[Spark基础]-- spark submmit大会(2017年6月5日 - 7日)

7:00 AM

Registration

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TRAINING ROOM 7

9:00 AM

​​Training: Data Science With Apache Spark 2.x​​

(9:00 AM–12:00 PM)

​​Training: Exploring Wikipedia 2 With Apache Spark 2.x​​

(9:00 AM–12:00 PM)

​​Training: Apache Spark Intro for Machine Learning and Data Science​​

(9:00 AM–12:00 PM)

​​Training: Apache Spark Intro for Data Engineering​​

(9:00 AM–12:00 PM)

​​Training: Just Enough Scala for Spark​​

(9:00 AM–12:00 PM)

​​Training: Architecting a Data Platform​​

(9:00 AM–12:00 PM)

​​Training: Building Your First Big Data Application on AWS​​

(9:00 AM–12:00 PM)

12:00 PM

Lunch

TRAINING ROOM 1

TRAINING ROOM 2

TRAINING ROOM 3

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TRAINING ROOM 6

TRAINING ROOM 7

1:00 PM

​​Training: Data Science With Apache Spark 2.x​​

(1:00 PM–5:00 PM)

​​Training: Exploring Wikipedia 2 With Apache Spark 2.x​​

(1:00 PM–5:00 PM)

​​Training: Apache Spark Intro for Machine Learning and Data Science​​

(1:00 PM–5:00 PM)

​​Training: Apache Spark Intro for Data Engineering​​

(1:00 PM–5:00 PM)

​​Training: Just Enough Scala for Spark​​

(1:00 PM–5:00 PM)

​​Training: Architecting a Data Platform​​

(1:00 PM–5:00 PM)

​​Training: Building Your First Big Data Application on AWS​​

(1:00 PM–5:00 PM)

6:00 PM

​​Meetup​​

Join us for an evening Bay Area Apache Spark Meetup at the 10th Spark Summit featuring tech-talks about using Apache 

Spark at scale from Pepperdata’s CTO Sean Suchter, RISELab’s Dan Crankshaw, and Databricks’ Spark committers… ​​Read more​​

7:00 AM

Registration

9:05 AM

​​What to Expect in 2017 for Big Data and Apache Spark​​

• ​​Matei Zaharia ​​(Databricks)
• ​​Tim Hunter ​​(Databricks)

9:30 AM

​​Snorkel: Dark Data and Machine Learning​​

• ​​Christopher Ré ​​(Stanford)

Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. 

However, building such applications is challenging: real world data is expressed in… ​​Read more​​

9:45 AM

​​Unleashing Data Intelligence with Intel and Apache Spark​​

• ​​Michael Greene ​​(Intel)

Organizations are developing deep learning applications to derive new insights, identify new opportunities and uncover new efficiencies.

 However, deep learning application development often means tapping into multiple frameworks, libraries, and clusters—a complex, 

time-consuming, and costly… ​​Read more​​

9:55 AM

​​Rise Lab Fireside Chat​​

• ​​Ben Lorica ​​(O’Reilly Media)
• ​​Ion Stoica ​​(UC Berkeley AMP/RISE Lab & Databricks)

Ben Lorica and Ion Stoica discuss the growth and new projects taking place at Rise Lab.

10:15 AM

​​Keynote by Riot Games​​

• ​​Wes Kerr ​​(Riot Games)

10:30 AM

Break

ROOM 1

ROOM 2

ROOM 3

ROOM 4

ROOM 5

ROOM 6

ROOM 7

ROOM 8

ROOM 9

11:00 AM

DEVELOPER

​​A Deep Dive into Spark SQL's Catalyst Optimizer​​

• ​​Yin Huai​​(Databricks)

(11:00 AM–11:30 AM)

MACHINE LEARNING

​​Challenging Web-Scale Graph Analytics with Apache Spark​​

• ​​Xiangrui Meng​​(Databricks)

(11:00 AM–11:30 AM)

SPARK ECOSYSTEM

​​Analyzing IOT Data in Apache Spark Across Data Centers and Cloud with NetApp Data Fabric and NetApp Private Storage​​

• ​​Karthikeyan Nagalingam​​(NetApp)
• ​​Nilesh Bagad​​(NetApp)

(11:00 AM–11:30 AM)

SPARK EXPERIENCE AND USE CASES

​​Scaling Up: How Switching to Apache Spark Improved Performance, Realizability, and Reduced Cost ​​

​​on a Very Large Scale ML Application​​

• ​​Kexin Xie​​(Salesforce)
• ​​Yacov Salomon​​(Salesforce)

(11:00 AM–11:30 AM)

ENTERPRISE

​​Spark Compute as a Service at Paypal​​

• ​​Prabhu Kasinathan​​(PayPal)

(11:00 AM–11:30 AM)

STREAMING

​​SSR: Structured Streaming on R for Machine Learning​​

• ​​Felix Cheung​​(Microsoft)

(11:00 AM–11:30 AM)

RESEARCH

​​Scaling Genetic Data Analysis with Apache Spark​​

• ​​Jonathan Bloom​​(Broad Institute of MIT and Harvard)
• ​​Timothy Poterba​​(Broad Institute of MIT and Harvard)

(11:00 AM–11:30 AM)

SPONSORED SESSIONS

TBA

(11:00 AM–11:30 AM)

TECHNICAL DEEP DIVES

​​Data Science Deep Dive: Spark ML with High Dimensional Labels​​

• ​​Michael Zargham​​(Cadent)
• ​​Stefan Panayotov​​(Cadent)

(11:00 AM–11:30 AM)

11:40 AM

DEVELOPER

​​TensorFlowOnSpark: Scalable TensorFlow Learning on Spark Clusters​​

• ​​Andy Feng ​​(Yahoo)
• ​​Lee Yang ​​(Yahoo)

(11:40 AM–12:10 PM)

MACHINE LEARNING

​​Needle in the Haystack—User Behavior Anomaly Detection for Information Security​​

• ​​Ping Yan​​(Salesforce.com)
• ​​Wei Deng​​(Salesforce)

(11:40 AM–12:10 PM)

SPARK ECOSYSTEM

​​Apache Kylin: Speed Up Cubing with Apache Spark​​

• ​​Luke Han​​(Kyligence, Inc.)
• ​​Shaofeng Shi​​(Kylingence Inc)

(11:40 AM–12:10 PM)

SPARK EXPERIENCE AND USE CASES

​​Incremental Processing on Large Analytical Datasets​​

• ​​Prasanna Rajaperumal ​​(Uber)
• ​​Vinoth Chandar​​(Uber)

(11:40 AM–12:10 PM)

ENTERPRISE

​​Using SparkML to Power a DSaaS (Data Science as a Service)​​

• ​​Kiran Muglurmath​​(Comcast)
• ​​Sridhar Alla​​(Comcast)

(11:40 AM–12:10 PM)

STREAMING

​​Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling​​

• ​​Jim Dowling ​​(KTH Royal Institute of Technology)

(11:40 AM–12:10 PM)

RESEARCH

​​Lazy Join Optimizations Without Upfront Statistics​​

• ​​Matteo Interlandi​​(UCLA)

(11:40 AM–12:10 PM)

SPONSORED SESSIONS

TBA

(11:40 AM–12:10 PM)

TECHNICAL DEEP DIVES

​​Data Science Deep Dive: Spark ML with High Dimensional Labels (continues)​​

• ​​Michael Zargham​​(Cadent)
• ​​Stefan Panayotov​​(Cadent)

(11:40 AM–12:10 PM)

12:20 PM

DEVELOPER

​​Hive Bucketing in Apache Spark​​

• ​​Tejas Patil​​(Facebook)

(12:20 PM–12:50 PM)

MACHINE LEARNING

​​Random Walks on Large Scale Graphs with Apache Spark​​

• ​​Min Shen​​(LinkedIn)

(12:20 PM–12:50 PM)

SPARK ECOSYSTEM

​​Building a Unified Data Pipeline with Apache Spark and XGBoost​​

• ​​Nan Zhu​​(Microsoft)

(12:20 PM–12:50 PM)

SPARK EXPERIENCE AND USE CASES

​​How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2.x​​

• ​​Richard Garris​​(Databricks)

(12:20 PM–12:50 PM)

ENTERPRISE

​​How Apache Spark and AI Powers UberEats​​

• ​​Chen Jin ​​(Uber)
• ​​Xian Xing Zhang​​(Uber Technologies)

(12:20 PM–12:50 PM)

STREAMING

​​The Top Five Mistakes Made When Writing Streaming Applications​​

• ​​Mark Grover​​(Cloudera)
• ​​Ted Malaska​​(Blizzard, Inc.)

(12:20 PM–12:50 PM)

RESEARCH

​​Running Apache Spark on a High-Performance Cluster Using RDMA and NVMe Flash​​

• ​​Patrick Stuedi​​(IBM)

(12:20 PM–12:50 PM)

SPONSORED SESSIONS

TBA

(12:20 PM–12:50 PM)

TECHNICAL DEEP DIVES

​​Ray: A Cluster Computing Engine for Reinforcement Learning Applications​​

• ​​Philipp Moritz ​​()
• ​​Robert Nishihara ​​()

(12:20 PM–12:50 PM)

12:50 PM

Lunch

ROOM 1

ROOM 2

ROOM 3

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ROOM 5

ROOM 6

ROOM 7

ROOM 8

ROOM 9

2:00 PM

DEVELOPER

​​Apache Spark MLlib's Past Trajectory and New Directions​​

• ​​Joseph Bradley​​(Databricks)

(2:00 PM–2:30 PM)

MACHINE LEARNING

​​Extending Spark Machine Learning: Adding Your Own Algorithms and Tools​​

• ​​Holden Karau ​​(IBM)
• ​​Seth Hendrickson​​(IBM)

(2:00 PM–2:30 PM)

SPARK ECOSYSTEM

​​Building Data Product Based on Apache Spark at Airbnb​​

• ​​Jingwei Lu ​​(Airbnb)
• ​​Liyin Tang ​​(Airbnb)

(2:00 PM–2:30 PM)

SPARK EXPERIENCE AND USE CASES

​​Building a Versatile Analytics Pipeline on Top of Apache Spark​​

• ​​Mikhail Chernetsov​​(Grammarly)

(2:00 PM–2:30 PM)

ENTERPRISE

​​Herding Cats: Migrating Dozens of Oddball Analytics Systems to Apache Spark​​

• ​​John Cavanaugh​​(HP)

(2:00 PM–2:30 PM)

STREAMING

​​Real-Time Machine Learning Analytics Using Structured Streaming and Kinesis Firehose​​

• ​​Caryl Yuhas​​(Databricks)
• ​​Myles Baker​​(Databricks)

(2:00 PM–2:30 PM)

RESEARCH

​​Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics on Spark and MPI Using Three Case Studies​​

• ​​Michael Mahoney​​(UC Berkeley)

(2:00 PM–2:30 PM)

SPONSORED SESSIONS

TBA

(2:00 PM–2:30 PM)

TECHNICAL DEEP DIVES

​​Cost-Based Optimizer in Apache Spark 2.2​​

• ​​Ron Hu ​​(Huawei Technologies)
• ​​Sameer Agarwal​​(Databricks)

(2:00 PM–2:30 PM)

2:40 PM

DEVELOPER

​​Informational Referential Integrity Constraints Support in Apache Spark​​

• ​​Ioana Delaney​​(IBM)
• ​​Suresh Thalamati​​(IBM)

(2:40 PM–3:10 PM)

MACHINE LEARNING

​​Fuzzy Matching on Apache Spark​​

• ​​Jennifer Shin ​​(8 Path Solutions)

(2:40 PM–3:10 PM)

SPARK ECOSYSTEM

​​Extending the R API for Spark with sparklyr and Microsoft R Server​​

• ​​Ali Zaidi ​​(Microsoft)

(2:40 PM–3:10 PM)

SPARK EXPERIENCE AND USE CASES

​​Best Practices for Using Alluxio with Apache Spark​​

• ​​Cheng Chang​​(Alluxio)
• ​​Haoyuan Li ​​(Alluxio)

(2:40 PM–3:10 PM)

ENTERPRISE

​​Scaling Data Science Capabilities with Apache Spark at Stitch Fix​​

• ​​Derek Bennett​​(Stitch Fix)

(2:40 PM–3:10 PM)

STREAMING

​​A Practical Approach to Building a Streaming Processing Pipeline for an Online Advertising Platform​​

• ​​Amit Ramesh​​(Yelp)
• ​​Yifan Wang ​​(Yelp)

(2:40 PM–3:10 PM)

RESEARCH

​​Apache Spark on Supercomputers: A Tale of the Storage Hierarchy​​

• ​​Costin Iancu​​(Lawrence Berkeley National Laboratory)
• ​​Nicholas Chaimov​​(University of Oregon)

(2:40 PM–3:10 PM)

SPONSORED SESSIONS

TBA

2:40 PM (2:40 PM–2:55 PM)

SPONSORED SESSIONS

TBA

2:55 PM (2:55 PM–3:10 PM)

TECHNICAL DEEP DIVES

​​Cost-Based Optimizer in Apache Spark 2.2 (continues)​​

• ​​Wenchen Fan​​(Databricks)
• ​​Zhenhua Wang​​(Huawei Technologies)

(2:40 PM–3:10 PM)

3:20 PM

DEVELOPER

​​Tricks of the Trade to be an Apache Spark Rock Star​​

• ​​Ted Malaska​​(Blizzard, Inc.)

(3:20 PM–3:50 PM)

MACHINE LEARNING

​​Assigning Responsibility for Deteriorations in Video Quality​​

• ​​Henry Milner​​(Conviva)
• ​​Oleg Vasilyev​​(Conviva)

(3:20 PM–3:50 PM)

SPARK ECOSYSTEM

​​Apache Spark on Kubernetes​​

• ​​Anirudh Ramanathan​​(Google)
• ​​Tim Chen​​(Hyperpilot)

(3:20 PM–3:50 PM)

SPARK EXPERIENCE AND USE CASES

​​Experiences Migrating Hive Workload to SparkSQL​​

• ​​Jie Xiong​​(Facebook)
• ​​Zhan Zhang​​(Facebook)

(3:20 PM–3:50 PM)

ENTERPRISE

​​Transforming B2B Sales with Spark-Powered Sales Intelligence​​

• ​​Songtao Guo​​(LinkedIn)
• ​​Wei Di ​​(LinkedIn)

(3:20 PM–3:50 PM)

STREAMING

​​An Online Spark Pipeline: Semi-Supervised Learning and Automatic Retraining with Spark Streaming​​

• ​​J White Bear ​​(IBM)

(3:20 PM–3:50 PM)

RESEARCH

​​Flare: Scale Up Spark SQL with Native Compilation and Set Your Data on Fire!​​

• ​​Tiark Rompf​​(Purdue University)

(3:20 PM–3:50 PM)

SPONSORED SESSIONS

TBA

3:20 PM (3:20 PM–3:35 PM)

SPONSORED SESSIONS

TBA

3:35 PM (3:35 PM–3:50 PM)

TECHNICAL DEEP DIVES

TBA

(3:20 PM–3:50 PM)

3:50 PM

Break

ROOM 1

ROOM 2

ROOM 3

ROOM 4

ROOM 5

ROOM 6

ROOM 7

ROOM 8

ROOM 9

4:20 PM

DEVELOPER

​​Improving Python and Spark Performance and Interoperability with Apache Arrow​​

• ​​Julien Le Dem​​(Dremio)
• ​​Li Jing ​​(Two Sigma Investments, LP)

(4:20 PM–4:50 PM)

MACHINE LEARNING

​​Multi-Label Graph Analysis and Computations Using GraphX​​

• ​​Qiang Zhu​​(LinkedIn)
• ​​Qingbo Hu​​(LinkedIn)

(4:20 PM–4:50 PM)

SPARK ECOSYSTEM

​​More Algorithms and Tools for Genomic Analysis on Apache Spark​​

• ​​Ryan Williams​​(Mount Sinai School of Medicine)

(4:20 PM–4:50 PM)

SPARK EXPERIENCE AND USE CASES

​​Lessons Learned from Managing Thousands of Production Apache Spark Clusters Daily​​

• ​​Henry Davidge​​(Databricks)
• ​​Josh Rosen​​(Databricks)

(4:20 PM–4:50 PM)

ENTERPRISE

​​GoDaddy Customer Success Dashboard Using Apache Spark​​

• ​​Baburao Kamble​​(GoDaddy)

(4:20 PM–4:50 PM)

STREAMING

​​Dynamic DDL: Adding Structure to Streaming Data on the Fly​​

• ​​David Winters​​(GoPro)
• ​​Hao Zou ​​(GoPro)

(4:20 PM–4:50 PM)

RESEARCH

​​Microservices and Teraflops: Effortlessly Scaling Data Science with PyWren​​

• ​​Eric Jonas ​​(UC Berkeley)
• ​​Shivaram Venkataraman ​​(UC Berkeley)

(4:20 PM–4:50 PM)

SPONSORED SESSIONS

TBA

4:20 PM (4:20 PM–4:35 PM)

SPONSORED SESSIONS

TBA

4:35 PM (4:35 PM–4:50 PM)

TECHNICAL DEEP DIVES

​​Easy, Scalable, Fault-Tolerant Stream Processing with Structured Streaming in Apache Spark​​

• ​​Michael Armbrust​​(Databricks)
• ​​Tathagata Das​​(Databricks)

(4:20 PM–4:50 PM)

5:00 PM

DEVELOPER

​​Building Robust ETL Pipelines with Apache Spark​​

• ​​Xiao Li ​​(Databricks)

(5:00 PM–5:30 PM)

MACHINE LEARNING

​​Visualization of Enhanced Spark Induced Naive Bayes Classifier​​

• ​​Barry Becker ​​(ESI Group)

(5:00 PM–5:30 PM)

SPARK ECOSYSTEM

​​Spark HBase Connector: Feature Rich and Efficient Access to HBase Through Spark SQL​​

• ​​Bikas Saha​​(Hortonworks)
• ​​Weiqing Yang​​(Hortonworks)

(5:00 PM–5:30 PM)

SPARK EXPERIENCE AND USE CASES

​​From Python Scikit-learn to Scala Apache Spark—The Road to Uncovering Botnets​​

• ​​Avi Aminov​​(Akamai Technologies)

(5:00 PM–5:30 PM)

ENTERPRISE

​​Applying Machine Learning to Construction​​

• ​​Charis Kaskiris​​(Autodesk)
• ​​Shubham Goel​​(Autodesk)

(5:00 PM–5:30 PM)

STREAMING

​​Building Continuous Application with Structured Streaming and Real-Time Data Source​​

• ​​Arijit Tarafdar​​(Microsoft)
• ​​Nan Zhu​​(Microsoft)

(5:00 PM–5:30 PM)

RESEARCH

​​Speeding Up Spark with Data Compression on Xeon+FPGA​​

• ​​David Ojika​​(University of Florida)

(5:00 PM–5:30 PM)

SPONSORED SESSIONS

TBA

5:00 PM (5:00 PM–5:15 PM)

SPONSORED SESSIONS

TBA

5:15 PM (5:15 PM–5:30 PM)

TECHNICAL DEEP DIVES

​​Easy, Scalable, Fault-Tolerant Stream Processing with Structured Streaming in Apache Spark (continues)​​

• ​​Michael Armbrust​​(Databricks)
• ​​Tathagata Das​​(Databricks)

(5:00 PM–5:30 PM)

5:40 PM

DEVELOPER

​​Behavior-Driven Development (BDD) Testing with Apache Spark​​

• ​​Aaron Colcord ​​(FIS Global)
• ​​Zachary Nanfelt​​(FIS)

(5:40 PM–6:10 PM)

MACHINE LEARNING

​​The Key to Machine Learning is Prepping the Right Data​​

• ​​Jean Georges Perrin ​​(Zaloni)

(5:40 PM–6:10 PM)

SPARK ECOSYSTEM

​​Building a Large Scale Recommendation Engine with Spark and Redis-ML​​

• ​​Dvir Volk ​​(Redis Labs)
• ​​Shay Nativ ​​(Redis Labs)

(5:40 PM–6:10 PM)

SPARK EXPERIENCE AND USE CASES

​​Apache Spark and Citizen Science: Using eBird Data to Predict Bird Abundance at Scale​​

• ​​Tom Auer ​​(Cornell University)

(5:40 PM–6:10 PM)

ENTERPRISE

​​Rental Cars and Industrialized Learning to Rank​​

• ​​Sean Downes​​(Expedia)

(5:40 PM–6:10 PM)

STREAMING

​​Scalable Monitoring Using Apache Spark and Friends​​

• ​​Utkarsh Bhatnagar​​(Tinder)

(5:40 PM–6:10 PM)

RESEARCH

​​Accelerating SparkML Workloads on the Intel Xeon+FPGA Platform​​

• ​​Zhankun Tang​​(Intel)
• ​​Zhongyue Nah​​(Intel)

(5:40 PM–6:10 PM)

SPONSORED SESSIONS

TBA

(5:40 PM–6:10 PM)

TECHNICAL DEEP DIVES

TBA

(5:40 PM–6:10 PM)

6:10 PM

​​Attendee Reception​​

Have fun mingling with other attendees over hors d’oeuvres and cocktails as you tour the Spark Summit Expo Hall.

8:00 AM

Registration

9:00 AM

​​Databricks Keynote​​

• ​​Ali Ghodsi ​​(Databricks)
• ​​Greg Owen ​​(Databricks)
• ​​Michael Armbrust ​​(Databricks)

9:40 AM

​​Keynote-TBA​​

9:55 AM

​​Keynote by Hotels.com​​

• ​​Matt Fryer ​​(Hotels.com)

10:10 AM

​​Cutting Edge Predictive Analytics​​

• ​​Eric Siegel ​​(Predictive Analytics World)

Apache Spark empowers predictive analytics and machine learning by increasing the reach and potential.

 But, before jumping to new deployments, it’s critical we 1) get the analytics right and 2) not overlook 

less conspicuous business… ​​Read more​​

10:30 AM

Break

ROOM 1

ROOM 2

ROOM 3

ROOM 4

ROOM 5

ROOM 6

ROOM 7

ROOM 8

ROOM 9

11:00 AM

DEVELOPER

​​Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop​​

• ​​Carl Steinbach​​(LinkedIn)
• ​​Simon King​​(Pepperdata)

(11:00 AM–11:30 AM)

MACHINE LEARNING

​​Embracing a Taxonomy of Types to Simplify Machine Learning​​

• ​​Leah McGuire​​(Salesforce.com)

(11:00 AM–11:30 AM)

SPARK ECOSYSTEM

​​HDFS on Kubernetes—Lessons Learned​​

• ​​Kimoon Kim​​(Pepperdata)

(11:00 AM–11:30 AM)

SPARK EXPERIENCE AND USE CASES

​​Spinach: Providing Ad-Hoc Query Support on Top of Spark SQL​​

• ​​Daoyuan Wang​​(Intel)
• ​​Yuanjian Li ​​(Baidu)

(11:00 AM–11:30 AM)

ENTERPRISE

​​Archiving, E-Discovery, and Supervision with Spark and Hadoop​​

• ​​Jordan Volz​​(Cloudera)

(11:00 AM–11:30 AM)

DATA SCIENCE

​​Yelp Ad Targeting at Scale with Apache Spark​​

• ​​Inaz Alaei-Novin​​(Yelp)
• ​​Joe Malicki ​​(Yelp)

(11:00 AM–11:30 AM)

RESEARCH

​​Debugging Big Data Analytics in Apache Spark with BigDebug​​

• ​​Matteo Interlandi​​(UCLA)
• ​​Muhammad Ali Gulzar ​​(UCLA)

(11:00 AM–11:30 AM)

SPONSORED SESSIONS

TBA

(11:00 AM–11:30 AM)

TECHNICAL DEEP DIVES

​​Deep Dive Into Apache Spark Multi-User Performance​​

• ​​Mikhail Genkin​​(IBM)
• ​​Peter Lankford​​(STAC)

(11:00 AM–11:30 AM)

11:40 AM

DEVELOPER

​​Productive Use of the Apache Spark Prompt​​

• ​​Sam Penrose​​(Mozilla)

(11:40 AM–12:10 PM)

MACHINE LEARNING

​​Identify Disease-Associated Genetic Variants Via 3D Genomics Structure and Regulatory Landscapes Using Deep Learning Frameworks​​

• ​​Yi-Hsiang Hsu ​​()
• ​​Yongsheng Huang​​(Databricks)

(11:40 AM–12:10 PM)

SPARK ECOSYSTEM

​​Homologous Apache Spark Clusters Using Nomad​​

• ​​Alex Dadgar​​(Hashicorp)

(11:40 AM–12:10 PM)

SPARK EXPERIENCE AND USE CASES

​​Social Media, Spark, Machine Learning, and Data Visualization to Find Patterns and Insight​​

• ​​Erik Schlegel​​(Microsoft)

(11:40 AM–12:10 PM)

ENTERPRISE

​​Next Generation Workshop Car Diagnostics at BMW Powered by Apache Spark​​

• ​​Bernhard Schlegel​​(BMW)

(11:40 AM–12:10 PM)

DATA SCIENCE

​​Data Wrangling with PySpark for Data Scientists Who Know Pandas​​

• ​​Andrew Ray​​(Silicon Valley Data Science)

(11:40 AM–12:10 PM)

RESEARCH

​​Building Genomic Data Processing and Machine Learning Workflows Using Apache Spark​​

• ​​Anupama Joshi​​(Epinomics)
• ​​Matt Negulescu​​(Epinomics)

(11:40 AM–12:10 PM)

SPONSORED SESSIONS

TBA

(11:40 AM–12:10 PM)

TECHNICAL DEEP DIVES

​​Deep Dive Into Apache Spark Multi-User Performance (continues)​​

(11:40 AM–12:10 PM)

12:20 PM

DEVELOPER

​​Taking Jupyter Notebooks and Apache Spark to the Next Level PixieDust​​

• ​​David Taieb ​​(IBM)

(12:20 PM–12:50 PM)

MACHINE LEARNING

​​Large-Scale Ads CTR Prediction with Spark and Deep Learning: Lessons Learned​​

• ​​Yanbo Liang​​(Hortonworks)

(12:20 PM–12:50 PM)

SPARK ECOSYSTEM

​​Interoperating a Zoo of Data Processing Platforms Using Rheem​​

• ​​Sebastian Kruse​​(PhD Student)
• ​​Yasser Idris ​​(Qatar Computing Research Institute)

(12:20 PM–12:50 PM)

SPARK EXPERIENCE AND USE CASES

​​Spark, GraphX, and Blockchains: Building a Behavioral Analytics Platform for Forensics, Fraud, and Finance​​

• ​​Bryan Cheng​​(BlockCypher)
• ​​Karen Hsu​​(BlockCypher)

(12:20 PM–12:50 PM)

ENTERPRISE

​​Big Data at Audi: Root Cause Analysis in an Automotive Paint Shop Using MLlib​​

• ​​Christian Raimann​​(Audi Business Innovation GmbH)
• ​​Christoph Kreibich​​(Audi)

(12:20 PM–12:50 PM)

DATA SCIENCE

​​Smart Scalable Feature Reduction With Random Forests​​

• ​​Erik Erlandson ​​(Red Hat)

(12:20 PM–12:50 PM)

RESEARCH

​​Neuro-Symbolic AI for Sentiment Analysis​​

• ​​Michael Malak​​(Oracle)

(12:20 PM–12:50 PM)

SPONSORED SESSIONS

​​Women in Big Data Lunch​​

(12:20 PM–12:50 PM)

TECHNICAL DEEP DIVES

​​From Pipelines to Refineries: Building Complex Data Applications with Apache Spark​​

• ​​Tim Hunter​​(Databricks)

(12:20 PM–12:50 PM)

12:50 PM

Lunch

ROOM 1

ROOM 2

ROOM 3

ROOM 4

ROOM 5

ROOM 6

ROOM 7

ROOM 8

ROOM 9

2:00 PM

DEVELOPER

​​Improving Apache Spark with S3​​

• ​​Ryan Blue ​​(Netflix)

(2:00 PM–2:30 PM)

MACHINE LEARNING

​​Building Competing Models Using Apache Spark DataFrames​​

• ​​Abdulla Al-Qawasmeh ​​(Credit Karma)

(2:00 PM–2:30 PM)

SPARK ECOSYSTEM

​​Cassandra and SparkSQL: You Don't Need Functional Programming for Fun​​

• ​​Russell Spitzer​​(DataStax)

(2:00 PM–2:30 PM)

SPARK EXPERIENCE AND USE CASES

​​Tuning Apache Spark for Large-Scale Workloads​​

• ​​Gaoxiang Liu​​(Facebook)
• ​​Sital Kedia​​(Facebook)

(2:00 PM–2:30 PM)

ENTERPRISE

​​From Data to Actions and Insights at Conviva​​

• ​​Rui Zhang​​(Conviva)
• ​​Yan Li ​​(Conviva)

(2:00 PM–2:30 PM)

DATA SCIENCE

​​Fully-Reproducible ML Deployment with Spark, Pachyderm, and MLeap​​

• ​​Daniel Whitenack​​(Pachyderm)
• ​​Hollin Wilkins​​(Combust, Inc.)

(2:00 PM–2:30 PM)

DATA SCIENCE

​​Natural Language Processing with CNTK and Apache Spark​​

• ​​Ali Zaidi ​​(Microsoft)

(2:00 PM–2:30 PM)

SPONSORED SESSIONS

TBA

(2:00 PM–2:30 PM)

TECHNICAL DEEP DIVES

​​Sparklyr: Recap, Updates, and Use Cases​​

• ​​Javier Luraschi​​(RStudio)

(2:00 PM–2:30 PM)

2:40 PM

DEVELOPER

​​Demystifying DataFrame and Dataset​​

• ​​Dr. Kazuaki Ishizaki​​(IBM)

(2:40 PM–3:10 PM)

MACHINE LEARNING

​​Real-Time Image Recognition with Apache Spark​​

• ​​Nikita Shamgunov​​(MemSQL)

(2:40 PM–3:10 PM)

SPARK ECOSYSTEM

​​Applying SparkSQL to Big Spatio-Temporal Data Using GeoMesa​​

• ​​Anthony Fox ​​(CCRi)

(2:40 PM–3:10 PM)

SPARK EXPERIENCE AND USE CASES

​​Performance Optimization of Recommendation Training Pipeline at Netflix​​

• ​​Hua Jiang ​​(Netflix)

(2:40 PM–3:10 PM)

ENTERPRISE

​​Changing the Way Viacom Looks at Video Performance​​

• ​​Mark Cohen​​(Viacom)
• ​​Michael Rosencrantz​​(Viacom, Inc.)

(2:40 PM–3:10 PM)

DATA SCIENCE

​​Large-Scaled Insurance Analytics Using Tweedie Models in Apache Spark​​

• ​​Yanwei Zhang​​(Uber)

(2:40 PM–3:10 PM)

DATA SCIENCE

​​ADMM-Based Scalable Machine Learning on Apache Spark​​

• ​​Mohak Shah​​(Robert Bosch LLC)
• ​​Sauptik Dhar​​(Robert Bosch LLC)

(2:40 PM–3:10 PM)

SPONSORED SESSIONS

TBA

(2:40 PM–3:10 PM)

TECHNICAL DEEP DIVES

​​Sparklyr: Recap, Updates, and Use Cases (continues)​​

(2:40 PM–3:10 PM)

3:20 PM

DEVELOPER

​​Apache Spark and Apache Ignite: Where Fast Data Meets the IoT​​

• ​​Denis Magda​​(GridGain)

(3:20 PM–3:50 PM)

MACHINE LEARNING

​​No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark​​

• ​​Masato Asahara​​(NEC)
• ​​Ryohei Fujimaki​​(NEC)

(3:20 PM–3:50 PM)

SPARK ECOSYSTEM

​​Just-in-Time Analytics and the Need for Autonomous Database Administration​​

• ​​Kristian Alexander​​(Algebraix Data Corp.)
• ​​Wes Holler​​(Algebraix)

(3:20 PM–3:50 PM)

SPARK EXPERIENCE AND USE CASES

​​Machine Learning as a Service: Apache Spark MLlib Enrichment and Web-Based Codeless Modeling​​

• ​​Zhengyi Le ​​(Suning R&D)

(3:20 PM–3:50 PM)

ENTERPRISE

​​Leveraging Apache Spark to Disrupt Airline Pricing Distribution​​

• ​​Anton Diego​​(EveryMundo)
• ​​Daniel Santana​​(EveryMundo)

(3:20 PM–3:50 PM)

DATA SCIENCE

​​Write Graph Algorithms Like a Boss​​

• ​​Andrew Ray​​(Silicon Valley Data Science)

(3:20 PM–3:50 PM)

DATA SCIENCE

​​A Predictive Analytics Workflow on DICOM Images using Apache Spark​​

• ​​Anahita Bhiwandiwalla​​(Intel)
• ​​Karthik Vadla ​​(Intel)

(3:20 PM–3:50 PM)

SPONSORED SESSIONS

TBA

(3:20 PM–3:50 PM)

TECHNICAL DEEP DIVES

TBA

(3:20 PM–3:50 PM)

3:50 PM

Break

ROOM 1

ROOM 2

ROOM 3

ROOM 4

ROOM 5

ROOM 6

ROOM 7

ROOM 8

ROOM 9

4:20 PM

DEVELOPER

​​A Developer’s View into Spark's Memory Model​​

• ​​Wenchen Fan​​(Databricks)

(4:20 PM–4:50 PM)

MACHINE LEARNING

​​Deep Learning in Security—Are We Ready?​​

• ​​Dr. Jisheng Wang​​(Niara)

(4:20 PM–4:50 PM)

SPARK ECOSYSTEM

​​Getting Ready to Use Redis with Apache Spark​​

• ​​Tague Griffith​​(Redis Labs)

(4:20 PM–4:50 PM)

SPARK EXPERIENCE AND USE CASES

​​Why You Should Care about Data Layout in the Filesystem​​

• ​​Cheng Lian​​(Databricks)
• ​​Vida Ha​​(Databricks)

(4:20 PM–4:50 PM)

ENTERPRISE

​​Leveraging Spark in Ecommerce Platform to Democratize Data​​

• ​​Shafaq Abdullah​​(Honest Company)

(4:20 PM–4:50 PM)

DATA SCIENCE

​​Using AI for Providing Insights and Recommendations on Activity Data​​

• ​​Alexis Roos​​(Salesforce)
• ​​Sammy Nammari​​(Salesforce)

(4:20 PM–4:50 PM)

DATA SCIENCE

​​Apache SparkR Under the Hood: How to Debug your SparkR Applications​​

• ​​Hossein Falaki​​(Databricks)

(4:20 PM–4:50 PM)

SPONSORED SESSIONS

TBA

(4:20 PM–4:50 PM)

TECHNICAL DEEP DIVES

​​Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more​​

• ​​Cihan Biyikoglu​​(Redis Labs)

(4:20 PM–4:50 PM)

5:00 PM

DEVELOPER

​​Continuous Application with FAIR Scheduler​​

• ​​Robert Xue​​(Groupon)

(5:00 PM–5:30 PM)

MACHINE LEARNING

​​Deep Learning to Big Data Analytics on Apache Spark Using BigDL​​

• ​​Xianyan Jia ​​(Intel)
• ​​Yuhao Yang ​​(Intel)

(5:00 PM–5:30 PM)

SPARK ECOSYSTEM

​​From R Script to Production Using rsparkling​​

• ​​Navdeep Gill​​(H2O.ai)

(5:00 PM–5:30 PM)

SPARK EXPERIENCE AND USE CASES

​​RubiOne: Apache Spark as the Backbone of a Retail Analytics Development Environment​​

• ​​Adrian Petrescu​​(Rubikloud)

(5:00 PM–5:30 PM)

ENTERPRISE

​​Stream All Things—Patterns of Modern Data Integration​​

• ​​Gwen Shapira​​(Confluent)

(5:00 PM–5:30 PM)

DATA SCIENCE

​​NLP with MLlib: Global Empire-Building for Fun and Profit​​

• ​​Michelle Casbon​​(Qordoba)

(5:00 PM–5:30 PM)

DATA SCIENCE

​​Building Smart IoT Applications Using Spark​​

• ​​Rafael Schultze-Kraft ​​(WATTx)

(5:00 PM–5:30 PM)

SPONSORED SESSIONS

TBA

(5:00 PM–5:30 PM)

TECHNICAL DEEP DIVES

​​Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more (continues)​​

(5:00 PM–5:30 PM)

5:40 PM

DEVELOPER

​​SparkOscope: Enabling Apache Spark Optimization through Cross Stack Monitoring​​

• ​​Yiannis Gkoufas​​(IBM)

(5:40 PM–6:10 PM)

MACHINE LEARNING

​​Deep Learning with Apache Spark and GPUs​​

• ​​Pierce Spitler​​(Bitfusion)
• ​​Tim Gasper​​(Bitfusion)

(5:40 PM–6:10 PM)

SPARK ECOSYSTEM

​​Distributed End-to-End Drug Similarity Analytics and Visualization Workflow​​

• ​​Anahita Bhiwandiwalla​​(Intel)
• ​​Dina Suehiro ​​(Intel Corporation)

(5:40 PM–6:10 PM)

SPARK EXPERIENCE AND USE CASES

​​The Smart Data Warehouse: Goal-Based Data Production​​

• ​​Sim Simeonov​​(Swoop)

(5:40 PM–6:10 PM)

ENTERPRISE

TBA

(5:40 PM–6:10 PM)

DATA SCIENCE

​​Very Large Data Files, Object Stores, and Deep Learning—Lessons Learned While Looking for Signs of Extra-Terrestrial Life​​

• ​​Gil Vernik ​​(IBM Corporation)
• ​​Graham Mackintosh ​​(IBM)

(5:40 PM–6:10 PM)

DATA SCIENCE

​​Semantic Search: Fast Results from Large, Non-Native Language Corpora​​

• ​​Rob Lantz ​​(Novetta)

(5:40 PM–6:10 PM)

SPONSORED SESSIONS

TBA

(5:40 PM–6:10 PM)

TECHNICAL DEEP DIVES

TBA

(5:40 PM–6:10 PM)

8:00 PM

​​JOIN Party​​

Come close out the 10th edition of Spark Summit at the JOIN attendee party. This rockin’ celebration includes drinks, games, 

DJs, dancing and a few fun surprises. In the coming weeks, we will announce even… ​​Read more​​​​Databricks​​