5 Databases and SQL eBooks

Gordon S. Linoff, "Data Analysis Using SQL and Excel, 2nd Edition"
Allan Visochek, "Practical Data Wrangling"
Data-driven Organization Design: Sustaining the Competitive Edge Through Organizational Analytics by Rupert Morrison
Handbook on Data Centers By Samee U. Khan
Learning Real-time Processing with Spark Streaming

Gordon S. Linoff, "Data Analysis Using SQL and Excel, 2nd Edition"
2016 | ISBN-10: 111902143X | 936 pages | true EPUB | 9 MB
Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis SQL and Excel to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You′ll learn the fundamental techniques before moving into the "where" and "why" of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non–standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way.
Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS.
Understand core analytic techniques that work with SQL and Excel
Ensure your analytic approach gets you the results you need
Design and perform your analysis using SQL and Excel
Data Analysis Using SQL and Excel, 2nd Edition shows you how to best use the tools you already know to achieve expert results.

Allan Visochek, "Practical Data Wrangling"
English | ISBN: 1787286134 | 2017 | EPUB | 288 pages | 4 MB
Key Features
An easy to follow guide taking you through every step of the data wrangling process in the best possible way
Work with different types of datasets, and reshape the layout of your data to make it easier for analysis
Simple examples and real-life data wrangling solutions for data pre-processing
Book Description
Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, and important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages which can be best used to manipulate different kinds of data, as per your requirement. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them.
You will start with understanding the data wrangling process and get a solid foundation for working with different types of data. You will work with different data structures and aqquire and parse data from various locations. The book will also show you how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, the book includes a quick primer on accessing and processing data from databases, conduct data exploration, and store and retrieve data quickly using databases.
The book will include practical examples on each of the above pointers using simple and real-world datasets for easier understanding. By the end of the book, you will have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way.
What you will learn
Read a csv file into python and R, and print out some statistics on the data.
Gain knowledge of the data formats and programming stuctures involved in retrieving API data.
Make effective use of regular expression in the data wrangling process.
Explore the tools and packages available for preparing numerical data for analysis.
Learn how to have better control over the manupulation of the structure of the data.
Create a dexterity for programmatically reading, auditing, correcting, and shaping data.
Write and complete programs for taking in, formatting and outputting datasets.

Data-driven Organization Design: Sustaining the Competitive Edge Through Organizational Analytics by Rupert Morrison
English | October 28th, 2015 | ISBN: 0749474416 | 369 Pages | PDF | 4.46 MB
Organizational design practitioners face challenges gathering data to help focus and implement design, understanding the complex nature of organizations, and communicating and sustaining change over a long period of time. Data-driven Organization Design seeks to overcome these challenges, showing how to collect meaningful data and link it to business performance data.
Through the use of case studies, practical tips, and sample exercises, the book explains how to:
• Map an organization by creating and connecting hierarchies and taxonomies
• Link ad-hoc organizational processes to ongoing workforce planning
• Apply new analytical approaches to project planning and management, risk management, and competencies

Handbook on Data Centers By Samee U. Khan
English | EPUB | 2015 | 1334 Pages | ISBN : 149392091X | 17.55 MB
This handbook offers a comprehensive review of the state-of-the-art research achievements in the field of data centers. Contributions from international, leading researchers and scholars offer topics in cloud computing, virtualization in data centers, energy efficient data centers, and next generation data center architecture. It also comprises current research trends in emerging areas, such as data security, data protection management, and network resource management in data centers.
Specific attention is devoted to industry needs associated with the challenges faced by data centers, such as various power, cooling, floor space, and associated environmental health and safety issues, while still working to support growth without disrupting quality of service. The contributions cut across various IT data technology domains as a single source to discuss the interdependencies that need to be supported to enable a virtualized, next-generation, energy efficient, economical, and environmentally friendly data center.
This book appeals to a broad spectrum of readers, including server, storage, networking, database, and applications analysts, administrators, and architects. It is intended for those seeking to gain a stronger grasp on data center networks: the fundamental protocol used by the applications and the network, the typical network technologies, and their design aspects. The Handbook of Data Centers is a leading reference on design and implementation for planning, implementing, and operating data center networks.

Learning Real-time Processing with Spark Streaming
English | 2015 | ISBN: 1783987669 | 198 Pages | True PDF | 5.6 MB
This book is intended for big data developers with basic knowledge of Scala but no knowledge of Spark. It will help you grasp the basics of developing real-time applications with Spark and understand efficient programming of core elements and applications.
What You Will Learn:
- Install and configure Spark and Spark Streaming to execute applications
- Explore the architecture and components of Spark and Spark Streaming to use it as a base for other libraries
- Process distributed log files in real-time to load data from distributed sources
- Apply transformations on streaming data to use its functions
- Integrate Apache Spark with the various advance libraries like MLib and GraphX
- Apply production deployment scenarios to deploy your application
Using practical examples with easy-to-follow steps, this book will teach you how to build real-time applications with Spark Streaming.
Starting with installing and setting the required environment, you will write and execute your first program for Spark Streaming. This will be followed by exploring the architecture and components of Spark Streaming along with an overview of libraries/functions exposed by Spark. Next you will be taught about various client APIs for coding in Spark by using the use-case of distributed log file processing. You will then apply various functions to transform and enrich streaming data. Next you will learn how to cache and persist datasets. Moving on you will integrate Apache Spark with various other libraries/components of Spark like Mlib, GraphX, and Spark SQL. Finally, you will learn about deploying your application and cover the different scenarios ranging from standalone mode to distributed mode using Mesos, Yarn, and private data centers or on cloud infrastructure.