Delve into the concepts and practices of PostgreSQL server-side programming with this practical and insightful guide. Learn how to extend PostgreSQL functionality through user-defined functions, various procedural languages, and effective debugging techniques. Gain a deeper understanding of PostgreSQL 9.4's features to optimize your database's capabilities. What this Book will help me do Master PostgreSQL's PL/pgSQL and other procedural languages for server-side programming. Craft powerful user-defined functions to provide database functionality specific to your needs. Explore debugging techniques and tools, including PL/pgSQL debugging extensions and NOTIFY. Scale and optimize databases effectively using tools like PL/Proxy. Leverage new features in PostgreSQL 9.4, such as event triggers, to enhance database performance. Author(s) The book is authored by experienced PostgreSQL professionals None Dar, None Krosing, and Jim Mlodgenski. Together, they bring years of expertise in database design, architecture, and development. Their combined backgrounds ensure a comprehensive and practical learning experience for readers. They aim to share practical insights and structured knowledge for database enthusiasts. Who is it for? This book is ideal for database professionals with a moderate to advanced understanding of PostgreSQL. Readers should have experience with SQL, query optimization concepts, and basic programming in languages like Python, Perl, or C. If you are aiming to enhance your knowledge of PostgreSQL in-depth capabilities and get hands-on with advanced features such as server programming and database scale optimization, this book is suitable for you.

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.

My quest this week is noteworthy a.i. researcher Randy Olson who joins me to share his work creating the Reddit World Map - a visualization that illuminates clusters in the reddit community based on user behavior. Randy's blog post on created the reddit world map is well complimented by a more detailed write up titled Navigating the massive world of reddit: using backbone networks to map user interests in social media. Last but not least, an interactive version of the results (which leverages Gephi) can be found here. For a benevolent recommendation, Randy suggetss people check out Seaborn - a python library for statistical data visualization. For a self serving recommendation, Randy recommends listeners visit the Data is beautiful subreddit where he's a moderator.

Apache ZooKeeper Essentials is your comprehensive guide to understanding and utilizing Apache ZooKeeper for coordinating services in distributed systems. This book offers a clear and practical approach to ZooKeeper's architecture and programming, focusing on its application in real-world scenarios. What this Book will help me do Understand the architecture and operational design of Apache ZooKeeper. Effectively use ZooKeeper to coordinate distributed systems. Implement ZooKeeper programming using languages such as Java, C, or Python. Administer and manage ZooKeeper servers and clusters. Utilize tools like Apache Curator to enhance your ZooKeeper experience. Author(s) None Haloi, the author of Apache ZooKeeper Essentials, brings extensive experience in distributed systems and software development. Their expertise ensures a clear and approachable style, ideal for technical learners. Their passion for sharing knowledge is evident through practical examples and focus on real-world applications. Who is it for? This book is ideal for software developers, system architects, and engineers who are looking to enhance their knowledge of distributed systems. Readers should have foundational programming knowledge in languages like Java, C, or Python. While prior experience with ZooKeeper isn't necessary, familiarity with distributed computing will enable you to gain the most from this guide. If you're interested in learning how to leverage ZooKeeper effectively, this book is for you.

The "ElasticSearch Cookbook - Second Edition" is a hands-on guide featuring over 130 advanced recipes to help you harness the power of ElasticSearch, a leading search and analytics engine. Through insightful examples and practical guidance, you'll learn to implement efficient search solutions, optimize queries, and manage ElasticSearch clusters effectively. What this Book will help me do Design and configure ElasticSearch topologies optimized for your specific deployment needs. Develop and utilize custom mappings to optimize your data indexes. Execute advanced queries and filters to refine and retrieve search results effectively. Set up and monitor ElasticSearch clusters for optimal performance. Extend ElasticSearch capabilities through plugin development and integrations using Java and Python. Author(s) Alberto Paro is a technology expert with years of experience working with ElasticSearch, Big Data solutions, and scalable cloud architecture. He has authored multiple books and technical articles on ElasticSearch, leveraging his extensive knowledge to provide practical insights. His approachable and detail-oriented style makes complex concepts accessible to technical professionals. Who is it for? This book is best suited for software developers and IT professionals looking to use ElasticSearch in their projects. Readers should be familiar with JSON, as well as basic programming skills in Java. It is ideal for those who have an understanding of search applications and want to deepen their expertise. Whether you're integrating ElasticSearch into a web application or optimizing your system's search capabilities, this book will provide the skills and knowledge you need.

Each new print copy includes Navigate 2 Advantage Access that unlocks a comprehensive and interactive eBook, student practice activities and assessments, a full suite of instructor resources, and learning analytics reporting tools.

Fully revised and updated, the Sixth Edition of the best-selling text Computer Science Illuminated retains the accessibility and in-depth coverage of previous editions, while incorporating all-new material on cutting-edge issues in computer science. Authored by the award-winning Nell Dale and John Lewis, Computer Science Illuminated’s unique and innovative layered approach moves through the levels of computing from an organized, language-neutral perspective.

Designed for the introductory computing and computer science course, this student-friendly Sixth Edition provides students with a solid foundation for further study, and offers non-majors a complete introduction to computing.

Key Features of the Sixth Edition include:

Access to Navigate 2 online learning materials including a comprehensive and interactive eBook, student practice activities and assessments, learning analytics reporting tools, and more
Completely revised sections on HTML and CSS
Updates regarding Top Level Domains, Social Networks, and Google Analytics
All-new section on Internet management, including ICANN control and net neutrality 
New design, including fully revised figures and tables
New and updated Did You Know callouts are included in the chapter margins
New and revised Ethical Issues and Biographies throughout emphasize the history and breadth of computing
Available in our customizable PUBLISH platform

A collection of programming language chapters are available as low-cost bundling options. Available chapters include: Java, C++, Python, Alice, SQL, VB.NET, RUBY, Perl, Pascal, and JavaScript.

With Navigate 2, technology and content combine to expand the reach of your classroom. Whether you teach an online, hybrid, or traditional classroom-based course, Navigate 2 delivers unbeatable value. Experience Navigate 2 today at www.jblnavigate.com/2

Wring more out of the data with a scientific approach to analysis Graph Analysis and Visualization brings graph theory out of the lab and into the real world. Using sophisticated methods and tools that span analysis functions, this guide shows you how to exploit graph and network analytic techniques to enable the discovery of new business insights and opportunities. Published in full color, the book describes the process of creating powerful visualizations using a rich and engaging set of examples from sports, finance, marketing, security, social media, and more. You will find practical guidance toward pattern identification and using various data sources, including Big Data, plus clear instruction on the use of software and programming. The companion website offers data sets, full code examples in Python, and links to all the tools covered in the book. Science has already reaped the benefit of network and graph theory, which has powered breakthroughs in physics, economics, genetics, and more. This book brings those proven techniques into the world of business, finance, strategy, and design, helping extract more information from data and better communicate the results to decision-makers. Study graphical examples of networks using clear and insightful visualizations Analyze specifically-curated, easy-to-use data sets from various industries Learn the software tools and programming languages that extract insights from data Code examples using the popular Python programming language There is a tremendous body of scientific work on network and graph theory, but very little of it directly applies to analyst functions outside of the core sciences - until now. Written for those seeking empirically based, systematic analysis methods and powerful tools that apply outside the lab, Graph Analysis and Visualization is a thorough, authoritative resource.

" Why have developers at places like Facebook and Twitter increasingly turned to graph databases to manage their highly connected big data? The short answer is that graphs offer superior speed and flexibility to get the job done. It’s time you added skills in graph databases to your toolkit. In Practical Neo4j, database expert Greg Jordan guides you through the background and basics of graph databases and gets you quickly up and running with Neo4j, the most prominent graph database on the market today. Jordan walks you through the data modeling stages for projects such as social networks, recommendation engines, and geo-based applications. The book also dives into the configuration steps as well as the language options used to create your Neo4j-backed applications. Neo4j runs some of the largest connected datasets in the world, and developing with it offers you a fast, proven NoSQL database option. Besides those working for social media, database, and networking companies of all sizes, academics and researchers will find Neo4j a powerful research tool that can help connect large sets of diverse data and provide insights that would otherwise remain hidden. Using Practical Neo4j, you will learn how to harness that power and create elegant solutions that address complex data problems. This book: Explains the basics of graph databases Demonstrates how to configure and maintain Neo4j Shows how to import data into Neo4j from a variety of sources Provides a working example of a Neo4j-based application using an array of language of options including Java, .Net, PHP, Python, Spring, and Ruby As you’ll discover, Neo4j offers a blend of simplicity and speed while allowing data relationships to maintain first-class status. That’s one reason among many that such a wide range of industries and fields have turned to graph databases to analyze deep, dense relationships. After reading this book, you’ll have a potent, elegant tool you can use to develop projects profitably and improve your career options.

Beginning Apache Cassandra Development introduces you to one of the most robust and best-performing NoSQL database platforms on the planet. Apache Cassandra is a document database following the JSON document model. It is specifically designed to manage large amounts of data across many commodity servers without there being any single point of failure. This design approach makes Apache Cassandra a robust and easy-to-implement platform when high availability is needed. Apache Cassandra can be used by developers in Java, PHP, Python, and JavaScript—the primary and most commonly used languages. In Beginning Apache Cassandra Development, author and Cassandra expert Vivek Mishra takes you through using Apache Cassandra from each of these primary languages. Mishra also covers the Cassandra Query Language (CQL), the Apache Cassandra analog to SQL. You'll learn to develop applications sourcing data from Cassandra, query that data, and deliver it at speed to your application's users. Cassandra is one of the leading NoSQL databases, meaning you get unparalleled throughput and performance without the sort of processing overhead that comes with traditional proprietary databases. Beginning Apache Cassandra Development will therefore help you create applications that generate search results quickly, stand up to high levels of demand, scale as your user base grows, ensure operational simplicity, and—not least—provide delightful user experiences.

Need a quick and easy to understand introduction to MongoDB and NoSQL databases? MongoDB Basics, from The Definitive Guide to MongoDB, 2E, shows you how a document-oriented database system differs from a relational database, and how to install and get started using it. You'll also learn MongoDB design basics, including geospatial indexing, how to navigate, view, and query your database, and how to use GridFS with a bit of Python.

Oracle Enterprise Manager 12c Command-Line Interface shows how to use Enterprise Manager’s powerful scripting language to automate your database administration work and save time by scripting routine tasks, and then executing those scripts across collections of databases and instances in your environment. This book is chock full of ready-made scripting examples contributed by the authors and leading members of the community. For example, you'll find scripts and examples of commands to: Remove an Enterprise Manager agent and its related targets Quickly create administrator accounts that are fully-configured with pre-expired passwords and all needed roles Invoke batch files to execute sequences of related commands with consistency against multiple targets Batch create large groups of user logins with a single command and more! The Enterprise Manager Command Line Interface (EM CLI) is the administrator's key to unlocking the power of Enterprise Manager 12c (EM12c) with scalability, repeatability, and confidence. In previous versions, most administrators ventured into the command-line interface only with the assistance of Oracle Support. But now there are many features in EM12c that are accessible only from the command-line. This is far from a disadvantage! Enterprise Manager is now a powerful tool for automation in the hands of a skilled database administrator. Enterprise Manager scripting offers potential for all administrators who manage Oracle’s enterprise-level products in their environment. You can automate from the smallest, single-instance configuration all the way up to a broadly distributed enterprise-level rollout having database instances strewn across broad geographical distributions. The power of the EM CLI returns the administrator to the golden age, where the entire environment, from database to application to infrastructure, can often be managed from this powerful command line tool secured by the robust Enterprise Manager framework. Brings a golden-age of automation to Oracle Database administrators Provides ready-made scripts contributed by leading members of the community Covers advanced techniques involving Jython and Python

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data

This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools. Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on plain text, CSV, HTML/XML, and JSON Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow using Drake Create reusable tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines using GNU Parallel Model data with dimensionality reduction, clustering, regression, and classification algorithms

Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

NoSQL database usage is growing at a stunning 50% per year, as organizations discover NoSQL's potential to address even the most challenging Big Data and real-time database problems. Every NoSQL database is different, but one is the most popular by far: MongoDB. Now, in just 24 lessons of one hour or less, you can learn how to leverage MongoDB's immense power. Each short, easy lesson builds on all that's come before, teaching NoSQL concepts and MongoDB techniques from the ground up. Sams Teach Yourself NoSQL with MongoDB in 24 Hours covers all this, and much more: Learning how NoSQL is different, when to use it, and when to use traditional RDBMSes instead Designing and implementing MongoDB databases of diverse types and sizes Storing and interacting with data via Java, PHP, Python, and Node.js/Mongoose Choosing the right NoSQL distribution model for your application Installing and configuring MongoDB Designing MongoDB data models, including collections, indexes, and GridFS Balancing consistency, performance, and durability Leveraging the immense power of Map-Reduce Administering, monitoring, securing, backing up, and repairing MongoDB databases Mastering advanced techniques such as sharding and replication Optimizing performance

MySQLâ??s popularity has brought a flood of questions about how to solve specific problems, and thatâ??s where this cookbook is essential. When you need quick solutions or techniques, this handy resource provides scores of short, focused pieces of code, hundreds of worked-out examples, and clear, concise explanations for programmers who donâ??t have the time (or expertise) to solve MySQL problems from scratch. Ideal for beginners and professional database and web developers, this updated third edition covers powerful features in MySQL 5.6 (and some in 5.7). The book focuses on programming APIs in Python, PHP, Java, Perl, and Ruby. With more than 200+ recipes, youâ??ll learn how to: Use the mysql client and write MySQL-based programs Create, populate, and select data from tables Store, retrieve, and manipulate strings Work with dates and times Sort query results and generate summaries Use stored routines, triggers, and scheduled events Import, export, validate, and reformat data Perform transactions and work with statistics Process web input, and generate web content from query results Use MySQL-based web session management Provide security and server administration

A Step-by-Step Guide to Describing Biomolecular Structure Computational and Visualization Techniques for Structural Bioinformatics Using Chimera shows how to perform computations with Python scripts in the Chimera environment. It focuses on the three core areas needed to study structural bioinformatics: biochemistry, mathematics, and computation. Understand Important Concepts of Structural Bioinformatics The book covers topics that deal primarily with protein structure and includes many exercises that are grounded in biological problems at the molecular level. The text encourages mathematical analysis by providing a firm foundation for computations. It analyzes numerous Python scripts for the Chimera environment, with the scripts and other material available on a supplementary website. Build Python Scripts to Extend the Capabilities of Chimera Through more than 60 exercises that involve the development of Python scripts, the book gives you concrete guidance on using the scripting capabilities of Chimera. You’ll gain experience in solving real problems as well as understand the various applications of linear algebra. You can also use the scripts as starting points for the development of similar applications and use classes from the StructBio toolkit for computations, such as structure overlap, data plotting, scenographics, and display of residue networks.

This book, 'Learning NumPy Array,' is the ultimate guide to mastering the fundamental library for numerical computing in Python: NumPy. Through concise explanations and practical examples, you will learn how to create and manipulate arrays, perform complex computations, and leverage NumPy's capabilities to streamline data analysis workflows. What this Book will help me do Install and set up NumPy in your Python environment for numerical computing. Create and manipulate multidimensional arrays to handle and process large data sets. Perform complex mathematical and statistical computations with NumPy's built-in methods. Explore time series analysis and signal processing techniques using NumPy. Optimize and improve the performance of Python code leveraging NumPy's efficient operations. Author(s) Ivan Idris is a seasoned programmer and data scientist with a great passion for Python and numerical computing. With years of experience working on data analysis projects, he has solidified his expertise in Python's scientific libraries, including NumPy. Ivan creates practical, reader-friendly guides that not only teach the technical how-to's but also inspire confidence in solving real-world problems. Who is it for? This book is ideal for Python programmers taking their first steps into the world of numerical computing or data analysis. Beginners looking to understand the basics of handling large numerical datasets in Python will find this resource highly enlightening. Developers and scientists wanting to streamline their calculations using efficient techniques will gain valuable insights. If working with Python in a data-driven environment interests you, this book is for you.

How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets Google BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addition to the mechanics of BigQuery, the book also covers the architecture of the underlying Dremel query engine, providing a thorough understanding that leads to better query results. Features a companion website that includes all code and data sets from the book Uses real-world examples to explain everything analysts need to know to effectively use BigQuery Includes web application examples coded in Python

The "matplotlib Plotting Cookbook" equips you with the skills to create impactful scientific visualizations using Python's matplotlib library. Through a series of concise recipes, this book covers everything from basic plotting to advanced techniques, ensuring you can create impressive graphics for your data. What this Book will help me do Learn to produce standard 2D plots like line, bar, and scatter plots. Master advanced plotting techniques such as 3D plotting and data overlays. Enhance plots with detailed annotations, rich legends, and labeling. Understand the use of colors, styles, and scales to maximize readability. Use matplotlib to generate plots programmatically or integrate with applications. Author(s) Alexandre Devert, the author of the "matplotlib Plotting Cookbook," is an experienced data scientist with a strong foundation in Python and data visualization techniques. Alexandre has worked extensively in the field of data analysis, and his expertise is reflected in the practical examples and hands-on guidance provided throughout this book. He takes a learner-focused approach to presenting technical topics in an accessible way. Who is it for? This book is designed for Python developers, data scientists, and researchers who need to create clear, professional-quality visualizations. If you are at a beginner or intermediate level in using matplotlib or visualization libraries, this book will empower you with essential plotting skills. Readers looking to save time while producing meaningful insights through data visualizations will find this book valuable. It is suitable for those aiming to improve their data representation skills for presentations or publications.