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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?

Wh

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Mastering Text Mining with R is your go-to guide for learning how to process and analyze textual data using R. Throughout the book, you'll gain the skills necessary to perform data extraction and natural language processing, equipping you with practical applications tailored to real-world scenarios. What this Book will help me do Learn to access and manipulate textual data from various sources using R. Understand text processing techniques and employ them with tools like OpenNLP. Explore methods for text categorization, reduction, and summarization with hands-on exercises. Perform text classification tasks such as sentiment analysis and entity recognition. Build custom applications using text mining techniques and frameworks. Author(s) Ashish Kumar is a seasoned data scientist and software developer with years of experience in text analytics and the R programming language. He has a knack for explaining complex topics in an accessible and practical manner, ideal for learners embracing their text mining journey. Who is it for? This book is for anyone keen on mastering text mining with R. If you're an R programmer, data analyst, or data scientist looking to delve into text analytics, you'll find it ideal. Some familiarity with basic programming and statistics will enhance your experience, but all concepts are introduced clearly and effectively.