Alumni Of AirBnB's Early Years Reflect On What They Learned About Building Data Driven Organizations
Data Engineering Podcast
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Select Star - Data Discovery Platform - The Biggest Challenge With Modern Data Systems
The biggest challenge with modern data systems is understanding what data you have, where it is located and who is using it. Srup: Data practitioners are having to deal with too many things to day instead of just getting their main job - delivering business value. Ard: I think the jury is out now. No one wants intrusive control over everything. It has to sit on top of this and allow all these tools to really inter operate well.
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Summary
AirBnB pioneered a number of the organizational practices that have become the goal of modern data teams. Out of that culture a number of successful businesses were created to provide the tools and methods to a broader audience. In this episode several almuni of AirBnB’s formative years who have gone on to found their own companies join the show to reflect on their shared successes, missed opportunities, and lessons learned.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- The biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Select Star’s data discovery platform solves that out of the box, with an automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day. Just connect it to your database/data warehouse/data lakehouse/whatever you’re using and let them do the rest. Go to dataengineeringpodcast.com/selectstar today to double the length of your free trial and get a swag package when you convert to a paid plan.
- 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 Lindsay Pettingill Chetan Sharma, Swaroop Jagadish, Maxime Beauchemin, and Nick Handel about the lessons that they learned in their time at AirBnB and how they are carrying that forward to their respective companies
Interview
- Introduction
- How did you get involved in the area of data management?
- You all worked at AirBnB in similar time frames and then went on to found data-focused companies that are finding success in their respective categories. Do you consider it an outgrowth of the specific company culture/work involved or a curiosity of the moment in time for the data industry that led you each in that direction?
- What are the elements of AirBnB’s data culture that you feel were done right?
- What do you see as the critical decisions/inflection points in the company’s growth that led you down that path?
- Every journey has its detours and dead-ends. What are the mistakes that were made (individual and collective) that were most instructive for you?
- What about that experience and other experiences led you each to go our respective directions with data startups?
- Was your motivation to start a company addressing the work that you did at AirBnB due to the desire to build on existing success, or the need to fix a nagging frustration?
- What are the critical lessons for data teams that you are focused on teaching to engineers inside and outside your company?
- What are your predictions for the next 5 years of data?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on translating your experiences at AirBnB into successful products?
Contact Info
- Lindsay
- @lpettingill on Twitter
- Chetan
- @chesharma87 on Twitter
- Maxime
- mistercrunch on GitHub
- @mistercrunch on Twitter
- Swaroop
- swaroopjagadish on GitHub
- @arudis on Twitter
- Nick
- @NicholasHandel on Twitter
- nhandel 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
- Iggy
- Eppo
- Acryl
- DataHub
- Preset
- Superset
- Airflow
- Transform
- Deutsche Bank
- Ubisoft
- BlackRock
- Kafka
- Pinot
- Stata
- R
- Knowledge-Repo
- AirBnB Almond Flour Cookie Recipe
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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