Spark Summit 2017 Europe 于2017-10-24 至 26在柏林进行，本次会议议题超过了70多个，会议的全部日程请参见：https://spark-summit.org/eu-2017/schedule/。本次议题主要包括：开发、研究、机器学习、流计算等领域。从这次会议可以看出，当前 Spark 发展两大方向：

• 深度学习（Deep Learning）
• 提升流系统的性能（ Streaming Performance）

如果想及时了解Spark、Hadoop或者Hbase相关的文章，欢迎关注微信公共帐号：iteblog_hadoop

2016年是深度学习之年，而且目前越来越多的人在加入这个，深度学习 + 大数据是目前一个很热门的趋势，所以spark中支持深度学习并且提供一个友好的API势在必行。

ppt下载：https://github.com/397090770/spark-summit-2017-Europe
高清视频下载：https://share.weiyun.com/4a186135b3213f1af2cd4cf6da1e3f9e

本次会议由很多比较值得关注的PPT，比如：Accelerating Shuffle A Tailor-Made RDMA Solution for Apache Spark、Deep Dive into Stateful Stream Processing in Structured Streaming、Deep Learning and Streaming in Apache Spark 2.x、Easy, Scalable, Fault-tolerant Stream Processing with Structured Streaming、An Adaptive Execution Engine for apache Spark SQL、Lessons From the Field Applying Best Practices to Your Apache Spark™ Applications等等。

全部可下载的PPT

下面的PPT是本次会议可下载的，已经全部上传到 GitHub 供大家下载：（GitHub）：进入GitHub下载本次会议全部PPT

A Tale of Three Apache Spark APIs RDDs, DataFrames & Datasets
Accelerating Shuffle A Tailor-Made RDMA Solution for Apache Spark
An Adaptive Execution Engine for apache Spark SQL
Apache Spark Streaming + Kafka 0.10 An Integration Story
Apache Spark Streaming Programming Techniques You Should Know
Apache Spark-Bench Simulate, Test, Compare, Exercise, and Yes, Benchmark
Apache Spark-and-Tensorflow-as-a-Service
Apache Sparkpache HBase Connector Feature Rich and Efficient Access to HBase through Spark SQL
Apache-Spark-Performance-Troubleshooting-at-Scale,-Challenges,-Tools,-and-Methodologies-with-Luca-Canali
Approximate Computing for Stream Analytics in Apache Spark
Art of Feature Engineering For Data Science
Best Practices for Using Alluxio with Spark
Beyond unit tests Testing for SparkHadoop workflows
Build, Scale, and Deploy Deep Learning Pipelines Using Apache Spark
Building Custom ML PipelineStages for Feature Selection
Building a Business Logic Translation Engine with Spark Streaming for Communicating Between Legacy Code and Microservices
Building machine learning algorithms on Apache Spark
Deduplication and Author-Disambiguation of Streaming Records via Supervised Models based on Content Encoders
Deep Dive into Stateful Stream Processing in Structured Streaming
Deep Learning and Streaming in Apache Spark 2.x
Digitalising the Core  How Analytics is Shaping the Energy Industry
Dr. Elephant Achieving Quicker, Easier, and Cost-Effective Big Data Analytics
Easy, Scalable, Fault-tolerant Stream Processing with Structured Streaming
Experimental Design for Distributed Machine Learning
Extending Spark SQL Data Sources APIs with Join Push Down
Extending Spark's Ingestion Build Your Own Java Data Source
Fast Data with Apache Ignite & Apache Spark
Feature Hashing for Scalable Machine Learning
Fire in the Sky An Introduction to Monitoring Apache Spark in the Cloud
From pipelines to refineries scaling big data applications
High Performance Enterprise Data Processing with Apache Spark
Hotels.com's Journey to Becoming an Algorithmic Business
How to share state across multiple Spark jobs using Apache Ignite
Indicium Interactive Querying at Scale
Lessons From the Field Applying Best Practices to Your Apache Spark Applications
Lessons Learned Developing and Managing Massive (300TB+) Apache Spark Pipelines in Production
Lessons Learned while Implementing a Sparse Logistic Regression Algorithm in Spark
Low touch machine learning
Lucid Genetic Programming Library for Apache Spark
MatFast In-Memory Distributed Matrix Computation Processing and Optimization Based on Spark SQL
Natural Language Understanding at Scale with Spark-Native NLP, Spark ML, and TensorFlow
Near Data Computing Architectures Opportunities and Challenges for Apache Spark
Next CERN Accelerator Logging Service A road to Big Data
One-Pass Data Science In Apache Spark With Generative T-Digests
Optimal Strategies for Large-Scale Batch ETL Jobs
Parallelizing Large Simulations with Apache SparkR
Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Learning
Productionizing Behavioural Features for Machine Learning with Apache Spark Streaming
Real-Time Image Recognition with Apache Spark
Real-time Detection Of Anomalies In The Database Infrastructure Using Apache Spark
Real-time Machine Learning with Redis-ML and Apache Spark
Running Spark Inside Docker Containers
Saving energy with Apache Spark and Toon
Scaling Your Skillset with Your Data
Smack Stack and Beyond Building Fast Data Pipelines
Spark Pipelines in the Cloud with Alluxio
Spatial Processing of Global Heat Maps with Apache Spark
Speedup Spark Applications using FPGA Accelerators on the cloud
Storage Engine Considerations for your Apache Spark Applications
Story Deduplication and Mutation
Supporting Highly Multitenant Spark Notebook Workloads
Tagging Text in Money Transfers A Use-Case of Spark in Banking
The state of spark in the cloud
Using Pluggable Apache Spark SQL Filters to Help GridPocket Users Keep Up with the Jones' (and save the planet)
Using Spark in the Cloud A Devops perspective
VEGAS The Missing Matplotlib for ScalaApache Spark
VariantSpark  Apache Spark for Bioinformatics
Web-Scale Graph Analytics with Apache Spark
Working with Skewed Data The Iterative Broadcast

视频

本地址只下载了本次会议的部分视频（共42个），如果需要全部的视频，请自行到 https://spark-summit.org/eu-2017/schedule/ 里面选择观看。