Spark Summit 2016 San Francisco会议于2016年6月06日至6月08日在美国San Francisco进行。本次会议有多达150位Speaker,来自业界顶级的公司。
由于会议的全部资料存储在http://www.slideshare.net
网站,此网站需要翻墙才能访问。基于此本站收集了本次会议的所有PPT资料供大家学习交流之用。本次会议PPT资料全部通过爬虫程序下载,如有问题及时联系。
会议内容
Apache Spark 2.0 Large-Scale Deep Learning with TensorFlow AI: The New Electricity Big Data in Production — Lessons from Running in the Cloud Break — Sponsored by Huohua: A Distributed Time Series Analysis Framework For Spark Structuring Spark: Dataframes, Datasets And Streaming Low Latency Execution For Apache Spark Spark Uber Development Kit Five Lessons Learned In Building Streaming Applications At Microsoft Bing Scale Bolt: Building A Distributed ndarray A Deep Dive Into Structured Streaming Re-Architecting Spark For Performance Understandability Managed Dataframes And Dynamically Composable Analytics: The Bloomberg Spark Server Airstream: Spark Streaming At Airbnb Recent Developments In SparkR For Advanced Analytics Temporal Operators For Spark Streaming And Its Application For Office365 Service Monitoring Deploying Accelerators At Datacenter Scale Using Spark Elasticsearch And Apache Lucene For Apache Spark And MLlib Building Realtime Data Pipelines with Kafka Connect and Spark Streaming Large Scale Multimedia Data Intelligence And Analysis On Spark Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For Sparse Data Time-Evolving Graph Processing On Commodity Clusters Spark On Mesos: The State Of The Art Netflix - Productionizing Spark On Yarn For ETL At Petabyte Scale Huawei Advanced Data Science With Spark Streaming Scaling Machine Learning To Billions Of Parameters Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark Building Custom Machine Learning Algorithms With Apache SystemML Databricks' Data Pipelines: Journey And Lessons Learned MLeap: Productionize Data Science Workflows Using Spark Scalable Deep Learning Platform On Spark In Baidu A Graph-Based Method For Cross-Entity Threat Detection Spark And Cassandra: 2 Fast, 2 Furious Scalable And Incremental Data Profiling With Spark CaffeOnSpark: Deep Learning On Spark Cluster High-Performance Python On Spark Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forecasts GPU Computing With Apache Spark And Python Heterogeneous Workflows With Spark At Netflix Utilizing Human Data Validation For KPI Analysis And Machine Learning Spark: Interactive To Production Spatial Analysis On Histological Images Using Spark Livy: A REST Web Service For Apache Spark Spark Your Legacy - Real-World Lessons From Distributing An 8-Year-Old Monolith Immersive Data Visualization Using Spark GPU Support In Spark And GPU/CPU Mixed Resource Scheduling At Production Scale Efficient State Management With Spark 2.0 And Scale-Out Databases Bulletproof Jobs: Patterns For Large-Scale Spark Processing Sim Simeonov (Swoop) Attendee Reception Disrupting Big Data with Apache Spark in the Cloud Sparking the Intelligent Cloud From MapReduce to Spark: An Ecosystem Evolves for New User Needs Credit Fraud Prevention with Spark and Graph Analysis Pedal to the Metal: Accelerating Apache Spark with Innovations in Silicon Technology Apache Spark is at the Core of Re-Invention in the Cognitive Era Spark 360 Panel Production Readiness Testing At Salesforce Using Spark MLlib Deep Dive: Apache Spark Memory Management Interactive Visualization of Streaming Data Powered by Spark Solving The N+1 Problem In Personalized Genomics Connecting Python To The Spark Ecosystem Scalable Machine Learning Pipeline For Meta Data Discovery From eBay Listings Deep Dive Into Catalyst: Apache Spark 2.0'S Optimizer Video Games at Scale: Improving the gaming experience with Apache Spark A Spark Framework For < $100, < 1 Hour, Accurate Personalized DNA Analysis At Scale 700 Queries Per Second with Updates: Spark As A Real-Time Web Service Apache Spark MLlib 2.0 Preview: Data Science and Production Enhancing Spark SQL Optimizer With Reliable Statistics Apache Spark the Hard Way: Challenges with Building an On-Prem Spark Analytics Platform and Strategies to Mitigate This Processing 70Tb Of Genomics Data With ADAM And Toil Women in Big Data GraphFrames: Graph Queries In Spark SQL Is Apache Spark the Future of Data Analysis? Which Is Deeper - Comparison Of Deep Learning Frameworks On Spark Morticia: Visualizing And Debugging Complex Spark Workflows Lessons Learned From Running Spark On Docker Finding Graph Isomorphisms In GraphX And GraphFrames How DNV GL is Breaking Down Analytical and Computational Barriers Across the Energy Industry Using Databricks Optimizing Terascale Machine Learning Pipelines With KeystoneML Vertica And Spark: Connecting Computation And Data Top 5 Mistakes When Writing Spark Applications Enterprise Scale Topological Data Analysis Using Spark Natural Sparksmanship – The Art of Making an Analytics Enterprise Cross the Chasm Distributed Heterogeneous Mixture Learning On Spark Mobius: C# Language Binding For Spark Operational Tips For Deploying Apache Spark Automatic Features Generation And Model Training On Spark: A Bayesian Approach The Internet of Everywhere—How IBM The Weather Company Scales Understanding Memory Management In Spark For Fun And Profit Reactive Streams, Linking Reactive Application To Spark Streaming Spark and Couchbase: Augmenting the Operational Database with Spark Analyzing Log Data With Apache Spark Unified Framework for Real Time, Near Real Time and Offline Analysis of Video Streaming Using Apache Spark and Databricks Automated Spark Deployment With Declarative Infrastructure Apache Spark Usage in the Open Source Ecosystem Getting The Best Performance With PySpark Locality Sensitive Hashing By Spark PowerStream: Propelling Energy Innovation with Predictive Analytics Handling Data Skew Adaptively In Spark Using Dynamic Repartitioning Solr As A SparkSQL DataSource Fully Automated QA System For Large Scale Search And Recommendation Engines Using Spark
下载地址
直接点下面的点击进入下载按钮进行下载;也可以关注本博客微信公共帐号:iteblog_hadoop,并回复 summit关键字获取。
点击进入下载本博客文章除特别声明,全部都是原创!
原创文章版权归过往记忆大数据(过往记忆)所有,未经许可不得转载。
本文链接: 【Spark Summit 2016 San Francisco PPT免费下载[共95个]】(https://www.iteblog.com/archives/1690.html)