So you’ve successfully tackled big data. Now let Vida Ha and Prakash Chockalingam help you take it real time and conquer fast data. Vida and Prakash cover the most common uses cases for streaming, important streaming design patterns, and the best practices for implementing them to achieve maximum throughput and performance of your system using Spark Streaming—one of the most popular stream processing frameworks, which enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Vida and Prakash walk you through the most common use cases for Spark Streaming, common design patterns that emerge from these use cases, tips on how to avoid common pitfalls while implementing these design patterns, and performance optimization techniques.
Vida Ha is currently a solutions engineer at Databricks. Previously, she worked on scaling Square’s reporting analytics system. Vida first began working with distributed computing at Google, where she improved search rankings of mobile-specific web content and built and tuned language models for speech recognition using a year’s worth of Google search queries. She’s passionate about accelerating the adoption of Apache Spark to bring the combination of speed and scale of data processing to the mainstream.
Prakash Chockalingam is currently a solutions architect at Databricks, where he focuses on helping customers build their big data infrastructure, drawing on his decade-long experience with large-scale distributed systems and machine-learning infrastructure at companies including Netflix and Yahoo. Prior to joining Databricks, Prakash was with Netflix, designing and building the recommendation infrastructure that serves out millions of recommendations to users every day. His interests broadly include distributed systems and machine learning. He coauthored several publications on machine learning and computer vision research in the early stages of his career.
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