Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using StormImplement strategies to solve the challenges of real-time data processingLoad datasets, build queries, and make recommendations using Spark SQL

Spotify笔记本 这是Spotify的主要内容的Python部分，主要探讨python的数据分析技术和表示样式。 这是创建仪表盘的基础。 归纳法 这是通过名为Spotify.ipynb的文件中的python和jupyter notbook完成的数据可视化和分析 了解类别 内容 “ data.csv”文件包含从Spotify Web API收集的170,000首歌曲，您还可以在其他数据集中找到按艺术家，年份或流派分组的数据。 该数据集由Kaggle用户YamaçEren Ay上传，您可以在这里找到原始数据集 主： id（Spotify生成的轨道的ID） 数值： 声音（范围从0到1） 舞蹈性（范围从0到1） 能量（范围从0到1） duration_ms（整数通常为200k至300​​k） 工具性（范围从0到1） 价（范围从0到1） 受欢迎程度（范围从0到100） 速度（

Spinger中的书籍 介绍关于社交网络

SQL-for-Data-Analytics：借助SQL的功能执行快速而有效的数据分析

Erudite yet real-world relevant. It's true that predictive analytics and machine learning go hand-in-hand: To put it loosely, prediction depends on learning from past examples. And, while Fundamentals succeeds as a comprehensive university textbook covering exactly how that works, the authors also recognize that predictive analytics is today's most booming commercial application of machine learning. So, in an unusual turn, this highly enriching opus brings the concepts to light with industry case studies and best practices, ensuring you'll experience the real-world value and avoid getting lost in abstraction. ―Eric Siegel, Ph.D., founder of Predictive Analytics World; author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

Spatio-Temporal Graph Data Analytics 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

Gain a solid knowledge of vital Data Analytics concepts via practical use cases Create elegant data visualizations using Jupyter Run, process, and analyze large chunks of datasets using PySpark Utilize Spark SQL to easily load big data into DataFrames Create fast and scalable Machine Learning applications using MLlib with Spark Perform exploratory Data Analysis in a scalable way Achieve scalable, high-throughput and fault-tolerant processing of data streams using Spark Streaming

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Amazon Kinesis Data Analytics Java示例 用于Kinesis Data Analytics的示例Java应用程序，演示了源，接收器和运算符。 许可证摘要 该示例代码在MIT-0许可下提供。 请参阅许可文件。

Big Data Analytics with Hadoop 3: Build highly effective analytics solutions to gain valuable insight into your big data Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3 Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.