing data in systems that aren’t supported by Atlan out of the box?
What are some of the most interesting, innovative, or unexpected ways that you have seen Atlan used?
What are the most interesting, unexpected, or challenging lessons that you have learned while building Atlan?
When is Atlan the wrong choice?
What do you have planned for the future of the product?
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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