Data Teams...A Unified Management Model for Successful Data-Focused Teams

Book

Brief

Learn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does. Most organizations fail with big data projects and the failure is almost always blamed on the technologies used. To be successful, organizations need to focus on both technology and management. Making use of data is a team sport. It takes different kinds of people with different skill sets all working together to get things done. In all but the smallest projects, people should be organized into multiple teams to reduce project failure and underperformance. This book focuses on management. A few years ago, there was little to nothing written or talked about on the management of big data projects or teams. Data Teams shows why management failures are at the root of so many project failures and how to proactively prevent such failures with your project.

DataOps: Powerful Ideas

Brief

Book

Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make DataOps investments work better. This DataOps All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth DataOps Self-Assessment. Featuring 700 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which DataOps improvements can be made.

DataOps Strategy A Complete Guide - 2020 Edition

Brief

Book

Why is database performance so critical now? How much overhead do you afford? Why is open source critical to data as a service? Who knows how big the files are, and modify time? Where is data as a service most useful? This one-of-a-kind DataOps Strategy self-assessment will make you the established DataOps Strategy domain standout by revealing just what you need to know to be fluent and ready for any DataOps Strategy challenge. How do I reduce the effort in the DataOps Strategy work to be done to get problems solved? How can I ensure that plans of action include every DataOps Strategy task and that every DataOps Strategy outcome is in place? How will I save time investigating strategic and tactical options and ensuring DataOps Strategy costs are low? How can I deliver tailored DataOps Strategy advice instantly with structured going-forward plans?

DataOps A Complete Guide - 2020 Edition

Brief

Book

How do you nurture data science capabilities? Have you upgraded your data analytics team to a DataOps team? What are the specific, high-impact data capabilities empowered by Intelligent SDN and related technologies? Who receives training on ICT, data standards or data analytics? How will you use qualitative data analysis to improve? This premium DataOps self-assessment will make you the established DataOps domain specialist by revealing just what you need to know to be fluent and ready for any DataOps challenge.  How do I reduce the effort in the DataOps work to be done to get problems solved? How can I ensure that plans of action include every DataOps task and that every DataOps outcome is in place? How will I save time investigating strategic and tactical options and ensuring DataOps costs are low? How can I deliver tailored DataOps advice instantly with structured going-forward plans?

Data Deduplication Approaches...Concepts, Strategies, and Challenges

Brief

Book

In the age of data science, the rapidly increasing amount of data is a major concern in numerous applications of computing operations and data storage. Duplicated data or redundant data is a main challenge in the field of data science research. Data Deduplication Approaches: Concepts, Strategies, and Challenges shows readers the various methods that can be used to eliminate multiple copies of the same files as well as duplicated segments or chunks of data within the associated files. Due to ever-increasing data duplication, its deduplication has become an especially useful field of research for storage environments, in particular persistent data storage. Data Deduplication Approaches provides readers with an overview of the concepts and background of data deduplication approaches, then proceeds to demonstrate in technical detail the strategies and challenges of real-time implementations of handling big data, data science, data backup, and recovery. The book also includes future research directions, case studies, and real-world applications of data deduplication, focusing on reduced storage, backup, recovery, and reliability.

Developer-Enabled Threat Modeling...Owning Your Role in Risk-Averse Design

Book

Brief

Threat modeling is one of the most essential—and most misunderstood—parts of the development lifecycle. Whether you’re a security practitioner or application developer, this book will help you gain a better understanding of core concepts and how to apply them to your practice to protect your systems from threats. The authors walk you through the myriad ways to approach and execute threat modeling. Contrary to popular belief, the process takes neither incredibly advanced security knowledge nor an unmanageable amount of effort. But it’s critical for spotting and addressing potential concerns in a cost-effective way before the code’s written and it’s too late to find a solution. Find out why threat modeling is important and how it can make you and your team better, more well-rounded architects and developers  Learn the most effective ways to integrate threat modeling into your development lifecycle  Use the results of a threat modeling exercise on other aspects of the system lifecycle

Building Solutions with Microsoft Teams...Understanding the Teams App Developer Platform

Book

Brief

Explore Microsoft Teams and use its principal tools such as Node.js, npm, Yeoman, Gulp, TypeScript, and React to help you develop for Teams better. This book covers the core components and use cases for Teams apps and guides you through ideas for automation, provisioning, and implementation. Building Solutions with Microsoft Teams starts with an overview of the Microsoft Teams developer platform followed by how to set up your environment for building apps and solutions with Teams. You will then go through various features of conversational bots and learn how to create a bot. You will gain an understanding of the messaging extension and command actions along with tabs for personal, groups, and teams contexts. Moving forward, you will work with SharePoint and Teams together via SharePoint Framework. Finally, you will manage the Teams life cycle and see design guidelines supported by various case studies. After reading this book, you will be able to integrate solutions from Power Apps, Power Automate, Power BI, and Power Virtual agents by using accelerators. You will also be able to leverage your existing skills from SharePoint Framework development.

Which Cloud?... A Developer's and Architect's Guide...

Brief

Book

The book addresses three key challenges: Understanding equivalent services to migrate to from a different cloud platform. Understanding the service and feature-level values gained across the different cloud platforms. Understanding effective ways to plan for or architect a cross-cloud solution. The authors begin with service-level comparisons, but they also dig into feature-level comparisons. They compare all three platforms together, to make it simple to understand the differences between all three cloud platforms. They use matrices, diagrams, and conceptual explanations to walk the reader through the comparisons. By the end of this book, you’ll have reached the next point in your cloud solution journey. Maybe that means you’re a student… you’ll now have a much stronger high-level view of all the platforms (and understand how to compare them). Or, if you’re a seasoned architect or developer, you’ll now have a better understanding of how to do your job… whether it’s for cross-cloud solutions, migrations, decision-making, or simply improved communication with coworkers.

Designing Data-Intensive Applications...The Big Ideas Behind

Brief

Book

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, the author  helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

Natural Language Processing...Adaptive Computation and Machine Learning series

Brief

Book

This textbook provides a technical perspective on natural language processing―methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning.  The sections does one or more of the following; establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. introduces structured representations of language, including sequences, trees, and graphs.  explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings.  offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation.  End-of-chapter exercises include both paper-and-pencil analysis and software implementation.  After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.

Reinforcement Learning...Adaptive Computation and Machine Learning series

Brief

Book

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.