Deliver Personal Experiences In Your Applications With The Unomi Open Source Customer Data Platform

Data Engineering Podcast - Ein Podcast von Tobias Macey - Sonntags

Kategorien:

Summary The core to providing your users with excellent service is to understand them and provide a personalized experience. Unfortunately many sites and applications take that to the extreme and collect too much information. In order to make it easier for developers to build customer profiles in a way that respects their privacy Serge Huber helped to create the Apache Unomi framework as an open source customer data platform. In this episode he explains how it can be used to build rich and useful profiles of your users, the system architecture that powers it, and some of the ways that it is being integrated into an organization’s broader data ecosystem. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more. The first 10 people to request a personalized product tour will receive an exclusive Monte Carlo Swag box. Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Your host is Tobias Macey and today I’m interviewing Serge Huber about Apache Unomi, an open source customer data platform designed to manage customers, leads and visitors data and help personalize customers experiences Interview Introduction How did you get involved in the area of data management? Can you describe what Unomi is and the story behind it? What are the goals and target use cases of Unomi? What are the aspects of collecting and aggregating profile information that present challenges to developers? How does the design of Unomi reduce that burden? How does the focus of Unomi compare to systems such as Segment/Rudderstack or Optimizely for collecting user interactions and applying personalization? How does Unomi fit in the architecture of an application or data infrastructure? Can you describe how Unomi itself is architected? How have the goals and design of the project changed or evolved since it started? What are some of the most complex or challenging engineering projects that you have worked through? Can you describe the workflow of using Unomi to manage a set of customer profiles? What are some examples of user experience customization that you can build with Unomi? What are some alternative architectures that you have seen to produce similar capabilities? One of the interesting features of Unomi is the end-user profile management. What are some of the system and developer challenges that are introduced by that capability? (e.g. constraints on data manipulation, security, privacy concerns, etc.) How did Unomi manage privacy concerns and the GDPR ? How does Unomi help with the new third party data restrictions ? Why is access to raw data so important ? Could cloud providers offer Unomi as a service ? How have you used Unomi in your own work? What are the most interesting, innovative, or unexpected ways that you have seen Unomi used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Unomi? When is Unomi the wrong choice? What do you have planned for the future of Unomi? Contact Info LinkedIn @sergehuber on Twitter @bhillou on Twitter sergehuber on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Apache Unomi Jahia OASIS Open Foundation Segment Podcast Episode Rudderstack The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Visit the podcast's native language site