Speaker: Sonali Sharma

Data Engineering and Analytics @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.

Find Sonali Sharma at:


Taming Large State: Lessons From Building Stream Processing

Streaming engines like Apache Flink are redefining ETL and data processing. Data can be extracted, transformed, filtered, and written out in real time with an ease matching that of batch processing. However, the real challenge of matching the prowess of batch ETL remains in doing joins, maintaining state, and dynamically pausing or resting the data.

At Netflix, micro-services serve and record many different kinds of user interactions with the product. Some of these live services generate millions of events per second, all carrying meaningful but often partial information. Things start to get exciting when the company wants to combine the events coming from one high-traffic micro-service to another. Joining these raw events generates rich datasets that are used to train the machine learning models that serve Netflix recommendations.

Historically, Netflix has done this joining of large volume datasets in batch. Recently, the company asked, If the data is being generated in real time, why can’t it be processed downstream in real time? Why wait a full day to get information from an event that was generated a few minutes ago?

This talks describes how we solved a complex join of two high-volume event streams at Netflix using Flink. You’ll learn about 

  • Managing out of order events and processing late arriving data
  • Exploring keyed state for maintaining large state
  • Fault tolerance of a stateful application
  • Strategies for failure recovery 
  • Schema evolution in a stateful realtime application
  • Data validation batch vs streaming


Ballroom BC


Modern Data Architectures


Interview Available


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