Integrating Elasticsearch with Analytics Workflows
Integrating Elasticsearch with Analytics Workflows


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/drawbacks of Elasticsearch data storage, and describe in detail, with examples, how your end users can get data out of Elasticsearch data storage powerfully and with confidence. 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.


Stephanie Kirmer is a Senior Data Scientist 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.

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