DataOps As A Service For Your Data Integration Workflows With Rivery
Data Engineering Podcast
00:00
Rivery Platform - What's the Biggest Gap in the Data Management Industry?
Down the road we will see more agan part of the product. Will see more native integrations with machine learning tools. I don't think that there is now any specific and unique gap i don't want to say didn't solve. And going back to the place that you have one, two or three data stack, i don't believe that every feature deserve to be an industry. In the next five years, you won't see the same picture as you see now. What you will see is consolidation. Even company like five n a billet, that won't look the same. It will maybe buy other companies. Younow reduce the number of players in this industry.
Transcript
Play full episode
Transcript
Episode notes
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
- 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!
- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
- Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.
- Are you looking for a structured and battle-tested approach for learning data engineering? Would you like to know how you can build proper data infrastructures that are built to last? Would you like to have a seasoned industry expert guide you and answer all your questions? Join Pipeline Academy, the worlds first data engineering bootcamp. Learn in small groups with likeminded professionals for 9 weeks part-time to level up in your career. The course covers the most relevant and essential data and software engineering topics that enable you to start your journey as a professional data engineer or analytics engineer. Plus we have AMAs with world-class guest speakers every week! The next cohort starts in April 2022. Visit dataengineeringpodcast.com/academy and apply now!
- 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
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
