Spark DataFrames: Simple and Fast Analysis of Structured Data

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This post will provide a technical overview of Spark’s DataFrame API. First, we’ll review the DataFrame API and show how to create DataFrames from a variety of data sources such as Hive, RDBMS databases, or structured file formats like Avro. We’ll then give example user programs that operate on DataFrames and point out common design patterns. The second half of the talk will focus on the technical implementation of DataFrames, such as the use of Spark SQL’s Catalyst optimizer to intelligently plan user programs, and the use of fast binary data structures in Spark’s core engine to substantially improve performance and memory use for common types of operations.

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About Michael Armbrust

Michael Armbrust is committer and PMC member of Apache Spark and the original creator of Spark SQL. He currently leads the team at Databricks that designed and built Structured Streaming and Databricks Delta. He received his PhD from UC Berkeley in 2013, and was advised by Michael Franklin, David Patterson, and Armando Fox. His thesis focused on building systems that allow developers to rapidly build scalable interactive applications, and specifically defined the notion of scale independence. His interests broadly include distributed systems, large-scale structured storage and query optimization. [daisna21-speakers]