Summary

The problems that are easiest to fix are the ones that you prevent from happening in the first place. Sifflet is a platform that brings your entire data stack into focus to improve the reliability of your data assets and empower collaboration across your teams. In this episode CEO and founder Salma Bakouk shares her views on the causes and impacts of "data entropy" and how you can tame it before it leads to failures.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer.
• Your host is Tobias Macey and today I’m interviewing Salma Bakouk about achieving data reliability and reducing entropy within your data stack with sifflet

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you describe what Sifflet is and the story behind it?
  • What is the motivating goal for the company and product?
• What are the categories of errors that you consider to be preventable?
  • How does the visibility provided by Sifflet contribute to those prevention efforts?
• What are the UI/UX patterns that you rely on to allow for meaningful exploration and analysis of dependency chains/impact assessments in the lineage graph?
• Can you describe how you’ve implemented Sifflet?
  • How have the scope and focus of the product evolved from when you first launched?
• What is the workflow for someone getting Sifflet integrated into their data stack?
• What are some of the data modeling considerations that need to be considered when pushing metadata to Sifflet?
• What are the most interesting, innovative, or unexpected ways that you have seen Sifflet used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Sifflet?
• When is Sifflet the wrong choice?
• What do you have planned for the future of Sifflet?

Contact Info

• LinkedIn
• @SalmaBakouk 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 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.
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Links

• Sifflet
• Data Observability
• DataDog
• NewRelic
• Splunk
• Modern Data Stack
• GoCardless
• Airbyte
• Fivetran
• ORM == Object Relational Mapping

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