Cassandra: The Definitive Guide, 3rd Edition
Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you'll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This third edition - updated for Cassandra 4.0 - provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra's nonrelational design, with special attention to data modeling. If you're a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra's speed and flexibility. Understand Cassandra's distributed and decentralized structure; Use the Cassandra Query Language (CQL) and cqlsh - the CQL shell; Create a working data model and compare it with an equivalent relational model; Develop sample applications using ...
Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you'll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This third edition - updated for Cassandra 4.0 - provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra's nonrelational design, with special attention to data modeling. If you're a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra's speed and flexibility. Understand Cassandra's distributed and decentralized structure; Use the Cassandra Query Language (CQL) and cqlsh - the CQL shell; Create a working data model and compare it with an equivalent relational model; Develop sample applications using ...
Practical Statistics for Data Scientists, 2nd Edition
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher-quality dataset, even with big data; How the principles of experimental design yield definitive answers to ...
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher-quality dataset, even with big data; How the principles of experimental design yield definitive answers to ...
High Performance Python, 2nd Edition
Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python's implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers; Learn how Python abstracts the underlying computer architecture; Use profiling to find bottlenecks in CPU time and memory usage; Write efficient programs by choosing appropriate data structures; Speed up matrix ...
Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python's implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers; Learn how Python abstracts the underlying computer architecture; Use profiling to find bottlenecks in CPU time and memory usage; Write efficient programs by choosing appropriate data structures; Speed up matrix ...
Chaos Engineering
As more companies move toward microservices and other distributed technologies, the complexity of these systems increases. You can't remove the complexity, but through Chaos Engineering you can discover vulnerabilities and prevent outages before they impact your customers. This practical guide shows engineers how to navigate complex systems while optimizing to meet business goals. Two of the field's prominent figures, Casey Rosenthal and Nora Jones, pioneered the discipline while working together at Netflix. In this book, they expound on the what, how, and why of Chaos Engineering while facilitating a conversation from practitioners across industries. Many chapters are written by contributing authors to widen the perspective across verticals within (and beyond) the software industry. Learn how Chaos Engineering enables your organization to navigate complexity; Explore a methodology to avoid failures within your application, network, and infrastructure; Move from theory to practic ...
As more companies move toward microservices and other distributed technologies, the complexity of these systems increases. You can't remove the complexity, but through Chaos Engineering you can discover vulnerabilities and prevent outages before they impact your customers. This practical guide shows engineers how to navigate complex systems while optimizing to meet business goals. Two of the field's prominent figures, Casey Rosenthal and Nora Jones, pioneered the discipline while working together at Netflix. In this book, they expound on the what, how, and why of Chaos Engineering while facilitating a conversation from practitioners across industries. Many chapters are written by contributing authors to widen the perspective across verticals within (and beyond) the software industry. Learn how Chaos Engineering enables your organization to navigate complexity; Explore a methodology to avoid failures within your application, network, and infrastructure; Move from theory to practic ...
97 Things Every Engineering Manager Should Know
Tap into the wisdom of experts to learn what every engineering manager should know. With 97 short and extremely useful tips for engineering managers, you'll discover new approaches to old problems, pick up road-tested best practices, and hone your management skills through sound advice. Managing people is hard, and the industry as a whole is bad at it. Many managers lack the experience, training, tools, texts, and frameworks to do it well. From mentoring interns to working in senior management, this book will take you through the stages of management and provide actionable advice on how to approach the obstacles you'll encounter as a technical manager. A few of the 97 things you should know: - "Three Ways to Be the Manager Your Report Needs" by Duretti Hirpa - "The First Two Questions to Ask When Your Team Is Struggling" by Cate Huston - "Fire Them!" by Mike Fisher - "The 5 Whys of Organizational Design" by Kellan Elliott-McCrea - "Career Conversations" by Raquel Vélez - ...
Tap into the wisdom of experts to learn what every engineering manager should know. With 97 short and extremely useful tips for engineering managers, you'll discover new approaches to old problems, pick up road-tested best practices, and hone your management skills through sound advice. Managing people is hard, and the industry as a whole is bad at it. Many managers lack the experience, training, tools, texts, and frameworks to do it well. From mentoring interns to working in senior management, this book will take you through the stages of management and provide actionable advice on how to approach the obstacles you'll encounter as a technical manager. A few of the 97 things you should know: - "Three Ways to Be the Manager Your Report Needs" by Duretti Hirpa - "The First Two Questions to Ask When Your Team Is Struggling" by Cate Huston - "Fire Them!" by Mike Fisher - "The 5 Whys of Organizational Design" by Kellan Elliott-McCrea - "Career Conversations" by Raquel Vélez - ...
.NET Microservices
This guide is an introduction to developing microservices-based applications and managing them using containers. It discusses architectural design and implementation approaches using .NET Core and Docker containers. To make it easier to get started with containers and microservices, the guide focuses on a reference containerized and microservice-based application that you can explore. This guide provides foundational development and architectural guidance primarily at a development environment level with a focus on two main technologies: Docker and .NET Core. Our intention is that you read this guide when thinking about your application design without focusing on the infrastructure (cloud or on-premises) of your production environment. You will make decisions about your infrastructure later, when you create your production-ready applications. Therefore, this guide is intended to be infrastructure agnostic and more development-environment-centric. ...
This guide is an introduction to developing microservices-based applications and managing them using containers. It discusses architectural design and implementation approaches using .NET Core and Docker containers. To make it easier to get started with containers and microservices, the guide focuses on a reference containerized and microservice-based application that you can explore. This guide provides foundational development and architectural guidance primarily at a development environment level with a focus on two main technologies: Docker and .NET Core. Our intention is that you read this guide when thinking about your application design without focusing on the infrastructure (cloud or on-premises) of your production environment. You will make decisions about your infrastructure later, when you create your production-ready applications. Therefore, this guide is intended to be infrastructure agnostic and more development-environment-centric. ...
SQL Server 2019 Administration Inside Out
Dive into SQL Server 2019 administration - and really put your SQL Server DBA expertise to work. This supremely organized reference packs hundreds of timesaving solutions, tips, and workarounds - all you need to plan, implement, manage, and secure SQL Server 2019 in any production environment: on-premises, cloud, or hybrid. Six experts thoroughly tour DBA capabilities available in SQL Server 2019 Database Engine, SQL Server Data Tools, SQL Server Management Studio, PowerShell, and Azure Portal. You'll find extensive new coverage of Azure SQL, big data clusters, PolyBase, data protection, automation, and more. Discover how experts tackle today's essential tasks - and challenge yourself to new levels of mastery. Explore SQL Server 2019's toolset, including the improved SQL Server Management Studio, Azure Data Studio, and Configuration Manager; Design, implement, manage, and govern on-premises, hybrid, or Azure database infrastructures; Install and configure SQL Server on Windows and L ...
Dive into SQL Server 2019 administration - and really put your SQL Server DBA expertise to work. This supremely organized reference packs hundreds of timesaving solutions, tips, and workarounds - all you need to plan, implement, manage, and secure SQL Server 2019 in any production environment: on-premises, cloud, or hybrid. Six experts thoroughly tour DBA capabilities available in SQL Server 2019 Database Engine, SQL Server Data Tools, SQL Server Management Studio, PowerShell, and Azure Portal. You'll find extensive new coverage of Azure SQL, big data clusters, PolyBase, data protection, automation, and more. Discover how experts tackle today's essential tasks - and challenge yourself to new levels of mastery. Explore SQL Server 2019's toolset, including the improved SQL Server Management Studio, Azure Data Studio, and Configuration Manager; Design, implement, manage, and govern on-premises, hybrid, or Azure database infrastructures; Install and configure SQL Server on Windows and L ...
React in patterns
This book about common design patterns / techniques used while developing with React. It includes techniques for composition, data flow, dependency management and more. ...
This book about common design patterns / techniques used while developing with React. It includes techniques for composition, data flow, dependency management and more. ...
Algorithms
Algorithms are the lifeblood of computer science. They are the machines that proofs build and the music that programs play. Their history is as old as mathematics itself. This book is a wide-ranging, idiosyncratic treatise on the design and analysis of algorithms, covering several fundamental techniques, with an emphasis on intuition and the problem-solving process. The book includes important classical examples, hundreds of battle-tested exercises, far too many historical digressions, and exaclty four typos. Jeff Erickson is a computer science professor at the University of Illinois, Urbana-Champaign; this book is based on algorithms classes he has taught there since 1998. ...
Algorithms are the lifeblood of computer science. They are the machines that proofs build and the music that programs play. Their history is as old as mathematics itself. This book is a wide-ranging, idiosyncratic treatise on the design and analysis of algorithms, covering several fundamental techniques, with an emphasis on intuition and the problem-solving process. The book includes important classical examples, hundreds of battle-tested exercises, far too many historical digressions, and exaclty four typos. Jeff Erickson is a computer science professor at the University of Illinois, Urbana-Champaign; this book is based on algorithms classes he has taught there since 1998. ...
TensorFlow Roadmap
A deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms and designing architectures is the software environment. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility is designing highly modular model which also can be a disadvantage too for beginners since lots of the pieces must be considered together for creating the model. This issue has been facilitated as well by developing high-level APIs such as Keras and Slim which gather lots of the design puzzle pieces. The interesting point about TensorFlow is that its trace can be found anywhere these days. Lots of the researchers and developers are using it and its community is growing with the speed of light! So the possible issues can be overcame easily since they might be the issues of lots of other people considering a large number of people involved in TensorFlow community. ...
A deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms and designing architectures is the software environment. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility is designing highly modular model which also can be a disadvantage too for beginners since lots of the pieces must be considered together for creating the model. This issue has been facilitated as well by developing high-level APIs such as Keras and Slim which gather lots of the design puzzle pieces. The interesting point about TensorFlow is that its trace can be found anywhere these days. Lots of the researchers and developers are using it and its community is growing with the speed of light! So the possible issues can be overcame easily since they might be the issues of lots of other people considering a large number of people involved in TensorFlow community. ...