Strata Data Conference New York
September 25-28, 2017

Join Trifacta at Strata Data Conference New York to learn more on our #1 Data Wrangling solution. Stop by and visit the Trifacta booth #327.

Ells Campbell

Health Scientist, CDC

Connor Carreras

Manager for Customer Success, Trifacta

Ryan Weil

Chief Scientist, Leidos

Tracking the Opioid-Fueled HIV Outbreak with Big Data

Location: 1A 01/02

In some areas of the United States, the spread of HIV/AIDS is gaining new traction from the opioid epidemic. However, identifying and suppressing these outbreaks is difficult—transmission between persons with acute HIV infection is extraordinarily difficult to detect via routine investigations. The Microbial Transmission Network Team (MTNT), a sector of the Centers for Disease Control (CDC), sought out to leverage new techniques in data collection, preparation and visualization to enhance their understanding of transmission dynamics and, consequently, tailor prevention and intervention efforts according to the needs of a population at risk. One of the analytic tools the MTNT team used to investigate the HIV outbreak was Collaborative Advanced Analytics & Data Sharing (CAADS), managed by Leidos, which incorporates self-service data preparation technology from Trifacta, along with Alpine Data, Tableau, Arcadia Data, and Centrifuge Systems .

In this session, Ells Campbell, a computational biologist, will outline the CDC’s approach to the data inference, characterization, and visualization processes that are being pioneered by MTNT. In addition, Ryan Weil, a chief scientist at Leidos, will explain how the CDC has leveraged the CAADS platform including Trifacta to execute their analysis. Finally, Connor Carreras, a customer success manager at Trifacta, will demo how MTNT leverages Trifacta’s data wrangling solution to perform deeper exploration and more efficient transformation of complex epidemiologic data.

Sean Kandel

Founder and Chief Technical Officer

Kaushal Gandhi

Senior Software Engineer

Interactive data exploration and analysis at enterprise scale

Location: 1E 14

Organizations deploying Hadoop are storing, organizing, processing, and analyzing more data than ever before, and the number of analytic applications natively integrating with Hadoop has grown rapidly in the last few years. Consequently, there are often hundreds or thousands of business and data analysts that leverage Hadoop clusters to explore, wrangle, visualize, and operationalize data for diverse use cases. As cluster utilization increases, however, maintaining performance of both exploratory and production use cases becomes critical.

Sean Kandel shares best practices for building and deploying Hadoop applications to support large-scale data exploration and analysis across an organization and demonstrates techniques to amortize exploratory workloads across clients to scale deployments while limiting performance degradation. Along the way, Sean explains how to flexibly compile queries across multiple runtime engines to optimize both data analytic and transformation queries and compares benchmarks for multiple architectures, demonstrating the effects of these techniques in data lake initiatives.

Why the Next Wave of Data Lineage is Driven by Automation, Visualization, and Interaction

Sean Kandel offers an overview of an entirely new approach to visualizing metadata and data lineage, demonstrating automated methods for detecting, visualizing, and interacting with potential anomalies in reporting pipelines. Join in to learn what’s required to efficiently apply these techniques to large-scale data.

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Meet Trifacta's CTO & Co-Author, Sean Kandel to get your very own signed copy.

Get your very own complimentary copy of Principles of Data Wrangling: Practical Techniques for Data Preparation while at Strata New York. Come by the Trifacta booth 327 on Wednesday September 27th at 3:45 pm. Sean Kandel will be there to sign your copy and answer any questions you may have.

Not attending Strata New York? Don’t worry – You can still download a PDF copy of the book here.

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