Wednesday, June 7
Drinks, Food and Music.
Stay tuned for more details!
Join more than 3,000 developers, engineers, data scientists, researchers and business professionals for three days of in-depth learning and networking.
With over 170 sessions and ten tracks to choose from — including Developer, Data Science, Enterprise, Machine Learning and Streaming — there’s content for every level and role. You can also add on 1-Day or 2-Day training courses.
Apache® Spark™ is a powerful open source processing engine built around speed, ease of use, and sophisticated analytics. It was started at UC Berkeley in 2009 and is now developed at the vendor-independent Apache Software Foundation. Since its release, Spark has seen rapid adoption by enterprises across a wide range of industries. Internet powerhouses such as Yahoo, eBay and Netflix have deployed Spark at massive scale, processing multiple petabytes of data on clusters of over 8,000 nodes. Apache Spark has also become the largest open source community in big data, with over 1000 contributors from 250+ organizations.
Reynold oversees technical contributions to Apache® Spark™ at Databricks, initiating efforts such as DataFrames and Project Tungsten. To demonstrate Spark’s scalability and performance, he lead the efforts in the 2014 Daytona GraySort contest and set the 2014 world record, beating the previous record held by Hadoop with 30X higher per-node efficiency. Prior to Databricks, he was a PhD student at the UC Berkeley AMPLab, where he focused on scalable data processing. He wrote the highest cited papers in SIGMOD 2011, 2013, and 2015, and won Best Demo Award at VLDB 2011 and SIGMOD 2012.
Founding chair of the pioneering data conference, O'Reilly Strata, Edd is a respected voice in the worlds of data, open source and the web. His work in emerging technology also includes six years as program chair of OSCON, and acting as the founding editor of the peer-reviewed journal "Big Data". He is currently VP of Technology Strategy at Silicon Valley Data Science.
Spark Summit 2017 features a number of 1-day and 2-day training workshops that include a mix of instruction and hands-on exercises to help you improve your Apache Spark skills. Training is offered as a standalone ticket; if you wish to attend any conference sessions on June 6 or 7, you must register for a conference pass as well.
Training courses and dates for Spark Summit 2017:
Exploring Wikipedia with Apache Spark
Just Enough Scala for Spark
SOLD OUT – Architecting a Data Platform
SOLD OUT – Data Science with Apache Spark 2.x
SOLD OUT – Apache Spark Intro for Data Engineering
SOLD OUT – Apache Spark Intro for Machine Learning and Data Science
Spark Summit 2017 has something for everyone, from developers and data scientists to researchers and business executives.
Find out what’s in store for:
|Conference Pass||Includes||Early Bird Available until 4/7/17||Standard Available until 6/4/17||On-Site Purchased during event|
|All-Access||Access to the VIP lounge, VIP party, lunch, keynotes, breakout sessions, expo hall, attendee reception and any attendee parties presented by Spark Summit. Limited quantities available.||$1,195||$1,495||$1,695|
|Conference||Access to lunch, keynotes, breakout sessions, expo hall, attendee reception and any attendee parties presented by Spark Summit.||$925||$1,125||$1,325|
|Expo Only||Access to expo hall, attendee reception and any attendee parties presented by Spark Summit.||$350||$400|
|1-Day Training||Access to a 1-day training course. 1-Day Training takes place Monday, June 5 from 9am to 5pm.||$625||$825||$1,025|
|2-Day Training||Access to a 2-day training course. 2-Day Training takes place Monday and Tuesday, June 5-6. Please note that training on June 6 will overlap with the first day of the Spark Summit conference.||$1,250||$1,450||$1,650|
Conveniently located in the South of Market area, Moscone West provides easy access to downtown San Francisco’s many hotels and restaurants giving opportunity to see what the city has to offer after the sessions close. Take advantage of easy transportation via BART, MUNI and CalTrain.