Unlocking The Potential Of Streaming Data Applications Without The Operational Headache At Grainite
Data Engineering Podcast
The Importance of SQL in the Financial Services Space
I'm wondering if you can give some kind of common examples and also as I think about streaming systems, it seems like every single streaming system that somebody builds eventually turns into some sort of kind of SQL database. And I'm wondering if that's a capability that you're actively pursuing or something that you are afraid of kind of incidentally building by accident. Yeah, let Abhishek take theSQL part of it, but let me maybe describe some of the applications that people are building on top of greenite and some of the scenarios that we're seeing.
Summary
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.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack today to learn more
- Hey there podcast listener, are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it’s all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to dataengineeringpodcast.com/timextender where you can do two things: watch us build a data estate in 15 minutes and start for free today.
- Join in with the event for the global data community, Data Council Austin. From March 28-30th 2023, they'll play host to hundreds of attendees, 100 top speakers, and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data. As a listener to the Data Engineering Podcast you can get a special discount of 20% off your ticket by using the promo code dataengpod20. Don't miss out on their only event this year! Visit: dataengineeringpodcast.com/data-council today
- 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
Interview
- Introduction
- 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?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- 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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Links
- Grainite
- BigTable
- Spanner
- Firestore
- OpenCensus
- Citrix
- NetScaler
- J2EE
- RocksDB
- Pulsar
- SQL Server
- MySQL
- RAFT Protocol
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Data Council:  Join us at the event for the global data community, Data Council Austin. From March 28-30th 2023, we'll play host to hundreds of attendees, 100 top speakers, and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data. As a listener to the Data Engineering Podcast you can get a special discount off tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit: [dataengineeringpodcast.com/data-council](https://www.dataengineeringpodcast.com/data-council) Promo Code: dataengpod20
- Rudderstack:  Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit [RudderStack.com/DEP](https://rudderstack.com/dep) to learn more
- TimeXtender:  TimeXtender is a holistic, metadata-driven solution for data integration, optimized for agility. TimeXtender provides all the features you need to build a future-proof infrastructure for ingesting, transforming, modelling, and delivering clean, reliable data in the fastest, most efficient way possible. You can't optimize for everything all at once. That's why we take a holistic approach to data integration that optimises for agility instead of fragmentation. By unifying each layer of the data stack, TimeXtender empowers you to build data solutions 10x faster while reducing costs by 70%-80%. We do this for one simple reason: because time matters. Go to [dataengineeringpodcast.com/timextender](https://www.dataengineeringpodcast.com/timextender) today to get started for free!
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