Spark Summit East 2016会议于2016年2月16日至2月18日在美国纽约进行。总体来说，Spark Summit一年比一年火，单看纽约的峰会中，规模已从900人增加到500个公司的1300人，更吸引到更多大型公司的分享，包括Bloomberg、Capital One、Novartis、Comcast等公司。而在这次会议上，Databricks还发布了两款产品——Community Edition Beta和Dashboards。本文收集了本次会议的视频共67个提供免费下载。

会议内容

Spark 2.0
Democratizing Access to Data
Accelerating Enterprise Spark
Apache Spark: The Analytics Operating System
Spark Usage in Core Enterprise Business Operations
Using Spark to Power the Office 365 Delve Organization Analytics
Spark at Bloomberg
Spark and the Enterprise
Spark Performance: What's Next
Realtime Risk Management Using Kafka, Python, and Spark Streaming
Building Realtime Data Pipelines with Kafka Connect and Spark Streaming
Distributed Time Travel for Feature Generation
Monte Carlo Simulations in Ad-Lift Measurement Using Spark
Using GraphX/Pregel on Browsing History to Discover Purchase Intent
Petabyte Scale Anomaly Detection Using R & Spark
5 Reasons Enterprise Adoption Of Spark Is Unstoppable
Relationship Extraction from Unstructured Text-Based on Stanford NLP with Spark
Magellan: Spark as a Geospatial Analytics Engine
Interactive Visualization of Streaming Data Powered by Spark
Building a Graph
Building a Recommendation Engine Using Diverse Features
Not Your Father's Database: How to Use Apache Spark Properly in Your Big Data Architecture
Time Series Analysis with Spark
Spark and the Future of Advanced Analytics
A Real-Time Monitoring System for Financial Transactions. Easier with Spark Streaming
5 Myths About Spark and Big Data (And Where It Goes Next)
Lambda at Weather Scale
Inside Apache SystemML
Spark Tuning for Enterprise System Administrators
Generalized Linear Models in Spark MLlib and SparkR
Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams
Insights into Customer Behavior from Clickstream Data
The Future of Real-Time in Spark
Leveraging Spark, AWS, and Graph Analytics to Better Protect Customers
Data Profiling and Pipeline Processing with Spark
Role of Spark in transforming eBay’s Enterprise Data Platform
Spark Streaming and IoT
Using Spark to Analyze Activity and Performance in High Speed Trading Environments
TopNotch: Systematically Quality Controlling Big Data
Mapping Brain Connectivity Through Large-Scale Segmentation and Analysis
GraphFrames: Graph Queries in Spark SQL
Online Security Analytics on Large Scale Video Surveillance System
Structuring Spark: DataFrames, Datasets, and Streaming
Implementing Near-Realtime Datacenter Health Analytics using Model-driven Vertex-centric Programming on Spark Streaming and GraphX
Beyond Collect and Parallelize for Tests
Distributed Tensor Flow on Spark: Scaling Google's Deep Learning Library
ggplot2.SparkR: Rebooting ggplot2 for Scalable Big Data Visualization
Escaping Flatland: Interactive High-Dimensional Data Analysis in Drug Discovery Using Spark
An Introduction to Sparkling Water
Flintrock: A Faster, Better spark-ec2
Highlights and Challenges from Running Spark on Mesos in Production
Succinct Spark: Fast Interactive Queries on Compressed RDDs
Scaling Unsupervised Ciliary Motion Analysis for Actionable Biomedical Insights with PySpark
Top 5 Mistakes When Writing Spark Applications
Continuous Integration for Spark Apps
Operational Tips for Deploying Spark
Spark @ DataXu: Multi-Model Machine Learning for Real Time Bidding Over Display Ads
MLLeap, or How to Productionize Data Science Workflows Using Spark
Reactive Feature Generation with Spark and MLlib
Building a Just-in-Time Data Warehouse
Mastering Your Customer Data on Apache Spark
Deep Recurrent Neural Networks for Sequence Learning in Spark
Building Robust, Scalable and Adaptive Applications on Spark Streaming
Enhancements on Spark SQL optimizer
Clickstream Analysis with Spark—Understanding Visitors in Realtime
What Lies Beneath Apache Spark's RDD API (Using Spark-shell and WebUI)
Reactive Streams, linking Reactive Application to Spark Streaming
Pivoting Data with SparkSQL

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