Improve Data Quality Through Engineering Rigor And Business Engagement With Synq

Data Engineering Podcast

Summary

This episode features an insightful conversation with Petr Janda, the CEO and founder of Synq. Petr shares his journey from being an engineer to founding Synq, emphasizing the importance of treating data systems with the same rigor as engineering systems. He discusses the challenges and solutions in data reliability, including the need for transparency and ownership in data systems. Synq's platform helps data teams manage incidents, understand data dependencies, and ensure data quality by providing insights and automation capabilities. Petr emphasizes the need for a holistic approach to data reliability, integrating data systems into broader business processes. He highlights the role of data teams in modern organizations and how Synq is empowering them to achieve this.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
• Your host is Tobias Macey and today I'm interviewing Petr Janda about Synq, a data reliability platform focused on leveling up data teams by supporting a culture of engineering rigor

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you describe what Synq is and the story behind it?
  • Data observability/reliability is a category that grew rapidly over the past ~5 years and has several vendors focused on different elements of the problem. What are the capabilities that you saw as lacking in the ecosystem which you are looking to address?
• Operational/infrastructure engineers have spent the past decade honing their approach to incident management and uptime commitments. How do those concepts map to the responsibilities and workflows of data teams?
  • Tooling only plays a small part in SLAs and incident management. How does Synq help to support the cultural transformation that is necessary?
• What does an on-call rotation for a data engineer/data platform engineer look like as compared with an application-focused team?
• How does the focus on data assets/data products shift your approach to observability as compared to a table/pipeline centric approach?
• With the focus on sharing ownership beyond the boundaries on the data team there is a strong correlation with data governance principles. How do you see organizations incorporating Synq into their approach to data governance/compliance?
• Can you describe how Synq is designed/implemented?
  • How have the scope and goals of the product changed since you first started working on it?
• For a team who is onboarding onto Synq, what are the steps required to get it integrated into their technology stack and workflows?
• What are the types of incidents/errors that you are able to identify and alert on?
  • What does a typical incident/error resolution process look like with Synq?
• What are the most interesting, innovative, or unexpected ways that you have seen Synq used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Synq?
• When is Synq the wrong choice?
• What do you have planned for the future of Synq?

Contact Info

• LinkedIn
• Substack

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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
• 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 [e-mail] with your story.

Links

• Synq
• Incident Management
• SLA == Service Level Agreement
• Data Governance
  • Podcast Episode
• PagerDuty
• OpsGenie
• Clickhouse
  • Podcast Episode
• dbt
  • Podcast Episode
• SQLMesh
  • Podcast Episode

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

• Starburst:  This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, the query engine Apache Iceberg was designed for, Starburst is an open platform with support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. Go to [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)

Support Data Engineering Podcast