Adding structure to your streaming pipelines: moving from Spark streaming to structured streaming

Adding structure to your streaming pipelines: moving from Spark streaming to structured streaming

Tuesday, June 19
11:50 AM - 12:30 PM
Grand Ballroom 220B

How do you go from a strictly typed object-based streaming pipeline with simple operations to a structured streaming pipeline with higher order complex relational operations? This is what the Data Engineering team did at GoPro to scale up the development of streaming pipelines for the rapidly growing number of devices and applications.

When big data frameworks such as Hadoop first came to exist, developers were happy because we could finally process large amounts of data without writing complex multi-threaded code or worse yet writing complicated distributed code. Unfortunately, only very simple operations were available such as map and reduce. Almost immediately, higher level operations were desired similar to relational operations. And so Hive and dozens (hundreds?) of SQL-based big data tools became available for more developer-efficient batch processing of massive amounts of data.

In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world, so that nearly every streaming framework now supports higher level relational operations.

In this talk, we will discuss in a very hands-on manner how the streaming data pipelines for GoPro devices and apps have moved from the original Spark streaming with its simple RDD-based operations in Spark 1.x to Spark's structured streaming with its higher level relational operations in Spark 2.x. We will talk about the differences, advantages, and necessary pain points that must be addressed in order to scale relational-based streaming pipelines for massive IoT streams. We will also talk about moving from “hand built” Hadoop/Spark clusters running in the cloud to using a Spark-based cloud service.


David Winters
Big Data Architect
David is an Architect in the Data Science and Engineering team at GoPro and the creator of their Spark-Kafka streaming data ingestion pipeline. He has been developing scalable data processing pipelines and eCommerce systems for over 20 years in Silicon Valley. David's current big data interests include streaming data as fast as possible from devices to near real-time dashboards and switching his primary programming language to Scala from Java after nearly 20 years. He holds a B.Sc. in Computer Science from The Ohio State University.
Hao Zou
Senior Software Engineer
Hao joined the Data Science and Engineering team at GoPro in 2016 and immediately started cranking out Java and Scala code for use in both the Spark Streaming and batch data pipelines. Hao continuously supports the data publishing needs of the device and software application development teams at GoPro and assists them in utilizing the most appropriate and efficient ways to stream, store, and access their data. He has a M.Sc. in Computer Science from Northeastern University.