Mastering Apache Spark
Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations. This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment. ...
Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations. This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment. ...
Practical Probabilistic Programming
Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In this book, you'll immediately work on practical examples like building a spam filter, diagnosing computer system data problems, and recovering digital images. You'll discover probabilistic inference, where algorithms help make extended predictions about issues like social media usage. Along the way, you'll learn to use functional-style programming for text analysis, object-oriented models to predict social phenomena like the spread of tweets, and open universe models to gauge real-life social media usage. The book also has chapters on how probabilistic models can help in decision making and modeling of dynamic systems. ...
Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In this book, you'll immediately work on practical examples like building a spam filter, diagnosing computer system data problems, and recovering digital images. You'll discover probabilistic inference, where algorithms help make extended predictions about issues like social media usage. Along the way, you'll learn to use functional-style programming for text analysis, object-oriented models to predict social phenomena like the spread of tweets, and open universe models to gauge real-life social media usage. The book also has chapters on how probabilistic models can help in decision making and modeling of dynamic systems. ...
An Introduction to the Analysis of Algorithms, 2nd Edition
Despite growing interest, basic information on methods and models for mathematically analyzing algorithms has rarely been directly accessible to practitioners, researchers, or students. An Introduction to the Analysis of Algorithms, 2ond Edition, organizes and presents that knowledge, fully introducing primary techniques and results in the field. Techniques covered in the first half of the book include recurrences, generating functions, asymptotics, and analytic combinatorics. Structures studied in the second half of the book include permutations, trees, strings, tries, and mappings. ...
Despite growing interest, basic information on methods and models for mathematically analyzing algorithms has rarely been directly accessible to practitioners, researchers, or students. An Introduction to the Analysis of Algorithms, 2ond Edition, organizes and presents that knowledge, fully introducing primary techniques and results in the field. Techniques covered in the first half of the book include recurrences, generating functions, asymptotics, and analytic combinatorics. Structures studied in the second half of the book include permutations, trees, strings, tries, and mappings. ...
Storm Real-time Processing Cookbook
Storm is a free and open source distributed real-time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. Storm is simple, can be used with any programming language, and is a lot of fun to use! Storm Real Time Processing Cookbook will have basic to advanced recipes on Storm for real-time computation. The book begins with setting up the development environment and then teaches log stream processing. This will be followed by real-time payments workflow, distributed RPC, integrating it with other software such as Hadoop and Apache Camel, and more. ...
Storm is a free and open source distributed real-time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. Storm is simple, can be used with any programming language, and is a lot of fun to use! Storm Real Time Processing Cookbook will have basic to advanced recipes on Storm for real-time computation. The book begins with setting up the development environment and then teaches log stream processing. This will be followed by real-time payments workflow, distributed RPC, integrating it with other software such as Hadoop and Apache Camel, and more. ...
Mastering QlikView
QlikView and its new sister product, Qlik Sense, are the leading tools for BI and data discovery. They both feature the ability to consolidate relevant data from multiple sources into a single application, an associative data model to allow you to explore the data the way your brain works, and state-of-the-art visualizations, dashboards, analysis, and reports. The book starts by reviewing the best performance-tuning techniques and then advances to help you discover strategies to improve performance and test scalability with JMeter. You will also learn dimensional data modeling and creating best-practice ETL techniques using the QlikView script and QlikView's graphical ETL tool, Expressor. Following this, you will deploy QlikView Governance Dashboard to import multiple data sources and view all the information in a single location. Finally, you will learn why virtualization is important and what are the best practices for virtualization in QlikView. ...
QlikView and its new sister product, Qlik Sense, are the leading tools for BI and data discovery. They both feature the ability to consolidate relevant data from multiple sources into a single application, an associative data model to allow you to explore the data the way your brain works, and state-of-the-art visualizations, dashboards, analysis, and reports. The book starts by reviewing the best performance-tuning techniques and then advances to help you discover strategies to improve performance and test scalability with JMeter. You will also learn dimensional data modeling and creating best-practice ETL techniques using the QlikView script and QlikView's graphical ETL tool, Expressor. Following this, you will deploy QlikView Governance Dashboard to import multiple data sources and view all the information in a single location. Finally, you will learn why virtualization is important and what are the best practices for virtualization in QlikView. ...
RStudio for R Statistical Computing Cookbook
The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js ...
The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js ...
Algorithms in a Nutshell, 2nd Edition
Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. This updated edition of Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs - with just enough math to let you understand and analyze algorithm performance. With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate. ...
Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. This updated edition of Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs - with just enough math to let you understand and analyze algorithm performance. With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate. ...
Building Machine Learning Systems with Python, 2nd Edition
Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling, creating recommendation systems. Later on, the book covers advanced topics such as topic modeling, basket analysis, and cloud computing. These will extend your abilities and enable you to create large complex systems. ...
Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling, creating recommendation systems. Later on, the book covers advanced topics such as topic modeling, basket analysis, and cloud computing. These will extend your abilities and enable you to create large complex systems. ...
QGIS 2 Cookbook
QGIS is a user-friendly, cross-platform desktop geographic information system used to make maps and analyze spatial data. QGIS allows users to understand, question, interpret, and visualize spatial data in many ways that reveal relationships, patterns, and trends in the form of maps. This book is a collection of simple to advanced techniques that are needed in everyday geospatial work, and shows how to accomplish them with QGIS. You will begin by understanding the different types of data management techniques, as well as how data exploration works. You will then learn how to perform classic vector and raster analysis with QGIS, apart from creating time-based visualizations. Finally, you will learn how to create interactive and visually appealing maps with custom cartography. By the end of this book, you will have all the necessary knowledge to handle spatial data management, exploration, and visualization tasks in QGIS. ...
QGIS is a user-friendly, cross-platform desktop geographic information system used to make maps and analyze spatial data. QGIS allows users to understand, question, interpret, and visualize spatial data in many ways that reveal relationships, patterns, and trends in the form of maps. This book is a collection of simple to advanced techniques that are needed in everyday geospatial work, and shows how to accomplish them with QGIS. You will begin by understanding the different types of data management techniques, as well as how data exploration works. You will then learn how to perform classic vector and raster analysis with QGIS, apart from creating time-based visualizations. Finally, you will learn how to create interactive and visually appealing maps with custom cartography. By the end of this book, you will have all the necessary knowledge to handle spatial data management, exploration, and visualization tasks in QGIS. ...
Distributed Graph Algorithms for Computer Networks
This hands-on textbook/reference presents a comprehensive review of key distributed graph algorithms for computer network applications, with a particular emphasis on practical implementation. Each chapter opens with a concise introduction to a specific problem, supporting the theory with numerous examples, before providing a list of relevant algorithms. These algorithms are described in detail from conceptual basis to pseudocode, complete with graph templates for the stepwise implementation of the algorithm, followed by its analysis. ...
This hands-on textbook/reference presents a comprehensive review of key distributed graph algorithms for computer network applications, with a particular emphasis on practical implementation. Each chapter opens with a concise introduction to a specific problem, supporting the theory with numerous examples, before providing a list of relevant algorithms. These algorithms are described in detail from conceptual basis to pseudocode, complete with graph templates for the stepwise implementation of the algorithm, followed by its analysis. ...