Learning PostgreSQL 10. A beginner’s guide to building high-performance PostgreSQL database solutions - Second Edition

Salahaldin Juba, Andrey Volkov, Salahaldin Juba

PostgreSQL is one of the most popular open source databases in the world, supporting the most advanced features included in SQL standards. This book will familiarize you with the latest features released in PostgreSQL 10.We’ll start with a thorough introduction to PostgreSQL and the new features introduced in PostgreSQL 10. We’ll cover the Data Definition Language (DDL) with an emphasis on PostgreSQL, and the common DDL commands supported by ANSI SQL. You’ll learn to create tables, define integrity constraints, build indexes, and set up views and other schema objects. Moving on, we’ll cover the concepts of Data Manipulation Language (DML) and PostgreSQL server-side programming capabilities using PL/pgSQL. We’ll also explore the NoSQL capabilities of PostgreSQL and connect to your PostgreSQL database to manipulate data objects.By the end of this book, you’ll have a thorough understanding of the basics of PostgreSQL 10 and will have the necessary skills to build efficient database solutions.

Learning PostgreSQL 11. A beginner's guide to building high-performance PostgreSQL database solutions - Third Edition

Salahaldin Juba, Andrey Volkov

PostgreSQL is one of the most popular open source database management systems in the world, and it supports advanced features included in SQL standards. This book will familiarize you with the latest features in PostgreSQL 11, and get you up and running with building efficient PostgreSQL database solutions from scratch.Learning PostgreSQL, 11 begins by covering the concepts of relational databases and their core principles. You’ll explore the Data Definition Language (DDL) and commonly used DDL commands supported by ANSI SQL. You’ll also learn how to create tables, define integrity constraints, build indexes, and set up views and other schema objects. As you advance, you’ll come to understand Data Manipulation Language (DML) and server-side programming capabilities using PL/pgSQL, giving you a robust background to develop, tune, test, and troubleshoot your database application. The book will guide you in exploring NoSQL capabilities and connecting to your database to manipulate data objects. You’ll get to grips with using data warehousing in analytical solutions and reports, and scaling the database for high availability and performance.By the end of this book, you’ll have gained a thorough understanding of PostgreSQL 11 and developed the necessary skills to build efficient database solutions.

Learning PostgreSQL. Create, develop and manage relational databases in real world applications using PostgreSQL

Salahaldin Juba, Andrey Volkov, Salahaldin Juba, Achim Vannahme

PostgreSQL is one of the most powerful and easy to use database management systems. It supports the most advanced features included in SQL standards. The book starts with the introduction of relational databases with PostegreSQL. It then moves on to covering data definition language (DDL) with emphasis on PostgreSQL and common DDL commands supported by ANSI SQL. You will then learn the data manipulation language (DML), and advanced topics like locking and multi version concurrency control (MVCC). This will give you a very robust background to tune and troubleshoot your application. The book then covers the implementation of data models in the database such as creating tables, setting up integrity constraints, building indexes, defining views and other schema objects. Next, it will give you an overview about the NoSQL capabilities of PostgreSQL along with Hstore, XML, Json and arrays. Finally by the end of the book, you'll learn to use the JDBC driver and manipulate data objects in the Hibernate framework.

Learning Predictive Analytics with R. Get to grips with key data visualization and predictive analytic skills using R

Eric Mayor

This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data.You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further.The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naïve Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages.

Learning Probabilistic Graphical Models in R. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R

David Bellot, Dan Toomey

Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.We’ll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we’ll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you’ll see the advantage of going probabilistic when you want to do prediction. Next, you’ll master using R packages and implementing its techniques. Finally, you’ll be presented with machine learning applications that have a direct impact in many fields. Here, we’ll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.

Learning PySpark. Click here to enter text

Tomasz Drabas, Denny Lee

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.

Learning Python Design Patterns. - Second Edition

Chetan Giridhar, Gennadiy Zlobin, Anand Balachandran Pillai

With the increasing focus on optimized software architecture and design it is important that software architects think about optimizations in object creation, code structure, and interaction between objects at the architecture or design level. This makes sure that the cost of software maintenance is low and code can be easily reused or is adaptable to change. The key to this is reusability and low maintenance in design patterns.Building on the success of the previous edition, Learning Python Design Patterns, Second Edition will help you implement real-world scenarios with Python’s latest release, Python v3.5. We start by introducing design patterns from the Python perspective. As you progress through the book, you will learn about Singleton patterns, Factory patterns, and Façade patterns in detail. After this, we’ll look at how to control object access with proxy patterns. It also covers observer patterns, command patterns, and compound patterns.By the end of the book, you will have enhanced your professional abilities in software architecture, design, and development.

Learning Qlik Sense: The Official Guide. Get the most out of your Qlik Sense investment with the latest insight and guidance direct from the Qlik Sense team - Second Edition

Christopher Ilacqua, QlikTech International AB, Henric Cronström, James Richardson

The intuitive and powerful Qlik Sense visual analytics software allows anyone to engage in data discovery, to explore your data, and find meaningful insights to empower your business. Qlik Sense lets you easily create personalized reports and visualizations and reveal essential connections to show new opportunities from every angle.Written by members of the Qlik Sense team, this book is the official guide from Qlik to understanding and using their powerful new product with fully updated coverage to the latest features of the most modern edition of Qlik Sense. Benefit from the vision behind the development of Qlik Sense and get to grips with how Qlik Sense can empower you as a data discovery consumer. Learn how to create your own applications for Qlik Sense to customize it to meet your personal needs for business intelligence, and how to oversee and administer the Qlik Sense data architecture. Finally, explore utilizing Qlik Sense to uncover essential data, with practical examples on finding and visualizing intelligence for sales figures, human resources information, travel expense tracking, and demographic data discovery.