Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive

该插件主要实现了将用户输入的内容转成二维码

Processor Clock Generation, Distribution, and Clock SensorManagement Loops Phillip Restle, IBM

Deep_learning_for_molecular_generation_and_optimization Daniel C. Elton

NGSIM US-101和I-80数据集

Thompson的关于网格生成的经典文章

Recent Advances in Neural Question Generation的思维导图

数据由《我们的世界在数据》中基于以下两个来源编制： –《 BP世界能源统计评论》 – Ember 2000年至2019年之间的THh生成 Hydropower_Consumption.csv

Numerical Grid Generation Complete

项目电视脚本生成（Udacity课程作业） 在此项目中，我将使用RNN生成Seinfeld电视脚本。 我使用了来自9个季节的Seinfeld脚本数据集。 神经网络将生成一个新的“伪”电视脚本。