Integrating Elasticsearch Into Analytics Workflows
1:15 - 1:45 | Friday, May 24
As larger quantities of data are being stored and managed by enterprises of all kinds, NoSQL storage solutions are becoming more popular. Elasticsearch is a popular, high-performance NoSQL data storage option, but it is often unfamiliar to end users and difficult to navigate for day to day analytic tasks. This presentation will briefly discuss the structure and benefits of Elasticsearch data storage, and describe in detail, with examples, how to efficiently and smoothly transfer data between R or Python and this kind of data storage. Attendees will be introduced to three packages designed for this work, elastic (R), elasticsearch-py (Python), and uptasticsearch (R and Python), and will see hands-on examples of how to use them.
Senior Data Scientist, Uptake
Stephanie Kirmer is a Senior Data Scientist at Uptake in Chicago, Illinois, where she develops software tools for analyzing fuel efficiency and consumption. Previously at Uptake she constructed predictive models for diagnosing and preventing mechanical failure in the rail industry space. Before joining Uptake, she worked on data science for social policy research at the University of Chicago. She is also an adjunct faculty member in the College of Science and Health at DePaul University, and a member of the Shortlist Committee of the Chicago Humanities Festival. Stephanie holds an MA in Sociology from Portland State University and an MA in Social and Cultural Foundations of Education from DePaul University.