San Francisco dbt Meetup Online
Details
Welcome to the first online-only San Francisco Meetup! We are disappointed we can't meet in person, but are still looking forward to seeing your friendly faces on Zoom.
dbt Meetups are networking events open to all folks working with data! Talks predominantly focus on community members’ experience with dbt, however, you'll catch presentations on broader topics such as analytics engineering, data stacks, data ops, modeling, testing, and team structures.
For the best Meetup experience, make sure to join the #events-sf channel in dbt Slack (https://slack.getdbt.com/).
----------------------------------------
Agenda ✅
11:00 am - 11:05 am: Welcome Remarks & Attendee Intros
11:05 am - 11:20 am: Responding in an emergency with data, DataSF, City & County of San Francisco
11:20 am - 11:30 am: DataSF, City & County of San Francisco Q&A
11:30 am - 11:45 am: Improving data reliability using tests in dbt, Zoox
11:45 am - 11:55 am: Zoox Q&A
11:55 am - 12:00 pm: Closing Remarks
----------------------------------------
Speaker & Presentation Details:
Responding in an emergency with data 🎤
Tania Jogesh, Data Scientist, DataSF, City & County of San Francisco
Blake Valenta, Data Analyst & Analytics Strategist, DataSF, City & County of San Francisco
Jason Lally, Chief Data Officer, DataSF, City & County of San Francisco
Discover how DataSF responded to the Covid-19 emergency and provided a 360-degree view of PPE (personal protective equipment) inventory. Leveraging dbt, agile processes and empathy, DataSF brought together different data sources in a rapidly changing environment while minimizing disruption to those responding to the crisis.
Improving data reliability using tests in dbt 🎤
Evgeny Rubtsov, Data Engineer, Zoox
Trust is one of the most important assets that a data team has, and nothing undermines trust as much as repeated issues with data accuracy. Learn how Zoox ensures data integrity with dbt: testing at runtime (vs. CI/CD), using schema tests so that issues with data sources are not contaminating public models, and using dbt snapshots to catch the cases when data sources alter historic data.
----------------------------------------