Summary

With the rise of the web and digital business came the need to understand how customers are interacting with the products and services that are being sold. Product analytics has grown into its own category and brought with it several services with generational differences in how they approach the problem. NetSpring is a warehouse-native product analytics service that allows you to gain powerful insights into your customers and their needs by combining your event streams with the rest of your business data. In this episode Priyendra Deshwal explains how NetSpring is designed to empower your product and data teams to build and explore insights around your products in a streamlined and maintainable workflow.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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!
RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder
Your host is Tobias Macey and today I'm interviewing Priyendra Deshwal about how NetSpring is using the data warehouse to deliver a more flexible and detailed view of your product analytics

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what NetSpring is and the story behind it?
- What are the activities that constitute "product analytics" and what are the roles/teams involved in those activities?
When teams first come to you, what are the common challenges that they are facing and what are the solutions that they have attempted to employ?
Can you describe some of the challenges involved in bringing product analytics into enterprise or highly regulated environments/industries?
- How does a warehouse-native approach simplify that effort?
There are many different players (both commercial and open source) in the product analytics space. Can you share your view on the role that NetSpring plays in that ecosystem?
How is the NetSpring platform implemented to be able to best take advantage of modern warehouse technologies and the associated data stacks?
- What are the pre-requisites for an organization's infrastructure/data maturity for being able to benefit from NetSpring?
- How have the goals and implementation of the NetSpring platform evolved from when you first started working on it?
Can you describe the steps involved in integrating NetSpring with an organization's existing warehouse?
- What are the signals that NetSpring uses to understand the customer journeys of different organizations?
- How do you manage the variance of the data models in the warehouse while providing a consistent experience for your users?
Given that you are a product organization, how are you using NetSpring to power NetSpring?
What are the most interesting, innovative, or unexpected ways that you have seen NetSpring used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on NetSpring?
When is NetSpring the wrong choice?
What do you have planned for the future of NetSpring?

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.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

‌

Episode description

Use Your Data Warehouse To Power Your Product Analytics With NetSpring

Episode description