There is a wealth of tools and systems available for processing data, but the user experience of integrating them and building workflows is still lacking. This is particularly important in large and complex organizations where domain knowledge and context is paramount and there may not be access to engineers for codifying that expertise. Raj Bains founded Prophecy to address this need by creating a UI first platform for building and executing data engineering workflows that orchestrates Airflow and Spark. Rather than locking your business logic into a proprietary storage layer and only exposing it through a drag-and-drop editor Prophecy synchronizes all of your jobs with source control, allowing an easy bi-directional interaction between code first and no-code experiences. In this episode he shares his motivations for creating Prophecy, how he is leveraging the magic of compilers to translate between UI and code oriented representations of logic, and the organizational benefits of having a cohesive experience designed to bring business users and domain experts into the same platform as data engineers and analysts.
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- Your host is Tobias Macey and today I’m interviewing Raj Bains about Prophecy, a low-code data engineering platform built on Spark and Airflow
- How did you get involved in the area of data management?
- Can you describe what you are building at Prophecy and the story behind it?
- There are a huge number of tools and recommended architectures for every variety of data need. Why is data engineering still such a complicated and challenging undertaking?
- What features and capabilities does Prophecy provide to help address those issues?
- What are the roles and use cases that you are focusing on serving with Prophecy?
- What are the elements of the data platform that Prophecy can replace?
- Can you describe how Prophecy is implemented?
- What was your selection criteria for the foundational elements of the platform?
- What would be involved in adopting other execution and orchestration engines?
- Can you describe the workflow of building a pipeline with Prophecy?
- What are the design and structural features that you have built to manage workflows as they scale in terms of technical and organizational complexity?
- What are the options for data engineers/data professionals to build and share reusable components across the organization?
- What are the most interesting, innovative, or unexpected ways that you have seen Prophecy used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Prophecy?
- When is Prophecy the wrong choice?
- What do you have planned for the future of Prophecy?
- 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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