Making Happy Modelers: Build and Maintain Your Data Warehouse with AWS Redshift and Airflow
Making Happy Modelers: Build and Maintain Your Data Warehouse with AWS Redshift and Airflow

Abstract: 

To apply AI effectively in the business setting, and to get the optimal benefit for business decisionmaking, data needs to be ready to use and easy to access for data science teams.
Journera (a travel industry data startup) has built our data warehouse using Airflow and AWS Redshift, and we're using it to access and analyze hundreds of millions of records on the fly. This talk will share an introduction to each tool, walk through the pipeline that can be built from any data store to the Redshift platform, discuss the architecture of a relational data warehouse in Redshift, and give tips on how to avoid mistakes we made in our own process.

Bio: 

Stephanie Kirmer is a Data Science Technical Lead at Journera, an early stage startup that helps companies in the travel industry use data efficiently and securely to create better travel experiences for customers. Previously she worked as a Senior Data Scientist at Uptake, where she developed tools for analyzing diesel engine fuel efficiency, and constructed predictive models for diagnosing and preventing mechanical failure. Before joining Uptake, she worked on data science for social policy research at the University of Chicago and taught sociology and health policy at DePaul University.