| data engineering maturity model: Non-Invasive Data Governance Robert S. Seiner, 2014-09-01 Data-governance programs focus on authority and accountability for the management of data as a valued organizational asset. Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people and culture of an organization. Non-Invasive Data Governance™ focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources. Non-Invasive Data Governance will provide you with a complete set of tools to help you deliver a successful data governance program. Learn how: • Steward responsibilities can be identified and recognized, formalized, and engaged according to their existing responsibility rather than being assigned or handed to people as more work. • Governance of information can be applied to existing policies, standard operating procedures, practices, and methodologies, rather than being introduced or emphasized as new processes or methods. • Governance of information can support all data integration, risk management, business intelligence and master data management activities rather than imposing inconsistent rigor to these initiatives. • A practical and non-threatening approach can be applied to governing information and promoting stewardship of data as a cross-organization asset. • Best practices and key concepts of this non-threatening approach can be communicated effectively to leverage strengths and address opportunities to improve. |
| data engineering maturity model: Fundamentals of Data Engineering Joe Reis, Matt Housley, 2022-06-22 Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle |
| data engineering maturity model: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data engineering maturity model: Interop John Palfrey, Urs Gasser, 2012-06-05 In Interop, technology experts John Palfrey and Urs Gasser explore the immense importance of interoperability -- the standardization and integration of technology -- and show how this simple principle will hold the key to our success in the coming decades and beyond. The practice of standardization has been facilitating innovation and economic growth for centuries. The standardization of the railroad gauge revolutionized the flow of commodities, the standardization of money revolutionized debt markets and simplified trade, and the standardization of credit networks has allowed for the purchase of goods using money deposited in a bank half a world away. These advancements did not eradicate the different systems they affected; instead, each system has been transformed so that it can interoperate with systems all over the world, while still preserving local diversity. As Palfrey and Gasser show, interoperability is a critical aspect of any successful system -- and now it is more important than ever. Today we are confronted with challenges that affect us on a global scale: the financial crisis, the quest for sustainable energy, and the need to reform health care systems and improve global disaster response systems. The successful flow of information across systems is crucial if we are to solve these problems, but we must also learn to manage the vast degree of interconnection inherent in each system involved. Interoperability offers a number of solutions to these global challenges, but Palfrey and Gasser also consider its potential negative effects, especially with respect to privacy, security, and co-dependence of states; indeed, interoperability has already sparked debates about document data formats, digital music, and how to create successful yet safe cloud computing. Interop demonstrates that, in order to get the most out of interoperability while minimizing its risks, we will need to fundamentally revisit our understanding of how it works, and how it can allow for improvements in each of its constituent parts. In Interop, Palfrey and Gasser argue that there needs to be a nuanced, stable theory of interoperability -- one that still generates efficiencies, but which also ensures a sustainable mode of interconnection. Pointing the way forward for the new information economy, Interop provides valuable insights into how technological integration and innovation can flourish in the twenty-first century. |
| data engineering maturity model: Summary of Joe Reis & Matt Housley's Fundamentals of Data Engineering Milkyway Media, 2024-04-14 Get the Summary of Joe Reis & Matt Housley’s Fundamentals of Data Engineering in 20 minutes. Please note: This is a summary & not the original book. In Fundamentals of Data Engineering (2022), data experts Joe Reis and Matt Housley provide a comprehensive overview of the field, from foundational concepts to advanced practices. They outline the data engineering lifecycle, with a detailed guide for planning and building systems that meet any organization ’ s needs. They explain how to evaluate and integrate the best technologies available, ensuring the architecture is robust and efficient... |
| data engineering maturity model: The Capability Maturity Model Mark C. Paulk, 1995 Principal Contributors and Editors: Mark C. Paulk, Charles V. Weber, Bill Curtis, Mary Beth Chrissis In every sense, the CMM represents the best thinking in the field today... this book is targeted at anyone involved in improving the software process, including members of assessment or evaluation teams, members of software engineering process groups, software managers, and software practitioners... From the Foreword by Watts Humphrey The Capability Maturity Model for Software (CMM) is a framework that demonstrates the key elements of an effective software process. The CMM describes an evolutionary improvement path for software development from an ad hoc, immature process to a mature, disciplined process, in a path laid out in five levels. When using the CMM, software professionals in government and industry can develop and improve their ability to identify, adopt, and use sound management and technical practices for delivering quality software on schedule and at a reasonable cost. This book provides a description and technical overview of the CMM, along with guidelines for improving software process management overall. It is a sequel to Watts Humphrey's important work, Managing the Software Process, in that it structures the maturity framework presented in that book more formally. Features: Compares the CMM with ISO 9001 Provides an overview of ISO's SPICE project, which is developing international standards for software process improvement and capability determination Presents a case study of IBM Houston's Space Shuttle project, which is frequently referred to as being at Level 5 0201546647B04062001 |
| data engineering maturity model: Model and Data Engineering Klaus-Dieter Schewe, Neeraj Kumar Singh, 2019-10-21 This book constitutes the refereed proceedings of the 9th International Conference on Model and Data Engineering, MEDI 2019, held in Toulouse, France, in October 2019. The 11 full papers and 7 short papers presented in this book were carefully reviewed and selected from 41 submissions. The papers cover broad research areas on both theoretical, systems and practical aspects. Some papers include mining complex databases, concurrent systems, machine learning, swarm optimization, query processing, semantic web, graph databases, formal methods, model-driven engineering, blockchain, cyber physical systems, IoT applications, and smart systems. |
| data engineering maturity model: Big Data For Dummies Judith S. Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman, 2013-04-02 Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization. |
| data engineering maturity model: Model and Data Engineering Alberto Abelló, Ladjel Bellatreche, Boualem Benatallah, 2012-09-25 This book constitutes the refereed proceedings of the 2nd International Conference on Model and Data Engineering, MEDI 2012, held in Poitiers, France, in October 2012. The 12 revised full papers presented together with 5 short papers were carefully reviewed and selected from 35 submissions. The papers are cover the topics of model driven engineering, ontology engineering, formal modeling, security, and data mining. |
| data engineering maturity model: Data Engineering Best Practices Richard J. Schiller, David Larochelle, 2024-10-11 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. |
| data engineering maturity model: Data Engineering for Smart Systems Priyadarsi Nanda, Vivek Kumar Verma, Sumit Srivastava, Rohit Kumar Gupta, Arka Prokash Mazumdar, 2021-11-13 This book features original papers from the 3rd International Conference on Smart IoT Systems: Innovations and Computing (SSIC 2021), organized by Manipal University, Jaipur, India, during January 22–23, 2021. It discusses scientific works related to data engineering in the context of computational collective intelligence consisted of interaction between smart devices for smart environments and interactions. Thanks to the high-quality content and the broad range of topics covered, the book appeals to researchers pursuing advanced studies. |
| data engineering maturity model: The Self-Service Data Roadmap Sandeep Uttamchandani, 2020-09-10 Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization |
| data engineering maturity model: CMMI for Acquisition Brian Gallagher, Mike Phillips, Karen Richter, Sandra Shrum, 2011-03-04 CMMI® for Acquisition (CMMI-ACQ) describes best practices for the successful acquisition of products and services. Providing a practical framework for improving acquisition processes, CMMI-ACQ addresses the growing trend in business and government for organizations to purchase or outsource required products and services as an alternative to in-house development or resource allocation. Changes in CMMI-ACQ Version 1.3 include improvements to high maturity process areas, improvements to the model architecture to simplify use of multiple models, and added guidance about using preferred suppliers. CMMI® for Acquisition, Second Edition, is the definitive reference for CMMI-ACQ Version 1.3. In addition to the entire revised CMMI-ACQ model, the book includes updated tips, hints, cross-references, and other author notes to help you understand, apply, and quickly find information about the content of the acquisition process areas. The book now includes more than a dozen contributed essays to help guide the adoption and use of CMMI-ACQ in industry and government. Whether you are new to CMMI models or are already familiar with one or more of them, you will find this book an essential resource for managing your acquisition processes and improving your overall performance. The book is divided into three parts. Part One introduces CMMI-ACQ in the broad context of CMMI models, including essential concepts and useful background. It then describes and shows the relationships among all the components of the CMMI-ACQ process areas, and explains paths to the adoption and use of the model for process improvement and benchmarking. Several original essays share insights and real experiences with CMMI-ACQ in both industry and government environments. Part Two first describes generic goals and generic practices, and then details the twenty-two CMMI-ACQ process areas, including specific goals, specific practices, and examples. These process areas are organized alphabetically and are tabbed by process area acronym to facilitate quick reference. Part Three provides several useful resources, including sources of further information about CMMI and CMMI-ACQ, acronym definitions, a glossary of terms, and an index. |
| data engineering maturity model: Implementing the Capability Maturity Model James R. Persse, 2001-08-27 Practical guidelines for an effective implementation of software development processes Designed to ensure effective software development processes, the Capability Maturity Model (CMM)--North America's leading standard for software development--requires companies to complete five steps, or levels, in the development process. But while it is widely adopted by Fortune 500 companies, many others get stuck at the initial planning stage. Focusing on Levels 2 and 3 of the CMM, this book helps readers to get over the hurdle of the two most problematic areas in this process--the project management and software development steps. It offers clear, step-by-step guidance on how to establish basic project management processes to track costs, schedules, and functionality; how to document, standardize, and integrate software processes; and how to improve software quality. |
| data engineering maturity model: Big Data MBA Bill Schmarzo, 2015-12-11 Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions. |
| data engineering maturity model: Simplifying Data Engineering and Analytics with Delta Anindita Mahapatra, Doug May, 2022-07-29 Explore how Delta brings reliability, performance, and governance to your data lake and all the AI and BI use cases built on top of it Key Features • Learn Delta’s core concepts and features as well as what makes it a perfect match for data engineering and analysis • Solve business challenges of different industry verticals using a scenario-based approach • Make optimal choices by understanding the various tradeoffs provided by Delta Book Description Delta helps you generate reliable insights at scale and simplifies architecture around data pipelines, allowing you to focus primarily on refining the use cases being worked on. This is especially important when you consider that existing architecture is frequently reused for new use cases. In this book, you'll learn about the principles of distributed computing, data modeling techniques, and big data design patterns and templates that help solve end-to-end data flow problems for common scenarios and are reusable across use cases and industry verticals. You'll also learn how to recover from errors and the best practices around handling structured, semi-structured, and unstructured data using Delta. After that, you'll get to grips with features such as ACID transactions on big data, disciplined schema evolution, time travel to help rewind a dataset to a different time or version, and unified batch and streaming capabilities that will help you build agile and robust data products. By the end of this Delta book, you'll be able to use Delta as the foundational block for creating analytics-ready data that fuels all AI/BI use cases. What you will learn • Explore the key challenges of traditional data lakes • Appreciate the unique features of Delta that come out of the box • Address reliability, performance, and governance concerns using Delta • Analyze the open data format for an extensible and pluggable architecture • Handle multiple use cases to support BI, AI, streaming, and data discovery • Discover how common data and machine learning design patterns are executed on Delta • Build and deploy data and machine learning pipelines at scale using Delta Who this book is for Data engineers, data scientists, ML practitioners, BI analysts, or anyone in the data domain working with big data will be able to put their knowledge to work with this practical guide to executing pipelines and supporting diverse use cases using the Delta protocol. Basic knowledge of SQL, Python programming, and Spark is required to get the most out of this book. |
| data engineering maturity model: Intelligent Data Engineering and Analytics Suresh Chandra Satapathy, Yu-Dong Zhang, Vikrant Bhateja, Ritanjali Majhi, 2020-08-29 This book gathers the proceedings of the 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), held at NIT Surathkal, Karnataka, India, on 4–5 January 2020. In these proceedings, researchers, scientists, engineers and practitioners share new ideas and lessons learned in the field of intelligent computing theories with prospective applications in various engineering disciplines. The respective papers cover broad areas of the information and decision sciences, and explore both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. Given its scope, the book offers a valuable resource for graduate students in various engineering disciplines. |
| data engineering maturity model: Big Data Bill Schmarzo, 2013-09-23 Leverage big data to add value to your business Social media analytics, web-tracking, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, products, competition, and markets. Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment. The tricky part for busy IT professionals and executives is how to get this done, and that's where this practical book comes in. Big Data: Understanding How Data Powers Big Business is a complete how-to guide to leveraging big data to drive business value. Full of practical techniques, real-world examples, and hands-on exercises, this book explores the technologies involved, as well as how to find areas of the organization that can take full advantage of big data. Shows how to decompose current business strategies in order to link big data initiatives to the organization’s value creation processes Explores different value creation processes and models Explains issues surrounding operationalizing big data, including organizational structures, education challenges, and new big data-related roles Provides methodology worksheets and exercises so readers can apply techniques Includes real-world examples from a variety of organizations leveraging big data Big Data: Understanding How Data Powers Big Business is written by one of Big Data's preeminent experts, William Schmarzo. Don't miss his invaluable insights and advice. |
| data engineering maturity model: The Rails Way Obie Fernandez, 2007-11-16 The expert guide to building Ruby on Rails applications Ruby on Rails strips complexity from the development process, enabling professional developers to focus on what matters most: delivering business value. Now, for the first time, there’s a comprehensive, authoritative guide to building production-quality software with Rails. Pioneering Rails developer Obie Fernandez and a team of experts illuminate the entire Rails API, along with the Ruby idioms, design approaches, libraries, and plug-ins that make Rails so valuable. Drawing on their unsurpassed experience, they address the real challenges development teams face, showing how to use Rails’ tools and best practices to maximize productivity and build polished applications users will enjoy. Using detailed code examples, Obie systematically covers Rails’ key capabilities and subsystems. He presents advanced programming techniques, introduces open source libraries that facilitate easy Rails adoption, and offers important insights into testing and production deployment. Dive deep into the Rails codebase together, discovering why Rails behaves as it does— and how to make it behave the way you want it to. This book will help you Increase your productivity as a web developer Realize the overall joy of programming with Ruby on Rails Learn what’s new in Rails 2.0 Drive design and protect long-term maintainability with TestUnit and RSpec Understand and manage complex program flow in Rails controllers Leverage Rails’ support for designing REST-compliant APIs Master sophisticated Rails routing concepts and techniques Examine and troubleshoot Rails routing Make the most of ActiveRecord object-relational mapping Utilize Ajax within your Rails applications Incorporate logins and authentication into your application Extend Rails with the best third-party plug-ins and write your own Integrate email services into your applications with ActionMailer Choose the right Rails production configurations Streamline deployment with Capistrano |
| data engineering maturity model: System Lifecycle Management Martin Eigner, 2021-08-09 Years of experience in the area of Product Lifecycle Management (PLM) in industry, research and education form the basis for this overview. The author covers the development from PDM via PLM to SysLM (System Lifecycle Management) in the form commonly used today, which are necessary prerequisites for the sustainable development and implementation of IoT/IoS, Industry 4.0 and Engineering 4.0 concepts. The building blocks and properties of future-proof systems for the successful implementation of the concepts of Engineering 4.0 are thereby dedicated to holistic considerations, which also inform in detail. SysLM functions and processes in mechatronic development and design as well as across the entire product lifecycle - from requirements management to the Digital Twin - are covered as examples. SysLM trends such as low code development, cloud, disruptive business models, and bimodality provide an outlook on future developments. The author dedicates the treatment of the agile SysLM introduction to the implementation in the enterprise. The basics are deepened with examples of a concrete SysLM system. |
| data engineering maturity model: The Pragmatic Programmer David Thomas, Andrew Hunt, 2019-07-30 “One of the most significant books in my life.” –Obie Fernandez, Author, The Rails Way “Twenty years ago, the first edition of The Pragmatic Programmer completely changed the trajectory of my career. This new edition could do the same for yours.” –Mike Cohn, Author of Succeeding with Agile , Agile Estimating and Planning , and User Stories Applied “. . . filled with practical advice, both technical and professional, that will serve you and your projects well for years to come.” –Andrea Goulet, CEO, Corgibytes, Founder, LegacyCode.Rocks “. . . lightning does strike twice, and this book is proof.” –VM (Vicky) Brasseur, Director of Open Source Strategy, Juniper Networks The Pragmatic Programmer is one of those rare tech books you’ll read, re-read, and read again over the years. Whether you’re new to the field or an experienced practitioner, you’ll come away with fresh insights each and every time. Dave Thomas and Andy Hunt wrote the first edition of this influential book in 1999 to help their clients create better software and rediscover the joy of coding. These lessons have helped a generation of programmers examine the very essence of software development, independent of any particular language, framework, or methodology, and the Pragmatic philosophy has spawned hundreds of books, screencasts, and audio books, as well as thousands of careers and success stories. Now, twenty years later, this new edition re-examines what it means to be a modern programmer. Topics range from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse. Read this book, and you’ll learn how to: Fight software rot Learn continuously Avoid the trap of duplicating knowledge Write flexible, dynamic, and adaptable code Harness the power of basic tools Avoid programming by coincidence Learn real requirements Solve the underlying problems of concurrent code Guard against security vulnerabilities Build teams of Pragmatic Programmers Take responsibility for your work and career Test ruthlessly and effectively, including property-based testing Implement the Pragmatic Starter Kit Delight your users Written as a series of self-contained sections and filled with classic and fresh anecdotes, thoughtful examples, and interesting analogies, The Pragmatic Programmer illustrates the best approaches and major pitfalls of many different aspects of software development. Whether you’re a new coder, an experienced programmer, or a manager responsible for software projects, use these lessons daily, and you’ll quickly see improvements in personal productivity, accuracy, and job satisfaction. You’ll learn skills and develop habits and attitudes that form the foundation for long-term success in your career. You’ll become a Pragmatic Programmer. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data engineering maturity model: Financial Data Engineering Tamer Khraisha, 2024-10-09 Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical and comprehensive understanding of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer developing a data infrastructure for a financial product possesses not only technical data engineering skills but also a solid understanding of financial domain-specific challenges, methodologies, data ecosystems, providers, formats, technological constraints, identifiers, entities, standards, regulatory requirements, and governance. This book offers a comprehensive, practical, domain-driven approach to financial data engineering, featuring real-world use cases, industry practices, and hands-on projects. You'll learn: The data engineering landscape in the financial sector Specific problems encountered in financial data engineering The structure, players, and particularities of the financial data domain Approaches to designing financial data identification and entity systems Financial data governance frameworks, concepts, and best practices The financial data engineering lifecycle from ingestion to production The varieties and main characteristics of financial data workflows How to build financial data pipelines using open source tools and APIs Tamer Khraisha, PhD, is a senior data engineer and scientific author with more than a decade of experience in the financial sector. |
| data engineering maturity model: Continuous Delivery Jez Humble, David Farley, 2010-07-27 Winner of the 2011 Jolt Excellence Award! Getting software released to users is often a painful, risky, and time-consuming process. This groundbreaking new book sets out the principles and technical practices that enable rapid, incremental delivery of high quality, valuable new functionality to users. Through automation of the build, deployment, and testing process, and improved collaboration between developers, testers, and operations, delivery teams can get changes released in a matter of hours— sometimes even minutes–no matter what the size of a project or the complexity of its code base. Jez Humble and David Farley begin by presenting the foundations of a rapid, reliable, low-risk delivery process. Next, they introduce the “deployment pipeline,” an automated process for managing all changes, from check-in to release. Finally, they discuss the “ecosystem” needed to support continuous delivery, from infrastructure, data and configuration management to governance. The authors introduce state-of-the-art techniques, including automated infrastructure management and data migration, and the use of virtualization. For each, they review key issues, identify best practices, and demonstrate how to mitigate risks. Coverage includes • Automating all facets of building, integrating, testing, and deploying software • Implementing deployment pipelines at team and organizational levels • Improving collaboration between developers, testers, and operations • Developing features incrementally on large and distributed teams • Implementing an effective configuration management strategy • Automating acceptance testing, from analysis to implementation • Testing capacity and other non-functional requirements • Implementing continuous deployment and zero-downtime releases • Managing infrastructure, data, components and dependencies • Navigating risk management, compliance, and auditing Whether you’re a developer, systems administrator, tester, or manager, this book will help your organization move from idea to release faster than ever—so you can deliver value to your business rapidly and reliably. |
| data engineering maturity model: CMMI Ralf Kneuper, 2009 CMMI is a well-known and standardized model for assessing and improving software and systems development processes. It can be used to guide process improvement across a project, a division, or an entire organization. CMMI was developed at the Carnegie Mellon Software Engineering Institute (SEI). The current version, 1.2, was published in 2006 and is being adopted worldwide. This book provides hands-on experience and will help the reader to gain an understanding of CMMI. It is an introduction to the model and its fundamental ideas. Through numerous examples, it helps the reader to get started with CMMI and to understand the interrelationship among model components (practices, goals, and process areas). The book covers the following topics: Model-based process improvement Overview of CMMI components History of CMMI and comparison to CMM Process areas of CMMI models Application, potential, and limitations of CMMI |
| data engineering maturity model: The Enterprise Big Data Framework Jan-Willem Middelburg, 2023-11-03 Businesses who can make sense of the huge influx and complexity of data will be the big winners in the information economy. This comprehensive guide covers all the aspects of transforming enterprise data into value, from the initial set-up of a big data strategy, towards algorithms, architecture and data governance processes. Using a vendor-independent approach, The Enterprise Big Data Framework offers practical advice on how to develop data-driven decision making, detailed data analysis and data engineering techniques. With a focus on business implementation, The Enterprise Big Data Framework includes sections on analysis, engineering, algorithm design and big data architecture, and covers topics such as data preparation and presentation, data modelling, data science, programming languages and machine learning algorithms. Endorsed by leading accreditation and examination institute AMPG International, this book is required reading for the Enterprise Big Data Certifications, which aim to develop excellence in big data practices across the globe. Online resources include sample data for practice purposes. |
| data engineering maturity model: Data Engineering with dbt Roberto Zagni, 2023-06-30 Use easy-to-apply patterns in SQL and Python to adopt modern analytics engineering to build agile platforms with dbt that are well-tested and simple to extend and run Purchase of the print or Kindle book includes a free PDF eBook Key Features Build a solid dbt base and learn data modeling and the modern data stack to become an analytics engineer Build automated and reliable pipelines to deploy, test, run, and monitor ELTs with dbt Cloud Guided dbt + Snowflake project to build a pattern-based architecture that delivers reliable datasets Book Descriptiondbt Cloud helps professional analytics engineers automate the application of powerful and proven patterns to transform data from ingestion to delivery, enabling real DataOps. This book begins by introducing you to dbt and its role in the data stack, along with how it uses simple SQL to build your data platform, helping you and your team work better together. You’ll find out how to leverage data modeling, data quality, master data management, and more to build a simple-to-understand and future-proof solution. As you advance, you’ll explore the modern data stack, understand how data-related careers are changing, and see how dbt enables this transition into the emerging role of an analytics engineer. The chapters help you build a sample project using the free version of dbt Cloud, Snowflake, and GitHub to create a professional DevOps setup with continuous integration, automated deployment, ELT run, scheduling, and monitoring, solving practical cases you encounter in your daily work. By the end of this dbt book, you’ll be able to build an end-to-end pragmatic data platform by ingesting data exported from your source systems, coding the needed transformations, including master data and the desired business rules, and building well-formed dimensional models or wide tables that’ll enable you to build reports with the BI tool of your choice.What you will learn Create a dbt Cloud account and understand the ELT workflow Combine Snowflake and dbt for building modern data engineering pipelines Use SQL to transform raw data into usable data, and test its accuracy Write dbt macros and use Jinja to apply software engineering principles Test data and transformations to ensure reliability and data quality Build a lightweight pragmatic data platform using proven patterns Write easy-to-maintain idempotent code using dbt materialization Who this book is for This book is for data engineers, analytics engineers, BI professionals, and data analysts who want to learn how to build simple, futureproof, and maintainable data platforms in an agile way. Project managers, data team managers, and decision makers looking to understand the importance of building a data platform and foster a culture of high-performing data teams will also find this book useful. Basic knowledge of SQL and data modeling will help you get the most out of the many layers of this book. The book also includes primers on many data-related subjects to help juniors get started. |
| data engineering maturity model: Business Process Maturity Amy Van Looy, 2014-01-27 Organisations face many challenges, which induce them to perform better, and thus to establish mature (or excellent) business processes. As they now face globalisation, higher competitiveness, demanding customers, growing IT possibilities, compliancy rules etc., business process maturity models (BPMMs) have been introduced to help organisations gradually assess and improve their business processes (e.g. CMMI or OMG-BPMM). In fact, there are now so many BPMMs to choose from that organisations risk selecting one that does not fit their needs or one of substandard quality. This book presents a study that distinguishes process management from process orientation so as to arrive at a common understanding. It also includes a classification study to identify the capability areas and maturity types of 69 existing BPMMs, in order to strengthen the basis of available BPMMs. Lastly it presents a selection study to identify criteria for choosing one BPMM from the broad selection, which produced a free online selection tool, BPMM Smart-Selector. |
| data engineering maturity model: Using the Project Management Maturity Model Harold Kerzner, 2011-11-29 Updated for today's businesses-a proven model FOR assessment and ongoing improvement Using the Project Management Maturity Model, Second Edition is the updated edition of Harold Kerzner's renowned book covering his Project Management Maturity Model (PMMM). In this hands-on book, Kerzner offers a unique, industry-validated tool for helping companies of all sizes assess and improve their progress in integrating project management into every part of their organizations. Conveniently organized into two sections, this Second Edition begins with an examination of strategic planning principles and the ways they relate to project management. In the second section, PMMM is introduced with in-depth coverage of the five different levels of development for achieving maturity. Easily adaptable benchmarking instruments for measuring an organization's progress along the maturity curve make this a practical guide for any type of company. Complete with an associated Web site packed with both teaching and learning tools, Using the Project Management Maturity Model, Second Edition helps managers, engineers, project team members, business consultants, and others build a powerful foundation for company improvement and excellence. |
| data engineering maturity model: Service-Oriented Perspectives in Design Science Research Hemant Jain, Atish P. Sinha, Padmal Vitharana, 2011-04-21 This book constitutes the refereed proceedings of the 6th International Conference on Service-Oriented Perspectives in Design Science Research, DERIST 2011, held in Milwaukee, WI, USA, in May 2011. The 29 revised full papers presented together with 5 revised short papers were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections on design theory, design science research strategies, design methods and techniques, design evaluation, design guidelines, service-oriented perspectives in design science, process design, neuroscience in design research, and designing for social media. |
| data engineering maturity model: Diverse Applications and Transferability of Maturity Models Katuu, Shadrack, 2018-10-19 Previously, professionals had to make judgment calls based on subjective criteria, including their own acumen, in their decision making. In order to combat this subjectivity, maturity models can be implemented to allow organizations a means of assessing everyday processes and to offer a path towards advancement using transparent objective criteria. Diverse Applications and Transferability of Maturity Models is a pivotal reference source that provides vital research on the application of maturity models in organizational development in a variety of work environments. While highlighting topics such as open government, archives and records management, enterprise content management, and digital economy, this publication explores methods to help organizations effectively implement plans in any given management system. This book is ideally designed for professionals and researchers seeking current research on a variety of social science and applied science fields including business studies, computer science, digital preservation, information governance, information science, information systems, public administration, records management, and project management. |
| data engineering maturity model: Introduction to Software Process Improvement Gerard O'Regan, 2010-12-16 This textbook is a systematic guide to the steps in setting up a Capability Maturity Model Integration (CMMI) improvement initiative. Readers will learn the project management practices necessary to deliver high-quality software solutions to the customer on time and on budget. The text also highlights how software process improvement can achieve specific business goals to provide a tangible return on investment. Topics and features: supplies review questions, summaries and key topics for each chapter, as well as a glossary of acronyms; describes the CMMI model thoroughly, detailing the five maturity levels; provides a broad overview of software engineering; reviews the activities and teams required to set up a CMMI improvement initiative; examines in detail the implementation of CMMI in a typical organization at each of the maturity levels; investigates the various tools that support organizations in improving their software engineering maturity; discusses the SCAMPI appraisal methodology. |
| data engineering maturity model: Collaborative Enterprise Architecture Stefan Bente, Uwe Bombosch, Shailendra Langade, 2012-08-29 Why collaborative enterprise architecture? -- What is enterprise architecture -- What enterprise architects do: core activities of EA -- EA frameworks -- EA maturity models -- Foundations of collaborative EA -- Towards pragmatism: lean and agile EA -- Inviting to participation: eam 2.0 -- The next steps: taking collaborative EA forward. |
| data engineering maturity model: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data engineering maturity model: Software and Data Technologies José Cordeiro, Slimane Hammoudi, Marten van Sinderen, 2013-12-13 This book constitutes the thoroughly refereed proceedings of the 7th International Conference on Software and Data Technologies, ICSOFT 2012, held in Rome, Italy, in July 2012. The 14 revised full papers presented were carefully reviewed and selected from 127 submissions. The papers focus on the following research topics and applications: programming issues, theoretical aspects of software engineering, management information systems, distributed systems, ubiquity, data interoperability, context understanding. |
| data engineering maturity model: Software Engineering for Embedded Systems Robert Oshana, 2013-04-01 This Expert Guide gives you the techniques and technologies in software engineering to optimally design and implement your embedded system. Written by experts with a solutions focus, this encyclopedic reference gives you an indispensable aid to tackling the day-to-day problems when using software engineering methods to develop your embedded systems. With this book you will learn: - The principles of good architecture for an embedded system - Design practices to help make your embedded project successful - Details on principles that are often a part of embedded systems, including digital signal processing, safety-critical principles, and development processes - Techniques for setting up a performance engineering strategy for your embedded system software - How to develop user interfaces for embedded systems - Strategies for testing and deploying your embedded system, and ensuring quality development processes - Practical techniques for optimizing embedded software for performance, memory, and power - Advanced guidelines for developing multicore software for embedded systems - How to develop embedded software for networking, storage, and automotive segments - How to manage the embedded development process Includes contributions from: Frank Schirrmeister, Shelly Gretlein, Bruce Douglass, Erich Styger, Gary Stringham, Jean Labrosse, Jim Trudeau, Mike Brogioli, Mark Pitchford, Catalin Dan Udma, Markus Levy, Pete Wilson, Whit Waldo, Inga Harris, Xinxin Yang, Srinivasa Addepalli, Andrew McKay, Mark Kraeling and Robert Oshana. - Road map of key problems/issues and references to their solution in the text - Review of core methods in the context of how to apply them - Examples demonstrating timeless implementation details - Short and to- the- point case studies show how key ideas can be implemented, the rationale for choices made, and design guidelines and trade-offs |
| data engineering maturity model: Encyclopedia of Information Science and Technology, First Edition Khosrow-Pour, D.B.A., Mehdi, 2005-01-31 Comprehensive coverage of critical issues related to information science and technology. |
| data engineering maturity model: The Definitive Guide to Google Vertex AI Jasmeet Bhatia, Kartik Chaudhary, 2023-12-29 Implement machine learning pipelines with Google Cloud Vertex AI Key Features Understand the role of an AI platform and MLOps practices in machine learning projects Get acquainted with Google Vertex AI tools and offerings that help accelerate the creation of end-to-end ML solutions Implement Vision, NLP, and recommendation-based real-world ML models on Google Cloud Platform Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWhile AI has become an integral part of every organization today, the development of large-scale ML solutions and management of complex ML workflows in production continue to pose challenges for many. Google’s unified data and AI platform, Vertex AI, directly addresses these challenges with its array of MLOPs tools designed for overall workflow management. This book is a comprehensive guide that lets you explore Google Vertex AI’s easy-to-advanced level features for end-to-end ML solution development. Throughout this book, you’ll discover how Vertex AI empowers you by providing essential tools for critical tasks, including data management, model building, large-scale experimentations, metadata logging, model deployments, and monitoring. You’ll learn how to harness the full potential of Vertex AI for developing and deploying no-code, low-code, or fully customized ML solutions. This book takes a hands-on approach to developing u deploying some real-world ML solutions on Google Cloud, leveraging key technologies such as Vision, NLP, generative AI, and recommendation systems. Additionally, this book covers pre-built and turnkey solution offerings as well as guidance on seamlessly integrating them into your ML workflows. By the end of this book, you’ll have the confidence to develop and deploy large-scale production-grade ML solutions using the MLOps tooling and best practices from Google.What you will learn Understand the ML lifecycle, challenges, and importance of MLOps Get started with ML model development quickly using Google Vertex AI Manage datasets, artifacts, and experiments Develop no-code, low-code, and custom AI solution on Google Cloud Implement advanced model optimization techniques and tooling Understand pre-built and turnkey AI solution offerings from Google Build and deploy custom ML models for real-world applications Explore the latest generative AI tools within Vertex AI Who this book is for If you are a machine learning practitioner who wants to learn end-to-end ML solution development on Google Cloud Platform using MLOps best practices and tools offered by Google Vertex AI, this is the book for you. |
| data engineering maturity model: Data Science and Analytics Strategy Kailash Awati, Alexander Scriven, 2023-04-05 This book describes how to establish data science and analytics capabilities in organisations using Emergent Design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment. Based on their experiences and those of a number of data leaders, the authors provide actionable advice on data technologies, processes, and governance structures so that readers can make choices that are appropriate to their organisational contexts and requirements. The book blends academic research on organisational change and data science processes with real-world stories from experienced data analytics leaders, focusing on the practical aspects of setting up a data capability. In addition to a detailed coverage of capability, culture, and technology choices, a unique feature of the book is its treatment of emerging issues such as data ethics and algorithmic fairness. Data Science and Analytics Strategy: An Emergent Design Approach has been written for professionals who are looking to build data science and analytics capabilities within their organisations as well as those who wish to expand their knowledge and advance their careers in the data space. Providing deep insights into the intersection between data science and business, this guide will help professionals understand how to help their organisations reap the benefits offered by data. Most importantly, readers will learn how to build a fit-for-purpose data science capability in a manner that avoids the most common pitfalls. |
| data engineering maturity model: An Elegant Puzzle Will Larson, 2019-05-20 A human-centric guide to solving complex problems in engineering management, from sizing teams to handling technical debt. There’s a saying that people don’t leave companies, they leave managers. Management is a key part of any organization, yet the discipline is often self-taught and unstructured. Getting to the good solutions for complex management challenges can make the difference between fulfillment and frustration for teams—and, ultimately, between the success and failure of companies. Will Larson’s An Elegant Puzzle focuses on the particular challenges of engineering management—from sizing teams to handling technical debt to performing succession planning—and provides a path to the good solutions. Drawing from his experience at Digg, Uber, and Stripe, Larson has developed a thoughtful approach to engineering management for leaders of all levels at companies of all sizes. An Elegant Puzzle balances structured principles and human-centric thinking to help any leader create more effective and rewarding organizations for engineers to thrive in. |
| data engineering maturity model: Modern Data Strategy Mike Fleckenstein, Lorraine Fellows, 2018-02-12 This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
