The promise of streaming data is that it allows you to react to new information as it happens, rather than introducing latency by batching records together. The peril is that building a robust and scalable streaming architecture is always more complicated and error-prone than you think it's going to be. After experiencing this unfortunate reality for themselves, Abhishek Chauhan and Ashish Kumar founded Grainite so that you don't have to suffer the same pain. In this episode they explain why streaming architectures are so challenging, how they have designed Grainite to be robust and scalable, and how you can start using it today to build your streaming data applications without all of the operational headache.
- 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 Ashish Kumar and Abhishek Chauhan about Grainite, a platform designed to give you a single place to build streaming data applications
- How did you get involved in the area of data management?
- Can you describe what Grainite is and the story behind it?
- What are the personas that you are focused on addressing with Grainite?
What are some of the most complex aspects of building streaming data applications in the absence of something like Grainite?
- How does Grainite work to reduce that complexity?
What are some of the commonalities that you see in the teams/organizations that find their way to Grainite?
What are some of the higher-order projects that teams are able to build when they are using Grainite as a starting point vs. where they would be spending effort on a fully managed streaming architecture?
Can you describe how Grainite is architected?
- How have the design and goals of the platform changed/evolved since you first started working on it?
What does your internal build vs. buy process look like for identifying where to spend your engineering resources?
What is the process for getting Grainite set up and integrated into an organizations technical environment?
- What is your process for determining which elements of the platform to expose as end-user features and customization options vs. keeping internal to the operational aspects of the product?
Once Grainite is running, can you describe the day 0 workflow of building an application or data flow?
- What are the day 2 - N capabilities that Grainite offers for ongoing maintenance/operation/evolution of those applications?
What are the most interesting, innovative, or unexpected ways that you have seen Grainite used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Grainite?
When is Grainite the wrong choice?
What do you have planned for the future of Grainite?
- 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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