Tame The Entropy In Your Data Stack And Prevent Failures With Sifflet

2022/11/21

Data Engineering Podcast

Frequently requested episodes will be transcribed first

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
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 new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode) today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show!
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. RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder)
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.
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 [email protected])) with your story.
To help other people find the show please leave a review on Apple Podcasts) and tell your friends and co-workers

Links

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

Tame The Entropy In Your Data Stack And Prevent Failures With Sifflet 46:47 Share