The CFP is now closed.
Spark Summits are the world’s largest big data events focused entirely on Apache Spark—assembling the very best engineers, scientists, analysts, and executives from around the globe to share their knowledge and receive expert training on this open-source powerhouse. Since our pioneering summit in 2013, thousands have come to learn how Spark, big data, machine learning, data engineering, and data science are delivering new insights to businesses and institutions worldwide.
Do you have a big idea to share, tips and tricks for community members embarking on the same journey, or a new developer tool or application to showcase? If so, we’d love to put your ideas, case studies, best practices, and technical knowledge in front of the largest gathering of big data professionals interested in all things Spark.
These are just guidelines and suggestions—we are open to your creativity. The CFP was open from May 15 – June 16, 2017.
In this track, presenters cover technical content on internals and the latest development in Spark core and Spark SQL.
This track is dedicated to academic and advanced industrial research. Talks span systems research involving Spark to research use cases (e.g. genomics, GPUs, etc.).
This track spotlights the practice of data science using Spark. Sessions cover innovative techniques, algorithms, and systems that refine raw data into actionable insight using visualization, statistics, and machine learning.
This track features use cases on how businesses deploy Spark and the lessons learned. Talks offer an exploration into business use cases across industries, ROI, best practices, relevant business metrics, compliance requirements for specific industries, and customer testimonials.
In parallel to data science, this track focuses more on production data pipelines, ETLs, and operations.
This track will feature open source and proprietary applications, libraries, or frameworks in the Spark ecosystem.
AI and deep learning are all the buzz these days. This track covers algorithms, techniques, models, and platforms for machine learning. You will be able to select a focus area of AI or Machine Learning.
Streaming use cases and continuous applications that reacts to data in real-time. Lambda architecture, Kappa architecture, Structured Streaming, Kafka, Kinesis, etc.
This track, as the name suggests, is a 60-min slot that allows a presenter to go deeper into the topic than the normal 30 min tracks do. The session should be highly technical with some demonstration. For example “Deep Dive into Catalyst Optimizer,” followed by “Deep Dive into Catalyst Optimizer Hands-on Lab.” There will be a limited number of talks in this track.
You’ll need to include the following information for your proposal:
Help us understand why your presentation is the right one for Spark Summit. Please keep in mind that this event is by and for professionals. All presentations and supporting materials must be respectful and inclusive.