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Speaker "Stephanie Kirmer" Details Back

 

Topic

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.
Who is this presentation for?
Data scientists or those tasked with managing data warehouses for data science use; ops teams serving data scientists
Prerequisite knowledge:
Python, basic relational database systems, some AWS is helpful
What you'll learn?
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.

Profile

Stephanie Kirmer is a Senior Data Scientist at Saturn Cloud, a platform enabling easy to use parallelization and scaling for Python with Dask. Previously she worked as a DS Tech Lead at Journera, a travel data startup, and Senior Data Scientist at Uptake, where she developed 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.