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.
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- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- 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.
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- 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