Summary
All software systems are in a constant state of evolution. This makes it impossible to select a truly future-proof technology stack for your data platform, making an eventual migration inevitable. In this episode Gleb Mezhanskiy and Rob Goretsky share their experiences leading various data platform migrations, and the hard-won lessons that they learned so that you don't have to.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
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Your host is Tobias Macey and today I'm interviewing Gleb Mezhanskiy and Rob Goretsky about when and how to think about migrating your data stack
Interview
Introduction
How did you get involved in the area of data management?
A migration can be anything from a minor task to a major undertaking. Can you start by describing what constitutes a migration for the purposes of this conversation?
Is it possible to completely avoid having to invest in a migration?
What are the signals that point to the need for a migration?
What are some of the sources of cost that need to be accounted for when considering a migration? (both in terms of doing one, and the costs of not doing one)
What are some signals that a migration is not the right solution for a perceived problem?
Once the decision has been made that a migration is necessary, what are the questions that the team should be asking to determine the technologies to move to and the sequencing of execution?
What are the preceding tasks that should be completed before starting the migration to ensure there is no breakage downstream of the changing component(s)?
What are some of the ways that a migration effort might fail?
What are the major pitfalls that teams need to be aware of as they work through a data platform migration?
What are the opportunities for automation during the migration process?
What are the most interesting, innovative, or unexpected ways that you have seen teams approach a platform migration?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on data platform migrations?
What are some ways that the technologies and patterns that we use can be evolved to reduce the cost/impact/need for migraitons?
Contact Info
Gleb
LinkedIn
@glebmm on Twitter
Rob
LinkedIn
RobGoretsky 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 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
Datafold
Podcast Episode
Informatica
Airflow
Snowflake
Podcast Episode
Redshift
Eventbrite
Teradata
BigQuery
Trino
EMR == Elastic Map-Reduce
Shadow IT
Podcast Episode
Mode Analytics
Looker
Sunk Cost Fallacy
data-diff
Podcast Episode
SQLGlot
Dagster
dbt
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
Hex: 
Hex is a collaborative workspace for data science and analytics. A single place for teams to explore, transform, and visualize data into beautiful interactive reports. Use SQL, Python, R, no-code and AI to find and share insights across your organization. Empower everyone in an organization to make an impact with data. Sign up today at [dataengineeringpodcast.com/hex](https://www.dataengineeringpodcast.com/hex} and get 30 days free!Rudderstack: 
Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstackSupport Data Engineering Podcast
