DataOps As A Service For Your Data Integration Workflows With Rivery
Data Engineering Podcast
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Reverie - Positive and Negative Sources of Inspiration
Reverie is a cloud-based tool for data engineers. It can be used to build, edit and share content on the go. The project was inspired by both positive and negative sources of inspiration as well as how best to integrate it with existing work tools.
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Summary
Data engineering is a practice that is multi-faceted and requires integration with a large number of systems. This often means working across multiple tools to get the job done which can introduce significant cost to productivity due to the number of context switches. Rivery is a platform designed to reduce this incidental complexity and provide a single system for working across the different stages of the data lifecycle. In this episode CEO and founder Itamar Ben hemo explains how his experiences in the industry led to his vision for the Rivery platform as a single place to build end-to-end analytical workflows, including how it is architected and how you can start using it today for your own work.
Announcements
- 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 Itamar Ben Hemo about Rivery, a SaaS platform designed to provide an end-to-end solution for Ingestion, Transformation, Orchestration, and Data Operations
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Rivery is and the story behind it?
- What are the primary goals of Rivery as a platform and company?
- What are the target personas for the Rivery platform?
- What are the points of interaction/workflows for each of those personas?
- What are some of the positive and negative sources of inspiration that you looked to while deciding on the scope of the platform?
- The majority of recently formed companies are focused on narrow and composable concerns of data management. What do you see as the shortcomings of that approach?
- What are some of the tradeoffs between integrating independent tools vs buying into an ecosystem?
- How is the Rivery platform designed and implemented?
- How have the design and goals of the platform changed or evolved since you began working on it?
- What were your criteria for the MVP that would allow you to test your hypothesis?
- How has the evolution of the ecosystem influenced your product strategy?
- One of the interesting features that you offer is the catalog of "kits" to quickly set up common workflows. How do you manage regression/integration testing for those kits as the Rivery platform evolves?
- What are the most interesting, innovative, or unexpected ways that you have seen Rivery used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Rivery?
- When is Rivery the wrong choice?
- What do you have planned for the future of Rivery?
Contact Info
- @ItamarBenHemo on Twitter
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 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 hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
- Rivery
- Matillion
- BigQuery
- Snowflake
- dbt
- Fivetran
- Snowpark
- Postman
- Debezium
- Snowflake Partner Connect
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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