Event based data is a rich source of information for analytics, unless none of the event structures are consistent. The team at Iteratively are building a platform to manage the end to end flow of collaboration around what events are needed, how to structure the attributes, and how they are captured. In this episode founders Patrick Thompson and Ondrej Hrebicek discuss the problems that they have experienced as a result of inconsistent event schemas, how the Iteratively platform integrates the definition, development, and delivery of event data, and the benefits of elevating the visibility of event data for improving the effectiveness of the resulting analytics. If you are struggling with inconsistent implementations of event data collection, lack of clarity on what attributes are needed, and how it is being used then this is definitely a conversation worth following.
The need for quality customer data across the entire organization has never been greater, but data engineers on the ground know that building a scalable pipeline and securely distributing that data across the company is no small task. Customer data platforms can help automate the process, but most have broken MTU pricing models or cloud security issues. RudderStack is the answer to those customer data infrastructure problems. Our open source data capture and routing solutions help you turn your own warehouse into a secure customer data platform and our fixed-fee pricing means you never have to worry about unexpected costs from spikes in volume. Visit our website to request a demo and get one free month of access to the hosted platform along with a free t-shirt.
Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $60 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise.
- 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 $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- If you’ve been exploring scalable, cost-effective and secure ways to collect and route data across your organization, RudderStack is the only solution that helps you turn your own warehouse into a state of the art customer data platform. Their mission is to empower data engineers to fully own their customer data infrastructure and easily push value to other parts of the organization, like marketing and product management. With their open-source foundation, fixed pricing, and unlimited volume, they are enterprise ready, but accessible to everyone. Go to dataengineeringpodcast.com/rudder to request a demo and get one free month of access to the hosted platform along with a free t-shirt.
- You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!
- Your host is Tobias Macey and today I’m interviewing Patrick Thompson and Ondrej Hrebicek about Iteratively, a platform for enforcing consistent schemas for your event data
- How did you get involved in the area of data management?
- Can you start by describing what you are building at Iteratively and your motivation for creating it?
- What are some of the ways that you have seen inconsistent message structures cause problems?
- What are some of the common anti-patterns that you have seen for managing the structure of event messages?
- What are the benefits that Iteratively provides for the different roles in an organization?
- Can you describe the workflow for a team using Iteratively?
- How is the Iteratively platform architected?
- How has the design changed or evolved since you first began working on it?
- What are the difficulties that you have faced in building integrations for the Iteratively workflow?
- How is schema evolution handled throughout the lifecycle of an event?
- What are the challenges that engineers face in building effective integration tests for their event schemas?
- What has been your biggest challenge in messaging for your platform and educating potential users of its benefits?
- What are some of the most interesting or unexpected ways that you have seen Iteratively used?
- What are some of the most interesting, unexpected, or challenging lessons that you have learned while building Iteratively?
- When is Iteratively the wrong choice?
- What do you have planned for the future of Iteratively?
- 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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
- 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 firstname.lastname@example.org) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
- Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
- Locally Optimistic
- Snowplow Analytics
- JSON Schema
- Master Data Management
- SDLC == Software Development Life Cycle
- Mode Analytics
- CRUD == Create, Read, Update, Delete
- Schemaver (JSON Schema Versioning Strategy)
- Great Expectations
- Confluent Schema Registry
- Snowplow Iglu Schema Registry
- Pulsar Schema Registry