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| data engineering testing tools: Software and Data Engineering Wenying Feng, |
| data engineering testing tools: 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 testing tools: Agile Database Techniques Scott Ambler, 2012-09-17 Describes Agile Modeling Driven Design (AMDD) and Test-Driven Design (TDD) approaches, database refactoring, database encapsulation strategies, and tools that support evolutionary techniques Agile software developers often use object and relational database (RDB) technology together and as a result must overcome the impedance mismatch The author covers techniques for mapping objects to RDBs and for implementing concurrency control, referential integrity, shared business logic, security access control, reports, and XML An agile foundation describes fundamental skills that all agile software developers require, particularly Agile DBAs Includes object modeling, UML data modeling, data normalization, class normalization, and how to deal with legacy databases Scott W. Ambler is author of Agile Modeling (0471202827), a contributing editor with Software Development (www.sdmagazine.com), and a featured speaker at software conferences worldwide |
| data engineering testing tools: Data Engineering with Alteryx Paul Houghton, 2022-06-30 Build and deploy data pipelines with Alteryx by applying practical DataOps principles Key Features • Learn DataOps principles to build data pipelines with Alteryx • Build robust data pipelines with Alteryx Designer • Use Alteryx Server and Alteryx Connect to share and deploy your data pipelines Book Description Alteryx is a GUI-based development platform for data analytic applications. Data Engineering with Alteryx will help you leverage Alteryx's code-free aspects which increase development speed while still enabling you to make the most of the code-based skills you have. This book will teach you the principles of DataOps and how they can be used with the Alteryx software stack. You'll build data pipelines with Alteryx Designer and incorporate the error handling and data validation needed for reliable datasets. Next, you'll take the data pipeline from raw data, transform it into a robust dataset, and publish it to Alteryx Server following a continuous integration process. By the end of this Alteryx book, you'll be able to build systems for validating datasets, monitoring workflow performance, managing access, and promoting the use of your data sources. What you will learn • Build a working pipeline to integrate an external data source • Develop monitoring processes for the pipeline example • Understand and apply DataOps principles to an Alteryx data pipeline • Gain skills for data engineering with the Alteryx software stack • Work with spatial analytics and machine learning techniques in an Alteryx workflow Explore Alteryx workflow deployment strategies using metadata validation and continuous integration • Organize content on Alteryx Server and secure user access Who this book is for If you're a data engineer, data scientist, or data analyst who wants to set up a reliable process for developing data pipelines using Alteryx, this book is for you. You'll also find this book useful if you are trying to make the development and deployment of datasets more robust by following the DataOps principles. Familiarity with Alteryx products will be helpful but is not necessary. |
| data engineering testing tools: 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 testing tools: Data Engineering and Data Science Kukatlapalli Pradeep Kumar, Aynur Unal, Vinay Jha Pillai, Hari Murthy, M. Niranjanamurthy, 2023-10-03 DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library. |
| data engineering testing tools: 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 testing tools: Recent Progress in Data Engineering and Internet Technology Ford Lumban Gaol, 2012-03-31 The latest inventions in internet technology influence most of business and daily activities. Internet security, internet data management, web search, data grids, cloud computing, and web-based applications play vital roles, especially in business and industry, as more transactions go online and mobile. Issues related to ubiquitous computing are becoming critical. Internet technology and data engineering should reinforce efficiency and effectiveness of business processes. These technologies should help people make better and more accurate decisions by presenting necessary information and possible consequences for the decisions. Intelligent information systems should help us better understand and manage information with ubiquitous data repository and cloud computing. This book is a compilation of some recent research findings in Internet Technology and Data Engineering. This book provides state-of-the-art accounts in computational algorithms/tools, database management and database technologies, intelligent information systems, data engineering applications, internet security, internet data management, web search, data grids, cloud computing, web-based application, and other related topics. |
| data engineering testing tools: Intelligent Data Engineering and Automated Learning – IDEAL 2020 Cesar Analide, Paulo Novais, David Camacho, Hujun Yin, 2020-10-29 This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.* The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. * The conference was held virtually due to the COVID-19 pandemic. |
| data engineering testing tools: Mastering Data Engineering: Advanced Techniques with Apache Hadoop and Hive Peter Jones, 2024-10-19 Immerse yourself in the realm of big data with Mastering Data Engineering: Advanced Techniques with Apache Hadoop and Hive, your definitive guide to mastering two of the most potent technologies in the data engineering landscape. This book provides comprehensive insights into the complexities of Apache Hadoop and Hive, equipping you with the expertise to store, manage, and analyze vast amounts of data with precision. From setting up your initial Hadoop cluster to performing sophisticated data analytics with HiveQL, each chapter methodically builds on the previous one, ensuring a robust understanding of both fundamental concepts and advanced methodologies. Discover how to harness HDFS for scalable and reliable storage, utilize MapReduce for intricate data processing, and fully exploit data warehousing capabilities with Hive. Targeted at data engineers, analysts, and IT professionals striving to advance their proficiency in big data technologies, this book is an indispensable resource. Through a blend of theoretical insights, practical knowledge, and real-world examples, you will master data storage optimization, advanced Hive functionalities, and best practices for secure and efficient data management. Equip yourself to confront big data challenges with confidence and skill with Mastering Data Engineering: Advanced Techniques with Apache Hadoop and Hive. Whether you're a novice in the field or seeking to expand your expertise, this book will be your invaluable guide on your data engineering journey. |
| data engineering testing tools: Verification, Validation and Testing in Software Engineering Aristides Dasso, Ana Funes, 2007-01-01 This book explores different applications in V & V that spawn many areas of software development -including real time applications- where V & V techniques are required, providing in all cases examples of the applications--Provided by publisher. |
| data engineering testing tools: 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 testing tools: Model and Data Engineering El Hassan Abdelwahed, Ladjel Bellatreche, Mattéo Golfarelli, Dominique Méry, Carlos Ordonez, 2018-10-19 This book constitutes the refereed proceedings of the 8h International Conference on Model and Data Engineering, MEDI 2018, held in Marrakesh, Morocco, in October 2018. The 23 full papers and 4 short papers presented together with 2 invited talks were carefully reviewed and selected from 86 submissions. The papers covered the recent and relevant topics in the areas of databases; ontology and model-driven engineering; data fusion, classsification and learning; communication and information technologies; safety and security; algorithms and text processing; and specification, verification and validation. |
| data engineering testing tools: Site Reliability Engineering Niall Richard Murphy, Betsy Beyer, Chris Jones, Jennifer Petoff, 2016-03-23 The overwhelming majority of a software system’s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google’s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You’ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient—lessons directly applicable to your organization. This book is divided into four sections: Introduction—Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles—Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices—Understand the theory and practice of an SRE’s day-to-day work: building and operating large distributed computing systems Management—Explore Google's best practices for training, communication, and meetings that your organization can use |
| data engineering testing tools: How Google Tests Software James A. Whittaker, Jason Arbon, Jeff Carollo, 2012-03-21 2012 Jolt Award finalist! Pioneering the Future of Software Test Do you need to get it right, too? Then, learn from Google. Legendary testing expert James Whittaker, until recently a Google testing leader, and two top Google experts reveal exactly how Google tests software, offering brand-new best practices you can use even if you’re not quite Google’s size...yet! Breakthrough Techniques You Can Actually Use Discover 100% practical, amazingly scalable techniques for analyzing risk and planning tests...thinking like real users...implementing exploratory, black box, white box, and acceptance testing...getting usable feedback...tracking issues...choosing and creating tools...testing “Docs & Mocks,” interfaces, classes, modules, libraries, binaries, services, and infrastructure...reviewing code and refactoring...using test hooks, presubmit scripts, queues, continuous builds, and more. With these techniques, you can transform testing from a bottleneck into an accelerator–and make your whole organization more productive! |
| data engineering testing tools: Developer Testing Alexander Tarlinder, 2016-09-07 How do successful agile teams deliver bug-free, maintainable software—iteration after iteration? The answer is: By seamlessly combining development and testing. On such teams, the developers write testable code that enables them to verify it using various types of automated tests. This approach keeps regressions at bay and prevents “testing crunches”—which otherwise may occur near the end of an iteration—from ever happening. Writing testable code, however, is often difficult, because it requires knowledge and skills that cut across multiple disciplines. In Developer Testing, leading test expert and mentor Alexander Tarlinder presents concise, focused guidance for making new and legacy code far more testable. Tarlinder helps you answer questions like: When have I tested this enough? How many tests do I need to write? What should my tests verify? You’ll learn how to design for testability and utilize techniques like refactoring, dependency breaking, unit testing, data-driven testing, and test-driven development to achieve the highest possible confidence in your software. Through practical examples in Java, C#, Groovy, and Ruby, you’ll discover what works—and what doesn’t. You can quickly begin using Tarlinder’s technology-agnostic insights with most languages and toolsets while not getting buried in specialist details. The author helps you adapt your current programming style for testability, make a testing mindset “second nature,” improve your code, and enrich your day-to-day experience as a software professional. With this guide, you will Understand the discipline and vocabulary of testing from the developer’s standpoint Base developer tests on well-established testing techniques and best practices Recognize code constructs that impact testability Effectively name, organize, and execute unit tests Master the essentials of classic and “mockist-style” TDD Leverage test doubles with or without mocking frameworks Capture the benefits of programming by contract, even without runtime support for contracts Take control of dependencies between classes, components, layers, and tiers Handle combinatorial explosions of test cases, or scenarios requiring many similar tests Manage code duplication when it can’t be eliminated Actively maintain and improve your test suites Perform more advanced tests at the integration, system, and end-to-end levels Develop an understanding for how the organizational context influences quality assurance Establish well-balanced and effective testing strategies suitable for agile teams |
| data engineering testing tools: Data Engineering and Communication Technology K. Srujan Raju, Roman Senkerik, Satya Prasad Lanka, V. Rajagopal, 2020-01-08 This book includes selected papers presented at the 3rd International Conference on Data Engineering and Communication Technology (ICDECT-2K19), held at Stanley College of Engineering and Technology for Women, Hyderabad, from 15 to 16 March 2019. It features advanced, multidisciplinary research towards the design of smart computing, information systems, and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence, and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment, and industry. |
| data engineering testing tools: AI-DRIVEN DATA ENGINEERING TRANSFORMING BIG DATA INTO ACTIONABLE INSIGHT Eswar Prasad Galla, Chandrababu Kuraku, Hemanth Kumar Gollangi, Janardhana Rao Sunkara, Chandrakanth Rao Madhavaram, ..... |
| data engineering testing tools: Software Testing Ali Mili, Fairouz Tchier, 2015-06-15 Explores and identifies the main issues, concepts, principles and evolution of software testing, including software quality engineering and testing concepts, test data generation, test deployment analysis, and software test management This book examines the principles, concepts, and processes that are fundamental to the software testing function. This book is divided into five broad parts. Part I introduces software testing in the broader context of software engineering and explores the qualities that testing aims to achieve or ascertain, as well as the lifecycle of software testing. Part II covers mathematical foundations of software testing, which include software specification, program correctness and verification, concepts of software dependability, and a software testing taxonomy. Part III discusses test data generation, specifically, functional criteria and structural criteria. Test oracle design, test driver design, and test outcome analysis is covered in Part IV. Finally, Part V surveys managerial aspects of software testing, including software metrics, software testing tools, and software product line testing. Presents software testing, not as an isolated technique, but as part of an integrated discipline of software verification and validation Proposes program testing and program correctness verification within the same mathematical model, making it possible to deploy the two techniques in concert, by virtue of the law of diminishing returns Defines the concept of a software fault, and the related concept of relative correctness, and shows how relative correctness can be used to characterize monotonic fault removal Presents the activity of software testing as a goal oriented activity, and explores how the conduct of the test depends on the selected goal Covers all phases of the software testing lifecycle, including test data generation, test oracle design, test driver design, and test outcome analysis Software Testing: Concepts and Operations is a great resource for software quality and software engineering students because it presents them with fundamentals that help them to prepare for their ever evolving discipline. |
| data engineering testing tools: Effective Software Test Automation Kanglin Li, Mengqi Wu, 2006-02-20 If you'd like a glimpse at how the next generation is going to program, this book is a good place to start. —Gregory V. Wilson, Dr. Dobbs Journal (October 2004) Build Your Own Automated Software Testing Tool Whatever its claims, commercially available testing software is not automatic. Configuring it to test your product is almost as time-consuming and error-prone as purely manual testing. There is an alternative that makes both engineering and economic sense: building your own, truly automatic tool. Inside, you'll learn a repeatable, step-by-step approach, suitable for virtually any development environment. Code-intensive examples support the book's instruction, which includes these key topics: Conducting active software testing without capture/replay Generating a script to test all members of one class without reverse-engineering Using XML to store previously designed testing cases Automatically generating testing data Combining Reflection and CodeDom to write test scripts focused on high-risk areas Generating test scripts from external data sources Using real and complete objects for integration testing Modifying your tool to test third-party software components Testing your testing tool Effective Software Test Automation goes well beyond the building of your own testing tool: it also provides expert guidance on deploying it in ways that let you reap the greatest benefits: earlier detection of coding errors, a smoother, swifter development process, and final software that is as bug-free as possible. Written for programmers, testers, designers, and managers, it will improve the way your team works and the quality of its products. |
| data engineering testing tools: Software Engineering for Automotive Systems P. Sivakumar, B. Vinoth Kumar, R. S. Sandhya Devi, 2022-08-08 Software Engineering for Automotive Systems: Principles and Applications discusses developments in the field of software engineering for automotive systems. This reference text presents detailed discussion of key concepts including timing analysis and reliability, validation and verification of automotive systems, AUTOSAR architecture for electric vehicles, automotive grade Linux for connected cars, open-source architecture in the automotive software industry, and communication protocols in the automotive software development process. Aimed at senior undergraduate and graduate students in the fields of electrical engineering, electronics and communication engineering, and automobile engineering, this text: Provides the fundamentals of automotive software architectures. Discusses validation and verification of automotive systems. Covers communication protocols in the automotive software development process. Discusses AUTOSAR architecture for electric vehicles. Examines open-source architecture in the automotive software industry. |
| data engineering testing tools: Proceedings of International Conference on Computational Intelligence and Data Engineering Nabendu Chaki, Nagaraju Devarakonda, Agostino Cortesi, Hari Seetha, 2022-02-28 This book covers various topics, including collective intelligence, intelligent transportation systems, fuzzy systems, Bayesian network, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, and speech processing. This book is a collection of high-quality research work on cutting-edge technologies and the most-happening areas of computational intelligence and data engineering. It includes selected papers from the International Conference on Computational Intelligence and Data Engineering (ICCIDE 2021). |
| data engineering testing tools: Computational Methods and Data Engineering Vijayan K. Asari, Vijendra Singh, Rajkumar Rajasekaran, R. B. Patel, 2022-09-08 The book features original papers from International Conference on Computational Methods and Data Engineering (ICCMDE 2021), organized by School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, during November 25–26, 2021. The book covers innovative and cutting-edge work of researchers, developers, and practitioners from academia and industry working in the area of advanced computing. |
| data engineering testing tools: Manage Software Testing Peter Farrell-Vinay, 2008-03-07 Whether you are inheriting a test team or starting one up, Manage Software Testing is a must-have resource that covers all aspects of test management. It guides you through the business and organizational issues that you are confronted with on a daily basis, explaining what you need to focus on strategically, tactically, and operationally. Using a |
| data engineering testing tools: Effective Software Testing Elfriede Dustin, 2002 With the advent of agile methodologies, testing is becoming the responsibility of more and more team members. In this new book, noted testing expert Dustin imparts the best of her collected wisdom. She presents 50 specific tips for a better testing program. These 50 tips are divided into ten sections, and presented so as to mirror the chronology of a software project. |
| data engineering testing tools: 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 testing tools: Software Quality Approaches: Testing, Verification, and Validation Michael Haug, Eric W. Olsen, Luisa Consolini, 2012-12-06 C. Amting Directorate General Information Society, European Commission, Brussels th Under the 4 Framework of European Research, the European Systems and Soft ware Initiative (ESSI) was part ofthe ESPRIT Programme. This initiative funded more than 470 projects in the area ofsoftware and system process improvements. The majority of these projects were process improvement experiments carrying out and taking up new development processes, methods and technology within the software development process ofa company. In addition, nodes (centres ofexper tise), European networks (organisations managing local activities), training and dissemination actions complemented the process improvementexperiments. ESSI aimed at improving the software development capabilities of European enterprises. It focused on best practice and helped European companies to develop world class skills and associated technologies to build the increasingly complex and varied systems needed to compete in the marketplace. The dissemination activities were designed to build a forum, at European level, to exchange information and knowledge gained within process improvement ex periments. Their major objective was to spread the message and the results of experiments to awider audience, through a variety ofdifferent channels. The European Experience Exchange ~UR~X) project has been one ofthese dis semination activities within the European Systems and Software Initiative.~UR~)( has collected the results of practitioner reports from numerous workshops in Europe and presents, in this series of books, the results of Best Practice achieve ments in European Companies over the last few years. |
| data engineering testing tools: Model and Data Engineering Christian Attiogbé, Sadok Ben Yahia, 2021-06-14 This book constitutes the refereed proceedings of the 10th International Conference on Model and Data Engineering, MEDI 2021, held in Tallinn, Estonia, in June 2021. The 16 full papers and 8 short papers presented in this book were carefully reviewed and selected from 47 submissions. Additionally, the volume includes 3 abstracts of invited talks. 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. Due to the Corona pandemic the conference was held virtually. |
| data engineering testing tools: Software Engineering for Data Scientists Catherine Nelson, 2024-04-16 Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more |
| data engineering testing tools: Mobile Software Testing Narayanan Palani, 2014-07-01 Mobile Software Testing, the second book written by author Narayanan Palani and the first ever book on Mobile Application based software testing as well, has already turned out a best reviewed in the I.T industry. Narayanan Palani is keen in sharing the technical knowledge for those starting out a career in Software Testing or even for those with few years of testing experience. He is endorsed by Tech City UK as an exceptional talent/world leader in digital technology. His aim is to reduce the unemployment of developed countries like United Kingdom and developing countries like India by training the graduate students and jobseekers through his technical books. This book is the culmination of 5 years of research and effort in this field. It gives a pragmatic view of using Mobile Application Technology Testing Techniques in various situations. And is recommended for those aspiring to be experts or advanced users of test automation and performance tools like Experitest, Perfecto Mobile, uTest, Neotys, Soasta, Robotium, Ranorex and Eggplant. From the Reviewers Mobile testing will capture the market space in the future and this book is very informative for testers who want to reserve the space in the future market-Sunil Kiran Balijepalli, Team Lead at Cornerstone on demand. “Mobile testing is increasingly complex on day by day due to the range of platforms, devices and innovations. Narayanan has articulated the complex mobile testing approach in simple terms with good references. I am sure, this book will enable QA community to pick up the latest developments in mobile testing arena and the tools available to deliver secured & quality product to the end users” -Ponsailapathi V, Vice President, Polaris Software Lab Limited |
| data engineering testing tools: Data-Driven Business Intelligence Systems for Socio-Technical Organizations Keikhosrokiani, Pantea, 2024-04-09 The convergence of modern technology and social dynamics have shaped the very fabric of today’s organizations, making the role of Business Intelligence (BI) profoundly significant. Data-Driven Business Intelligence Systems for Socio-Technical Organizations delves into the heart of this transformative realm, offering an academic exploration of the tools, strategies, and methodologies that propel enterprises toward data-driven decision-making excellence. Socio-technical organizations, with their intricate interplay between human and technological components, require a unique approach to BI. This book embarks on a comprehensive journey, revealing how BI tools empower these entities to decipher the complexities of their data landscape. From user behavior to social interactions, technological systems to environmental factors, this work sheds light on the multifaceted sources of information that inform organizational strategies. Decision-makers within socio-technical organizations leverage BI insights to discern patterns, spot trends, and uncover correlations that influence operations and the intricate social dynamics within their entities. Research covering real-time monitoring and predictive analytics equips these organizations to respond swiftly to demands and anticipate future trends, harnessing the full potential of data. The book delves into their design, development, and architectural nuances, illuminating these concepts through case studies. This book is ideal for business executives, entrepreneurs, data analysts, marketers, government officials, educators, and researchers. |
| data engineering testing tools: Foundations of Software Testing: For VTU , 2013 |
| data engineering testing tools: An Integration Framework for Knowledge-Supported Project Management in IT Consortia Andreas Grünwald, |
| data engineering testing tools: Fundamentals of Analytics Engineering Dumky De Wilde, Fanny Kassapian, Jovan Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira, 2024-03-29 Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering Key Features Discover how analytics engineering aligns with your organization's data strategy Access insights shared by a team of seven industry experts Tackle common analytics engineering problems faced by modern businesses Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer. After conquering data ingestion and techniques for data quality and scalability, you’ll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You’ll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You’ll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance. By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.What you will learn Design and implement data pipelines from ingestion to serving data Explore best practices for data modeling and schema design Scale data processing with cloud based analytics platforms and tools Understand the principles of data quality management and data governance Streamline code base with best practices like collaborative coding, version control, reviews and standards Automate and orchestrate data pipelines Drive business adoption with effective scoping and prioritization of analytics use cases Who this book is for This book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing. |
| data engineering testing tools: Trends in Development of Accelerated Testing for Automotive and Aerospace Engineering Lev M. Klyatis, 2020-04-17 Accelerated testing (most types of laboratory testing, proving ground testing, intensive field/flight testing, any experimental research) is increasingly a key component for predicting of product's/process performance. Trends in Development Accelerated Testing for Automotive and Aerospace Engineering provides a completely updated analysis of the current status of accelerated testing, including the basic general directions of testing (methods and equipment) development, how one needs to study real world conditions for their accurate simulation and successful accelerated testing, describes in details the role of accurate simulation in the development of automotive and aerospace engineering, shows that failures are most often found in the interconnections, step-by-step instructions and examples. This is the only book presently available that considers in detail both the positive and negative trends in testing development for prediction quality, reliability, safety, durability, maintainability, supportability, profit, and decreasing life-cycle cost, recalls, complaints and other performance components of the product. The author presents new ideas and offers a unique strategic approach to obtaining solutions which were not possible using earlier. His methodology has been widely implemented, continue to be adopted throughout the world, and leads to advance society through product improvement that can reduce loss of life, injuries, financial losses, and product recalls. It also covers new ideas in development positive and cost- effective trends in testing development, especially accelerated reliability and durability testing (ART/ADT), which includes integration accurate simulation of field/flight influences, safety, human factors, and leads to successful prediction of product performance during pre-design, design, manufacturing, and usage for the product's service life. Engineers, researchers, teachers and postgraduate/advanced students who are involved in automotive and aerospace engineering will find this a useful reference on how to apply the accelerated testing method to solve practical problems in these areas. |
| data engineering testing tools: Testing Data Warehouse Applications Doug Vucevic, Mengxi Jett Zhang, 2011-08 A data warehouse is a valuable corporate asset used to envisage business strategies and make informed business decisions. The enhanced access to information that a data warehouse provides, enable an organization to make the time-critical business decisions that are required to remain competitive. Data warehousing needs a comprehensive assessment of the impact to the entire organization and development of a plan for an organized, systematic solution. As for the Quality Assurance [QA] teams, it creates an exciting new opportunity that comes once in a life time. It is nothing less than a new business paradigm, which creates a new unlimited learning opportunities required to prosper in it. As in any new paradigm, most of us are unprepared for it. That is a bad news. Good news is so is everybody else. The race is on! The most nimble of us will flourish the most. Read on, with this book you will give yourself the head start. |
| data engineering testing tools: Big Data Technologies and Applications Jason J. Jung, Pankoo Kim, Kwang Nam Choi, 2018-11-08 This book constitutes the refereed post-conference proceedings of the 8th International Conference on Big Data Technologies and Applications, BDTA 2017, held in Gwangju, South Korea, in November 2017. The 15 revised full papers were carefully reviewed and selected from 25 submissions and handle theoretical foundations and practical applications which premise the new generation of data analytics and engineering. The contributions deal with following topics: privacy and security, image processing, context awareness, s/w engineering and e-commerce, social media and health care. |
| data engineering testing tools: Software Quality. The Future of Systems- and Software Development Dietmar Winkler, Stefan Biffl, Johannes Bergsmann, 2015-12-10 This book constitutes the refereed proceedings of the scientific track of the 8th Software Quality Days Conference, SWQD 2016, held in Vienna, Austria, in January 2016. The SWQD conference offers a range of comprehensive and valuable information by presenting new ideas from the latest research papers, keynote speeches by renowned academics and industry leaders, professional lectures, exhibits, and tutorials. The five scientific full papers accepted for SWQD were each peer reviewed by three or more reviewers and selected out of 13 high-quality submissions. Further, nine short papers were also presented and are included in this book. In addition, one keynote paper by Scott Ambler and Mark Lines is also included. |
| data engineering testing tools: Computerworld , 1999-04-05 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| data engineering testing tools: The Testing Network Jean-Jacques Pierre Henry, 2008-08-17 The Testing Network presents an integrated approach to testing based on cutting-edge methodologies, processes and tools in today's IT context. It means complex network-centric applications to be tested in heterogeneous IT infrastructures and in multiple test environments (also geographically distributed). The added-value of this book is the in-depth explanation of all processes and relevant methodologies and tools to address this complexity. Main aspects of testing are explained using TD/QC - the world-leader test platform. This up-to-date know-how is based on real-life IT experiences gained in large-scale projects of companies operating worldwide. The book is abundantly illustrated to better show all technical aspects of modern testing in a national and international context. The author has a deep expertise by designing and giving testing training in large companies using the above-mentioned tools and processes. The Testing Network is a unique synthesis of core test topics applied in real-life. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnational ...
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 enable a broader scientific community to benefit from the identified …
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 minimum time delay; Understandable in a way that allows …
Belmont Forum Adopts Open Data Principles for Environmental Change Research
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, released in June, 2015. “A Place to Stand” is the …
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, prevents fraud and thereby builds trust in …
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 …
