Exploring The Design And Benefits Of The...

Summary

We have been building platforms and workflows to store, process, and analyze data since the earliest days of computing. Over that time there have been countless architectures, patterns, and "best practices" to make that task manageable. With the growing popularity of cloud services a new pattern has emerged and been dubbed the "Modern Data Stack". In this episode members of the GoDataDriven team, Guillermo Sanchez, Bram Ochsendorf, and Juan Perafan, explain the combinations of services that comprise this architecture, share their experiences working with clients to employ the stack, and the benefits of bringing engineers and business users together with data.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy!
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• RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today.
• We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial.
• Your host is Tobias Macey and today I’m interviewing Guillermo Sanchez, Bram Ochsendorf, and Juan Perafan about their experiences with managed services in the modern data stack in their work as consultants at GoDataDriven

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you start by giving your definition of the modern data stack?
  • What are the key characteristics of a tool or platform that make it a candidate for the "modern" stack?
• How does the modern data stack shift the responsibilities and capabilities of data professionals and consumers?
• What are some difficulties that you face when working with customers to migrate to these new architectures?
• What are some of the limitations of the components or paradigms of the modern stack?
  • What are some strategies that you have devised for addressing those limitations?
  • What are some edge cases that you have run up against with specific vendors that you have had to work around?
  • What are the "gotchas" that you don’t run up against until you’ve deployed a service and started using it at scale and over time?
• How does data governance get applied across the various services and systems of the modern stack?
• One of the core promises of cloud-based and managed services for data is the ability for data analysts and consumers to self-serve. What kinds of training have you found to be necessary/useful for those end-users?
• What is the role of data engineers in the context of the "modern" stack?
• What are the most interesting, innovative, or unexpected manifestations of the modern data stack that you have seen?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working with customers to implement a modern data stack?
• When is the modern data stack the wrong choice?
• What new architectures or tools are you keeping an eye on for future client work?

Contact Info

• Guillermo
  • LinkedIn
  • guillesd on GitHub
• Bram
  • LinkedIn
  • bramochsendorf on GitHub
• Juan
  • LinkedIn
  • jmperafan on GitHub

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
• Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

• GoDataDriven
• Deloitte
• RPA == Robotic Process Automation
• Analytics Engineer
• James Webb Space Telescope
• Fivetran
  • Podcast Episode
• dbt
  • Podcast Episode
• Data Governance
  • Podcast Episodes
• Azure Cloud Platform
• Stitch Data
• Airflow
• Prefect
• Argo Project
• Looker
• Azure Purview
• Soda Data
  • Podcast Episode
• Datafold
• Materialize
  • Podcast Episode

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