Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
Sonali Sharma

Sonali Sharma
Senior Data Engineer, Netflix

Sonali Sharma a data engineer on the data personalization team at Netflix, which, among other things, delivers recommendations made for each user. The team is responsible for the data that goes into training and scoring of the various machine learning models that power the Netflix home page. They have been working on moving some of the company’s core datasets from being processed in a once-a-day daily batch ETL to being processed in near real time using Apache Flink. A UC Berkeley graduate, Sonali has worked on a variety of problems involving big data. Previously, she worked on the mail monetization and data insights engineering team at Yahoo, where she focused on building great data-driven products to do large-scale unstructured data extractions, recommendation systems, and audience insights for targeting using technologies like Spark, the Hadoop ecosystem (Pig, Hive, MapReduce), Solr, Druid, and Elasticsearch.


4:40pm5:20pm Thursday, March 28, 2019
Sonali Sharma (Netflix), Shriya Arora (Netflix)
Average rating: ***..
(3.00, 2 ratings)
With so much data being generated in real time, what if we could combine all these high-volume data streams and provide near real-time feedback for model training, improving personalization and recommendations and taking the customer experience to a whole new level. Sonali Sharma and Shriya Arora explain how to do exactly that, using Flink's keyed state. Read more.