TalkingData产品 之 移动观象台：
1.终端指数
品牌
设备
分辨率
操作系统
运营商
网络
2.数据报告
3.用户趋势
用户趋势：智能终端累积量发展趋势
区域热度
用户喜好
4.人迹地图

数据报告 之 2016清明时节下江南热点分析
说明：本报告主要分为三个部分：数据说明、用户人群画像、用户线下行为
其中：

用户人群画像
用户来源人群分布（常居地->目的地）
用户旅游热点城市
节前/节日/节后用户活跃度
男女比例
年龄比例
用户喜好(70后、80后、90后)
用户线下行为
生活轨迹：休闲消费/夜生活
景区热力图：节中期间/节日前后

TalkingData的同类产品：

1. Flurry
2. 友盟
3. Google Analytics

参考：

1. http://www.talkingdata.com/
2. http://www.talkingdata.com/index/#/device/mob/zh_CN
3. http://www.talkingdata.com/index/#/datareport/-1/zh_CN
4. http://www.talkingdata.com/index/#/profile/usertrend/zh_CN
5. http://www.talkingdata.com/index/behaviormap/heatMapOverlay.jsp
6. http://www.talkingdata.com/index/files/2016-04/1461050046352.pdf
7. http://www.domarketing.org/html/2012/yd_0508/4311.html

<完>

机器学习 & 深度学习 & 人工智能

分布式开源及新兴平台工具：

1. Apache Mahout
2. Spark MLib
3. Flink
4. H2O
5. Dato

深度学习开源实现：

• Torch
• Theano
• Caffe
• Petuum
• Deep4J
• H2O

从谷歌(Google)旗下DeepMind公司阿尔法围棋（AlphaGo）挑战围棋冠军李世石之后，人工智能进一步发酵，变成热门方向和热门关键词，各公司分别发布新闻，和开源相关产品。其中：

• Microsoft: DMTK
• Google: TensorFlow、SyntaxNet
• Apple:
• Facebook: Torch
• Amazon:
• OpenAI: OpenAI Gym
• Yahoo!：
• Baidu:
• IBM: SystemML

数据挖掘

商业：

1. SPSS

传统开源工具(单机)：

1. Weka
2. R
3. Python

参考：
1.《美团大数据平台架构实践》
2.互联网金融时代下机器学习与大数据风控系统
3.Dato
4.H2O
5.大数据＋机器学习＋平台，所以Dato拿了1850万美元B轮融资

1. http://mt.sohu.com/20160513/n449235974.shtml
2. http://www.infoq.com/cn/articles/h2o-ai-chief-architect-talk-deep-learning
3. http://cmsoft.10086.cn/info.html?key=348

商业流计算框架：

1. IBM StreamBase

开源流计算框架：

1. Spark Streaming
2. Apache Flink
3. Apache Storm (Twitter)
4. Apache Heron
5. Apache Apex
6. Yahoo! S4
7. Apache Samza (Linkedin)
8. Borealis(Brandeis University、Brown University、MIT)

参考：

1. http://www.infoq.com/cn/articles/overview-of-apache-streaming-technology?utm_campaign=rightbar_v2&utm_source=infoq&utm_medium=articles_link&utm_content=link_text
2. http://www.dataguru.cn/article-9532-1.html?utm_source=tuicool&utm_medium=referral
3. 18款顶级开源与商业流分析平台推荐与详解

美国AT&T

http://www.processexcellencenetwork.com/business-transformation/articles/the-goldmine-in-our-pockets-how-telecommunications
https://en.wikipedia.org/wiki/Data_monetization
http://www.footfallchina.cn/index.php/insights/

西班牙电信 智慧足迹(smart steps)

http://dynamicinsights.telefonica.com/blog/488/smart-steps-2
http://dynamicinsights.telefonica.com/wp-content/uploads/2015/04/O2-Retail-Smart-Steps-Case-Study.pdf
http://dynamicinsights.telefonica.com/blog/1634/smart-steps-saved-newark-from-commuter-lock-down

美国威瑞森(Verizon) Precision Market Insights

http://www.verizonwireless.com/featured/precision/

参考：

敏捷Scrum框架一览

用户故事切分

用户故事切分三步法：
第一步 准备待切分的故事
通过提出问题准备故事：

1
2

原则是待切分的大故事是否满足INVEST*原则（“较小的”这一点可以除外）？
故事大小是团队速率的 1/10 到 1/6 吗？

第二步 运用切分模式

1
2
3
4
5
6
7
8

按工作流程步骤切分
延迟性能优化
按简单/复杂切分
按主要工作切分
按不同界面切分
按不同类型的数据切分
按不同的业务规则切分
按操作切分

第三步 评估切分
通过对切分提出以下问题进行评估：

1
2
3
4
5

新故事的大小大致相等吗？
每个故事大概是团队速率的1/10到1/6吗？
每个故事都满足INVEST原则吗？
有可以降低优先级或删除掉的故事吗？
有没有明显的故事先开始，从而可以获得早期的价值、认知或风险降低等？

注：INVEST - 故事应该是：
独立的
可商谈的
有价值的
可估算的
较小的
可测试的

参考：

1. Introduction to Agile
2. New Story Splitting Resource
3. How to Split a User Story

笔记如下：

• 技术雷达从两个维度对技术进行评估，一个维度是技术归类的四个象限，包括：技术、平台、工具、语言及框架，另一个维度是反映所持有的态度的四个环，依次为：采用、试验、评估、暂缓。

  1 2 3 4

  采用：我们强烈主张业界采用这些技术。如果适用我们的项目，我们会采用他们。 试验：值得追求。重要的是理解如何建立这种能力。企业应该在风险可控的项目中尝试该项技术。 评估：为了确认它将如何影响你所在的企业，值得做一番探究。 暂缓：谨慎推行。

• 在技术理念上需要关注：

  1 2 3 4 5 6

  Products over projects BFF - Backend for frontends Data Lake Event Storming QA in production Reactive architectures

• 平台部分关注：

  1 2 3

  Docker Apache Mesos Kubernets

• 工具部分关注：

  1 2 3

  Consul Apache Kafka Zipkin

• 语言和框架部分关注：

  1 2 3 4 5 6 7 8

  ES6 React.js Spring Boot Swift Dagger Dapper Ember.js Reactive Native

• 暂缓部分关注：

  1 2	1. Application Servers 2. Jenkins as a deployment pipeline

<完>

参考：
1.http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HdfsSnapshots.html
2.http://debugo.com/hdfs-snapshot/
3.http://blog.csdn.net/linlinv3/article/details/44622203

一个典型的流式架构(a typical streaming architecture)

流式架构三组件

• A producer: 与数据源相连的软件系统。生产者从数据源采集、转换、过滤、聚合、增强之后发布事件数据到流式系统中。
• The streaming system: 接受生产者发布的数据，持久化这些数据，然后可靠的将数据分发给消费者。
• Consumers: 从流中订阅数据，并操作，或者分析这些数据。

技术选型(Options)

Producers:

• Apache Flume
• StreamSets Data Collector

Streaming System

• Apache Kafka
• MapR Streams

Comsumers(Processing)

• Spark Streaming
• Apache Storm
• Apache Flink
• Apache Apex

参考：

1.Real-Time Event Streaming What Are Your Options?
2.《Streaming Architecture》
3.Stream-based Architecture
4.Streaming Architecture: Ideal Platform for Microservices

Apache Storm

Storm发布1.0.0版本，关键特性：

• Developer Productivity
  New Storm Connectors Storm-Kafka Spout using new client APIs
  Distributed Log Search Dynamic Worker Profiling
• Enterprise Readiness
  Improved nimbus HA Automatic Back pressure
  Distributed cache Windowing and State Management
  ** Storm Performance improvements
• Operational Simplicity
  Storm Topology Event inspector Resource Aware Scheduling
  Dynamic Log Levels Pacemaker Storm Daemon
  http://hortonworks.com/blog/announcing-apache-storm-1-0-0/
  https://storm.apache.org/2016/04/12/storm100-released.html

HDP 2.4.2版本中APACHE SPARK & APACHE ZEPPELIN的增强

• Certified SparkSQL with ODBC (ODBC driver available from Hortonworks).
• Bug fixes in Spark Oozie action for a Kerberos enabled cluster.
• Spark Streaming with Apache Kafka support in a Kerberos enabled cluster.
• SparkSQL & ORC performance improvements.
• Final technical preview of Apache Zeppelin that includes Kerberos support, LDAP Authentication, and identity propagation.
  http://hortonworks.com/blog/apache-spark-apache-zeppelin-whats-coming-in-hdp-2-4-2/

Cloudera Engineering

How-to: Detect and Report Web-Traffic Anomalies in Near Real-Time
http://blog.cloudera.com/blog/2016/06/how-to-detect-and-report-web-traffic-anomalies-in-near-real-time/
Best Practices for Enterprise Data Hub Encryption
http://blog.cloudera.com/blog/2016/06/best-practices-for-enterprise-data-hub-encryption/
How-to: Analyze Fantasy Sports with Apache Spark and SQL (Part 2: Data Exploration)
http://blog.cloudera.com/blog/2016/06/how-to-analyze-fantasy-sports-with-apache-spark-and-sql-part-2-data-exploration/
How-to: Analyze Fantasy Sports using Apache Spark and SQL
http://blog.cloudera.com/blog/2016/06/how-to-analyze-fantasy-sports-using-apache-spark-and-sql/
https://spark-summit.org/2016/schedule/
Guide to Configuring Apache Impala (incubating) for HA with F5 BIG-IP
http://blog.cloudera.com/blog/2016/05/guide-to-configuring-apache-impala-incubating-for-ha-with-f5-big-ip/
http://www.cloudera.com/documentation/other/reference-architecture/PDF/Impala-HA-with-F5-BIG-IP.pdf
Multi-node Clusters with Cloudera QuickStart for Docker
http://blog.cloudera.com/blog/2016/08/multi-node-clusters-with-cloudera-quickstart-for-docker/
Livy, the Open Source REST Service for Apache Spark, Joins Cloudera Labs
http://blog.cloudera.com/blog/2016/07/livy-the-open-source-rest-service-for-apache-spark-joins-cloudera-labs/
Untangling Apache Hadoop YARN, Part 4: Fair Scheduler Queue Basics
http://blog.cloudera.com/blog/2016/06/untangling-apache-hadoop-yarn-part-4-fair-scheduler-queue-basics/
New Study: Evaluating Apache HBase Performance on Modern Storage Media
http://blog.cloudera.com/blog/2016/06/new-study-evaluating-apache-hbase-performance-on-modern-storage-media/
https://software.intel.com/sites/default/files/managed/95/0d/Optimize%20Hadoop%20Cluster%20Performance%20with%20Various%20Storage%20Media%20334463-001US.pdf
How-to: Process and Index Medical Images with Apache Hadoop and Apache Solr
http://blog.cloudera.com/blog/2016/05/how-to-process-and-index-medical-images-with-apache-hadoop-and-apache-solr/
How-to: Configure SAP HANA with Apache Impala (incubating)
http://blog.cloudera.com/blog/2016/05/how-to-configure-sap-hana-with-apache-impala-incubating/
How-to: Build a Prediction Engine using Spark, Kudu, and Impala
http://blog.cloudera.com/blog/2016/05/how-to-build-a-prediction-engine-using-spark-kudu-and-impala/
How-to: Improve Apache HBase Performance via Data Serialization with Apache Avro
http://blog.cloudera.com/blog/2016/05/how-to-improve-apache-hbase-performance-via-data-serialization-with-apache-avro/
Inside Santander’s Near Real-Time Data Ingest Architecture (Part 2)
http://blog.cloudera.com/blog/2016/05/inside-santanders-near-real-time-data-ingest-architecture-part-2/
Inside Santander’s Near Real-Time Data Ingest Architecture
http://blog.cloudera.com/blog/2015/08/inside-santanders-near-real-time-data-ingest-architecture/
Apache Impala (incubating) in CDH 5.7: 4x Faster for BI Workloads on Apache Hadoop
http://blog.cloudera.com/blog/2016/04/apache-impala-incubating-in-cdh-5-7-4x-faster-for-bi-workloads-on-apache-hadoop/
New in Cloudera Manager 5.7: Cluster Utilization Reporting
http://blog.cloudera.com/blog/2016/04/new-in-cloudera-manager-5-7-cluster-utilization-reporting/
Cloudera Enterprise 5.7 is Released
http://blog.cloudera.com/blog/2016/04/cloudera-enterprise-5-7-is-released/
How-to: Use Impala and Kudu Together for Analytic Workloads
http://blog.cloudera.com/blog/2016/04/how-to-use-impala-and-kudu-together-for-analytic-workloads/
Quality Assurance at Cloudera: Running/Upgrading to New Releases on Our Own EDH Cluster
http://blog.cloudera.com/blog/2016/04/quality-assurance-at-cloudera-runningupgrading-to-new-releases-on-our-own-edh-cluster/
Quality Assurance at Cloudera: Fault Injection and Elastic Partitioning
http://blog.cloudera.com/blog/2016/04/quality-assurance-at-cloudera-fault-injection-and-elastic-partitioning/
Benchmarking Apache Parquet: The Allstate Experience
http://blog.cloudera.com/blog/2016/04/benchmarking-apache-parquet-the-allstate-experience/

Cloudera Vision

How GoPro uses Apache Hadoop in the Cloud
https://vision.cloudera.com/gopro-hadoop-cloud/
SQL-on-Apache Hadoop – Choosing the right tool for the right job
https://vision.cloudera.com/sql-on-apache-hadoop-choosing-the-right-tool-for-the-right-job/
New Open-Source Service Enables Apache Spark Development
https://vision.cloudera.com/new-open-source-service-enables-apache-spark-development/
Tuning Hive on Spark
http://www.cloudera.com/documentation/enterprise/latest/topics/admin_hos_tuning.html
http://www.cloudera.com/documentation/enterprise/latest/topics/admin_performance.html
Faster Batch Processing with Hive-on-Spark
https://vision.cloudera.com/faster-batch-processing-with-hive-on-spark/
Beyond ETL: Real-time, Streaming Architectures
https://vision.cloudera.com/beyond-etl-real-time-streaming-architectures/
The One Platform Initiative Delivers
https://vision.cloudera.com/the-one-platform-initiative-delivers/

Hortonworks

Rack Awareness
https://community.hortonworks.com/articles/43057/rack-awareness-1.html
Rack Awareness Series 2
https://community.hortonworks.com/articles/43164/rack-awareness-series-2.html
Disaster recovery and Backup best practices in a typical Hadoop Cluster :Series 1 Introduction
https://community.hortonworks.com/articles/43525/disaster-recovery-and-backup-best-practices-in-a-t.html
MICROBENCHMARKING APACHE STORM 1.0 PERFORMANCE
http://hortonworks.com/blog/microbenchmarking-storm-1-0-performance/
TOP 5 ARTICLES ON HADOOP
http://hortonworks.com/blog/top-5-articles-hadoop/
TOP ARTICLES AND QUESTIONS FROM HCC LAST WEEK
http://hortonworks.com/blog/top-articles-questions-hcc-last-week/
HIVE LLAP TECHNICAL PREVIEW ENABLES SUB-SECOND SQL ON HADOOP AND MORE
http://hortonworks.com/blog/llap-enables-sub-second-sql-hadoop/
APACHE METRON TECH PREVIEW 2 AVAILABLE NOW!
http://hortonworks.com/blog/apache-metron-technical-preview-2/
LATEST INNOVATION WITHIN HORTONWORKS DATA PLATFORM (HDP) 2.5 UNVEILED
http://hortonworks.com/blog/latest-innovation-within-hortonworks-data-platform-hdp-2-5-unveiled/
SPARK-ON-HBASE: DATAFRAME BASED HBASE CONNECTOR
http://hortonworks.com/blog/spark-hbase-dataframe-based-hbase-connector/
UNDER-THE-HOOD WITH AMBARI METRICS AND GRAFANA
http://hortonworks.com/blog/hood-ambari-metrics-grafana/
A BRIEF HISTORY OF APACHE STORM
http://hortonworks.com/blog/brief-history-apache-storm/
HORTONWORKS HDP AND SAS EVENT STREAM PROCESSING TOGETHER, USING YARN
https://hortonworks.com/blog/hortonworks-hdp-and-sas-event-stream-processing-together-using-yarn/
APACHE SPARK & APACHE ZEPPELIN: WHAT’S COMING IN HDP 2.4.2
http://hortonworks.com/blog/apache-spark-apache-zeppelin-whats-coming-in-hdp-2-4-2/
ANNOUNCING CLOUDBREAK 1.2
http://hortonworks.com/blog/announcing-cloudbreak-1-2/
ANNOUNCING APACHE STORM 1.0.0
http://hortonworks.com/blog/announcing-apache-storm-1-0-0/
THE NEXT GENERATION OF HADOOP-BASED SECURITY & DATA GOVERNANCE
http://hortonworks.com/blog/the-next-generation-of-hadoop-based-security-data-governance/
ADVANCED METRICS VISUALIZATION DASHBOARDING WITH APACHE AMBARI
http://hortonworks.com/blog/advanced-metrics-visualization-dashboarding-apache-ambari/
STREAMLINING APACHE HADOOP OPERATIONS
http://hortonworks.com/blog/streamlining-apache-hadoop-operations/
THE NEXT MARKET LEADERS WILL POWER THEIR BUSINESSES FROM IOAT DATA SOURCES
http://hortonworks.com/blog/next-market-leaders-will-power-businesses-ioat-data-sources/

Databricks

Preview of Apache Spark 2.0 now on Databricks Community Edition
https://databricks.com/blog/2016/05/11/spark-2-0-technical-preview-easier-faster-and-smarter.html
Spark Trending in the Stack Overflow Survey
https://databricks.com/blog/2016/03/22/spark-trending-in-the-stack-overflow-survey.html
http://stackoverflow.com/research/developer-survey-2016
Continuous Integration and Delivery of Spark Applications at Metacog
https://databricks.com/blog/2016/04/06/continuous-integration-and-delivery-of-spark-applications-at-metacog.html

MapR

IoT Spotlight: Sensor to Dashboard – Real-Time Stream Processing for Oil and Gas
https://www.mapr.com/blog/iot-spotlight-sensor-dashboard-real-time-stream-processing-oil-and-gas
Using MapR, Mesos, Marathon, Docker, and Apache Spark to Deploy and Run Your First Jobs and Containers
https://www.mapr.com/blog/using-mapr-mesos-marathon-docker-and-apache-spark-deploy-and-run-your-first-jobs-and-containers
Apache Apex on MapR Converged Platform
https://www.mapr.com/blog/apache-apex-mapr-converged-platform
Monitoring a MapR Cluster with Elasticsearch + Kibana
https://www.mapr.com/blog/monitoring-mapr-cluster-elasticsearch-kibana
Real Time Credit Card Fraud Detection with Apache Spark and Event Streaming
https://www.mapr.com/blog/real-time-credit-card-fraud-detection-apache-spark-and-event-streaming
Fast, Scalable, Streaming Applications with the Kafka API (MapR Streams), Spark Streaming, and the HBase API (MapR-DB)
https://www.mapr.com/blog/fast-scalable-streaming-applications-kafka-api-mapr-streams-spark-streaming-and-hbase-api-mapr

Cloudera

Hortonworks

Top Ten Blogs from 2015
http://hortonworks.com/blog/top-ten-blogs-from-2015/
Best practices in HDFS authorization with Apache Ranger
http://hortonworks.com/blog/best-practices-in-hdfs-authorization-with-apache-ranger/
Community Choice Winner Blog: Advanced Execution Visualization of Spark jobs
http://hortonworks.com/blog/community-choice-winner-blog-advanced-execution-visualization-spark-jobs/
Delivering an Advanced Analytics Platform for Media Management with Arkena
http://hortonworks.com/blog/delivering-an-advanced-analytics-platform-for-media-management-with-arkena/
Top 3 articles for every Hadoop Developer
http://hortonworks.com/blog/it-has-a-been-a-busy-week-on-hcc-here-is-the-hot-content-for-this-week-based-on-community-activity-and-votes/
Ambari Metrics - Switching from Embedded to Distributed Mode Fails
https://community.hortonworks.com/questions/17421/ambari-metrics-switching-from-embedded-to-distribu.html
Top 3 articles for every Hadoop Developer
http://hortonworks.com/blog/top-3-articles-that-ever-hadoop-developer-should-read-week-072016/
Windowing and State checkpointing in Apache Storm
http://hortonworks.com/blog/storm-support-windowing-state/
https://community.hortonworks.com/articles/14171/windowing-and-state-checkpointing-in-apache-storm.html
Hadoop All Grown Up
http://hortonworks.com/blog/hadoop-all-grown-up/
Top 3 articles for every Hadoop Developer 2
http://hortonworks.com/blog/top-3-articles-every-hadoop-developer-2/
Hive on Tez Performance Tuning – Determining Reducer Counts
https://community.hortonworks.com/articles/22419/hive-on-tez-performance-tuning-determining-reducer.html
What is the Hortonworks recommendation on Swap usage?
https://community.hortonworks.com/questions/22548/what-is-the-hortonworks-recommendation-on-swap-usa.html
Top 3 articles for every Hadoop Developer
http://hortonworks.com/blog/top-3-articles-every-hadoop-developer/
Install Apache Hawq on HDP 2.3.4
https://community.hortonworks.com/articles/20420/install-apache-hawq-on-hdp-234.html
Top 3 articles for every Hadoop Developer
http://hortonworks.com/blog/enter-title-here/
How to remove risk disks from Hadoop cluster ?
https://community.hortonworks.com/questions/18593/how-to-remove-risk-disks-from-hadoop-cluster.html
https://community.hortonworks.com/articles/3131/replacing-disk-on-datanode-hosts.html
Capacity scheduler queue mapping while doAs disabled
https://community.hortonworks.com/questions/18639/capacity-scheduler-queue-mapping-while-doas-disabl.html
Hortonworks DataFlow 1.2 Released
http://hortonworks.com/blog/hortonworks-dataflow-1-2-released/
Top 3 articles for every Hadoop Developer
http://hortonworks.com/blog/top-3-articles-every-hadoop-developer-3/
Quickly enable SSL encryption for Hadoop components in HDP Sandbox
https://community.hortonworks.com/articles/22756/quickly-enable-ssl-encryption-for-hadoop-component.html
Hortonworks HDP and SAS Event Stream Processing together, using YARN
http://hortonworks.com/blog/hortonworks-hdp-and-sas-event-stream-processing-together-using-yarn/

MapR

Apache Spark as a Distributed SQL Engine
https://www.mapr.com/blog/apache-spark-distributed-sql-engine
Apache Flink GA - Planning for the Future
https://www.mapr.com/blog/apache-flink-ga-planning-future
How to Log in Apache Spark
https://www.mapr.com/blog/how-log-apache-spark
Streaming in the Extreme
https://www.mapr.com/blog/streaming-extreme
Secondary Indexing for MapR-DB using Elasticsearch
https://www.mapr.com/blog/secondary-indexing-mapr-db-using-elasticsearch
Top 10 Most Popular MapR Blog Posts of 2015
https://www.mapr.com/blog/top-10-most-popular-mapr-blog-posts-2015
Architecture Matters for Production Success
https://www.mapr.com/why-hadoop/why-mapr/architecture-matters
Spark Data Source API: Extending Our Spark SQL Query Engine
https://www.mapr.com/blog/spark-data-source-api-extending-our-spark-sql-query-engine
What Will You Do in 2016? Apache Spark, Kafka, Drill and More
https://www.mapr.com/blog/what-will-you-do-2016-apache-spark-kafka-drill-and-more
A Brief Overview of Performance Enhancements in Apache Drill 1.4
https://www.mapr.com/blog/brief-overview-performance-enhancements-apache-drill-14

Databricks

Announcing Spark 1.6
https://databricks.com/blog/2016/01/04/announcing-spark-1-6.html
Introducing Spark Datasets
https://databricks.com/blog/2016/01/04/introducing-spark-datasets.html
Spark 2015 Year In Review
https://databricks.com/blog/2016/01/05/spark-2015-year-in-review.html
Deep Learning with Spark and TensorFlow
https://databricks.com/blog/2016/01/25/deep-learning-with-spark-and-tensorflow.html
Faster Stateful Stream Processing in Spark Streaming
https://databricks.com/blog/2016/02/01/faster-stateful-stream-processing-in-spark-streaming.html
An Illustrated Guide to Advertising Analytics
https://databricks.com/blog/2016/02/02/an-illustrated-guide-to-advertising-analytics.html
Introducing Databricks Community Edition: Apache Spark for All
https://databricks.com/blog/2016/02/17/introducing-databricks-community-edition-apache-spark-for-all.html
Introducing Databricks Dashboards
https://databricks.com/blog/2016/02/17/introducing-databricks-dashboards.html
Introducing GraphFrames
https://databricks.com/blog/2016/03/03/introducing-graphframes.html
On-Time Flight Performance with GraphFrames for Apache Spark
https://databricks.com/blog/2016/03/16/on-time-flight-performance-with-spark-graphframes.html
Announcing New Databricks APIs for Faster Production Spark Application Deployment
https://databricks.com/blog/2016/03/30/announcing-new-databricks-apis-for-faster-production-spark-application-deployment.html
Introducing our new eBook: Apache Spark Analytics Made Simple
https://databricks.com/blog/2016/03/31/introducing-our-new-ebook-apache-spark-analytics-made-simple.html

参考