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Engineering Agile Big Data Systems
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Book Synopsis Engineering Agile Big-Data Systems by : Kevin Feeney
Download or read book Engineering Agile Big-Data Systems written by Kevin Feeney and published by CRC Press. This book was released on 2022-09-01 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems.
Book Synopsis Engineering Agile Big-data Systems by : Kevin Feeney
Download or read book Engineering Agile Big-data Systems written by Kevin Feeney and published by . This book was released on 2018 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Agile Data Science by : Russell Jurney
Download or read book Agile Data Science written by Russell Jurney and published by "O'Reilly Media, Inc.". This book was released on 2013-10-15 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track
Download or read book Agile Analytics written by Ken Collier and published by Addison-Wesley. This book was released on 2012 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that. Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets and how to support enormous and fast-growing data volumes. Collier's techniques offer optimal value whether your projects involve "back-end" data management, "front-end" business analysis, or both. Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your Agile DW/BI project community can collaborate toward success Part II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automation Collier brings together proven solutions you can apply right now--whether you're an IT decision-maker, data warehouse professional, database administrator, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better results--and have fun along the way.
Book Synopsis Agile Data Science 2.0 by : Russell Jurney
Download or read book Agile Data Science 2.0 written by Russell Jurney and published by "O'Reilly Media, Inc.". This book was released on 2017-06-07 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track
Book Synopsis Strategic Engineering for Cloud Computing and Big Data Analytics by : Amin Hosseinian-Far
Download or read book Strategic Engineering for Cloud Computing and Big Data Analytics written by Amin Hosseinian-Far and published by Springer. This book was released on 2017-02-13 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book demonstrates the use of a wide range of strategic engineering concepts, theories and applied case studies to improve the safety, security and sustainability of complex and large-scale engineering and computer systems. It first details the concepts of system design, life cycle, impact assessment and security to show how these ideas can be brought to bear on the modeling, analysis and design of information systems with a focused view on cloud-computing systems and big data analytics. This informative book is a valuable resource for graduate students, researchers and industry-based practitioners working in engineering, information and business systems as well as strategy.
Book Synopsis Big Data Strategies for Agile Business by : Bhuvan Unhelkar
Download or read book Big Data Strategies for Agile Business written by Bhuvan Unhelkar and published by CRC Press. This book was released on 2017-09-13 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Agile is a set of values, principles, techniques, and frameworks for the adaptable, incremental, and efficient delivery of work. Big Data is a rapidly growing field that encompasses crucial aspects of data such as its volume, velocity, variety, and veracity. This book outlines a strategic approach to Big Data that will render a business Agile. It discusses the important competencies required to streamline and focus on the analytics and presents a roadmap for implementing such analytics in business.
Book Synopsis Development Methodologies for Big Data Analytics Systems by : Manuel Mora
Download or read book Development Methodologies for Big Data Analytics Systems written by Manuel Mora and published by Springer Nature. This book was released on 2023-11-03 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents research in big data analytics (BDA) for business of all sizes. The authors analyze problems presented in the application of BDA in some businesses through the study of development methodologies based on the three approaches – 1) plan-driven, 2) agile and 3) hybrid lightweight. The authors first describe BDA systems and how they emerged with the convergence of Statistics, Computer Science, and Business Intelligent Analytics with the practical aim to provide concepts, models, methods and tools required for exploiting the wide variety, volume, and velocity of available business internal and external data - i.e. Big Data – and provide decision-making value to decision-makers. The book presents high-quality conceptual and empirical research-oriented chapters on plan-driven, agile, and hybrid lightweight development methodologies and relevant supporting topics for BDA systems suitable to be used for large-, medium-, and small-sized business organizations.
Book Synopsis Data Engineering Best Practices by : Richard J. Schiller
Download or read book Data Engineering Best Practices written by Richard J. Schiller and published by Packt Publishing Ltd. This book was released on 2024-10-11 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.
Book Synopsis Balancing Agile and Disciplined Engineering and Management Approaches for IT Services and Software Products by : Mora, Manuel
Download or read book Balancing Agile and Disciplined Engineering and Management Approaches for IT Services and Software Products written by Mora, Manuel and published by IGI Global. This book was released on 2020-07-10 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: The highly dynamic world of information technology service management stresses the benefits of the quick and correct implementation of IT services. A disciplined approach relies on a separate set of assumptions and principles as an agile approach, both of which have complicated implementation processes as well as copious benefits. Combining these two approaches to enhance the effectiveness of each, while difficult, can yield exceptional dividends. Balancing Agile and Disciplined Engineering and Management Approaches for IT Services and Software Products is an essential publication that focuses on clarifying theoretical foundations of balanced design methods with conceptual frameworks and empirical cases. Highlighting a broad range of topics including business trends, IT service, and software development, this book is ideally designed for software engineers, software developers, programmers, information technology professionals, researchers, academicians, and students.
Book Synopsis Designing Big Data Platforms by : Yusuf Aytas
Download or read book Designing Big Data Platforms written by Yusuf Aytas and published by John Wiley & Sons. This book was released on 2021-07-08 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: DESIGNING BIG DATA PLATFORMS Provides expert guidance and valuable insights on getting the most out of Big Data systems An array of tools are currently available for managing and processing data—some are ready-to-go solutions that can be immediately deployed, while others require complex and time-intensive setups. With such a vast range of options, choosing the right tool to build a solution can be complicated, as can determining which tools work well with each other. Designing Big Data Platforms provides clear and authoritative guidance on the critical decisions necessary for successfully deploying, operating, and maintaining Big Data systems. This highly practical guide helps readers understand how to process large amounts of data with well-known Linux tools and database solutions, use effective techniques to collect and manage data from multiple sources, transform data into meaningful business insights, and much more. Author Yusuf Aytas, a software engineer with a vast amount of big data experience, discusses the design of the ideal Big Data platform: one that meets the needs of data analysts, data engineers, data scientists, software engineers, and a spectrum of other stakeholders across an organization. Detailed yet accessible chapters cover key topics such as stream data processing, data analytics, data science, data discovery, and data security. This real-world manual for Big Data technologies: Provides up-to-date coverage of the tools currently used in Big Data processing and management Offers step-by-step guidance on building a data pipeline, from basic scripting to distributed systems Highlights and explains how data is processed at scale Includes an introduction to the foundation of a modern data platform Designing Big Data Platforms: How to Use, Deploy, and Maintain Big Data Systems is a must-have for all professionals working with Big Data, as well researchers and students in computer science and related fields.
Book Synopsis Enterprise Big Data Engineering, Analytics, and Management by : Atzmueller, Martin
Download or read book Enterprise Big Data Engineering, Analytics, and Management written by Atzmueller, Martin and published by IGI Global. This book was released on 2016-06-01 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significance of big data can be observed in any decision-making process as it is often used for forecasting and predictive analytics. Additionally, big data can be used to build a holistic view of an enterprise through a collection and analysis of large data sets retrospectively. As the data deluge deepens, new methods for analyzing, comprehending, and making use of big data become necessary. Enterprise Big Data Engineering, Analytics, and Management presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation. Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this publication provides a platform for retargeting the current research available in the field. Data analysts, IT professionals, researchers, and graduate-level students will find the timely research presented in this publication essential to furthering their knowledge in the field.
Book Synopsis Big Data Application in Power Systems by : Reza Arghandeh
Download or read book Big Data Application in Power Systems written by Reza Arghandeh and published by Elsevier. This book was released on 2024-07-01 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today’s challenges in this rapidly accelerating area of power engineering. Divided into three parts, this book begins by breaking down the big picture for electric utilities, before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes. Including five brand new chapters on emerging technological solutions, Big Data Application in Power Systems, Second Edition remains an essential resource for the reader aiming to utilize the potential of big data in the power systems of the future. Provides a total refresh to include the most up-to-date research, developments, and challenges Focuses on practical techniques, including rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches for processing high dimensional, heterogeneous, and spatiotemporal data Engages with cross-disciplinary lessons, drawing on the impact of intersectional technology including statistics, computer science, and bioinformatics Includes five brand new chapters on hot topics, ranging from uncertainty decision-making to features, selection methods, and the opportunities provided by social network data
Book Synopsis Big Data Analytics in Agile Software Development by : David Gerald
Download or read book Big Data Analytics in Agile Software Development written by David Gerald and published by . This book was released on 2021-07-04 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last decade, Agile methods have changed the software development process in an unparalleled way. As opposed to traditional, plan-driven models of software development (e.g. waterfall model), where processes are organized in a series of sequentially ordered stages, Agile software development (ASD) entails collaborative development with swift and incremental iterations. As a result, adaptability to fre- quently changing requirements and a strong emphasis on delivering value to customers represent the crux of ASD and have driven its wide acceptance among software practitioners in the last years. Furthermore, this paradigm shift from plan-driven software development processes to ASD accorded with social and technological advances. Keywords: Big Data analytics in Agile software development big data facebook big data baseball big data analysis for green computing concepts and applications big data big climb big data systems big data healthcare big data aws big data science big data mba big data a big data dragon tank big data a revolution that will transform big data a revolution that will transform how we live work and think big data algorithms big data analysis big data analytics big data and health analytics big data and social science big data architect big data architecture big data at work big data band big data big analytics big data big climb big data big design big data book big data dangerous big data demystified big data design big data does size matter big data driven business big data engineer big data engineering big data español big data finance big data for beginners big data for social good big data frameworks big data fundamentals big data fundamentals concepts, drivers & techniques big data genomics big data glossary big data health analytics big data in education big data in finance big data in healthcare big data in practice big data integration big data interview big data lake big data management big data manning big data marketing big data marz big data mba big data mba driving business strategies with data science big data modeling big data on campus big data para ceos y directores de marketing big data platform big data policing big data principles and best practices big data profits success analytics big data project big data project management big data python big data questions and answers big data race big data real estate big data revolution big data science big data science in finance big data security big data small wars big data spanish big data spark big data system big data technologies for business big data textbook big data uncharted big data understanding how data powers big business big data using hadoop big data using hadoop and hive big data visualization big data with java big data with spark
Book Synopsis Data-Centric Business and Applications by : Aneta Poniszewska-Marańda
Download or read book Data-Centric Business and Applications written by Aneta Poniszewska-Marańda and published by Springer Nature. This book was released on 2019-12-14 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores various aspects of software creation and development as well as data and information processing. It covers relevant topics such as business analysis, business rules, requirements engineering, software development processes, software defect prediction, information management systems, and knowledge management solutions. Lastly, the book presents lessons learned in information and data management processes and procedures.
Book Synopsis Practical DataOps by : Harvinder Atwal
Download or read book Practical DataOps written by Harvinder Atwal and published by Apress. This book was released on 2019-12-09 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.
Book Synopsis Agile Software Requirements by : Dean Leffingwell
Download or read book Agile Software Requirements written by Dean Leffingwell and published by Addison-Wesley Professional. This book was released on 2010-12-27 with total page 977 pages. Available in PDF, EPUB and Kindle. Book excerpt: “We need better approaches to understanding and managing software requirements, and Dean provides them in this book. He draws ideas from three very useful intellectual pools: classical management practices, Agile methods, and lean product development. By combining the strengths of these three approaches, he has produced something that works better than any one in isolation.” –From the Foreword by Don Reinertsen, President of Reinertsen & Associates; author of Managing the Design Factory; and leading expert on rapid product development Effective requirements discovery and analysis is a critical best practice for serious application development. Until now, however, requirements and Agile methods have rarely coexisted peacefully. For many enterprises considering Agile approaches, the absence of effective and scalable Agile requirements processes has been a showstopper for Agile adoption. In Agile Software Requirements, Dean Leffingwell shows exactly how to create effective requirements in Agile environments. Part I presents the “big picture” of Agile requirements in the enterprise, and describes an overall process model for Agile requirements at the project team, program, and portfolio levels Part II describes a simple and lightweight, yet comprehensive model that Agile project teams can use to manage requirements Part III shows how to develop Agile requirements for complex systems that require the cooperation of multiple teams Part IV guides enterprises in developing Agile requirements for ever-larger “systems of systems,” application suites, and product portfolios This book will help you leverage the benefits of Agile without sacrificing the value of effective requirements discovery and analysis. You’ll find proven solutions you can apply right now–whether you’re a software developer or tester, executive, project/program manager, architect, or team leader.
