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| data engineering vs devops: Data Engineering on Azure Vlad Riscutia, 2021-08-17 Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data |
| data engineering vs devops: The Definitive Guide to Azure Data Engineering Ron C. L'Esteve, 2021-08-24 Build efficient and scalable batch and real-time data ingestion pipelines, DevOps continuous integration and deployment pipelines, and advanced analytics solutions on the Azure Data Platform. This book teaches you to design and implement robust data engineering solutions using Data Factory, Databricks, Synapse Analytics, Snowflake, Azure SQL database, Stream Analytics, Cosmos database, and Data Lake Storage Gen2. You will learn how to engineer your use of these Azure Data Platform components for optimal performance and scalability. You will also learn to design self-service capabilities to maintain and drive the pipelines and your workloads. The approach in this book is to guide you through a hands-on, scenario-based learning process that will empower you to promote digital innovation best practices while you work through your organization’s projects, challenges, and needs. The clear examples enable you to use this book as a reference and guide for building data engineering solutions in Azure. After reading this book, you will have a far stronger skill set and confidence level in getting hands on with the Azure Data Platform. What You Will Learn Build dynamic, parameterized ELT data ingestion orchestration pipelines in Azure Data Factory Create data ingestion pipelines that integrate control tables for self-service ELT Implement a reusable logging framework that can be applied to multiple pipelines Integrate Azure Data Factory pipelines with a variety of Azure data sources and tools Transform data with Mapping Data Flows in Azure Data Factory Apply Azure DevOps continuous integration and deployment practices to your Azure Data Factory pipelines and development SQL databases Design and implement real-time streaming and advanced analytics solutions using Databricks, Stream Analytics, and Synapse Analytics Get started with a variety of Azure data services through hands-on examples Who This Book Is For Data engineers and data architects who are interested in learning architectural and engineering best practices around ELT and ETL on the Azure Data Platform, those who are creating complex Azure data engineering projects and are searching for patterns of success, and aspiring cloud and data professionals involved in data engineering, data governance, continuous integration and deployment of DevOps practices, and advanced analytics who want a full understanding of the many different tools and technologies that Azure Data Platform provides |
| data engineering vs devops: DevOps For Dummies Emily Freeman, 2019-08-20 Develop faster with DevOps DevOps embraces a culture of unifying the creation and distribution of technology in a way that allows for faster release cycles and more resource-efficient product updating. DevOps For Dummies provides a guidebook for those on the development or operations side in need of a primer on this way of working. Inside, DevOps evangelist Emily Freeman provides a roadmap for adopting the management and technology tools, as well as the culture changes, needed to dive head-first into DevOps. Identify your organization’s needs Create a DevOps framework Change your organizational structure Manage projects in the DevOps world DevOps For Dummies is essential reading for developers and operations professionals in the early stages of DevOps adoption. |
| data engineering vs devops: Google Cloud for DevOps Engineers Sandeep Madamanchi, 2021-07-02 Explore site reliability engineering practices and learn key Google Cloud Platform (GCP) services such as CSR, Cloud Build, Container Registry, GKE, and Cloud Operations to implement DevOps Key FeaturesLearn GCP services for version control, building code, creating artifacts, and deploying secured containerized applicationsExplore Cloud Operations features such as Metrics Explorer, Logs Explorer, and debug logpointsPrepare for the certification exam using practice questions and mock testsBook Description DevOps is a set of practices that help remove barriers between developers and system administrators, and is implemented by Google through site reliability engineering (SRE). With the help of this book, you'll explore the evolution of DevOps and SRE, before delving into SRE technical practices such as SLA, SLO, SLI, and error budgets that are critical to building reliable software faster and balance new feature deployment with system reliability. You'll then explore SRE cultural practices such as incident management and being on-call, and learn the building blocks to form SRE teams. The second part of the book focuses on Google Cloud services to implement DevOps via continuous integration and continuous delivery (CI/CD). You'll learn how to add source code via Cloud Source Repositories, build code to create deployment artifacts via Cloud Build, and push it to Container Registry. Moving on, you'll understand the need for container orchestration via Kubernetes, comprehend Kubernetes essentials, apply via Google Kubernetes Engine (GKE), and secure the GKE cluster. Finally, you'll explore Cloud Operations to monitor, alert, debug, trace, and profile deployed applications. By the end of this SRE book, you'll be well-versed with the key concepts necessary for gaining Professional Cloud DevOps Engineer certification with the help of mock tests. What you will learnCategorize user journeys and explore different ways to measure SLIsExplore the four golden signals for monitoring a user-facing systemUnderstand psychological safety along with other SRE cultural practicesCreate containers with build triggers and manual invocationsDelve into Kubernetes workloads and potential deployment strategiesSecure GKE clusters via private clusters, Binary Authorization, and shielded GKE nodesGet to grips with monitoring, Metrics Explorer, uptime checks, and alertingDiscover how logs are ingested via the Cloud Logging APIWho this book is for This book is for cloud system administrators and network engineers interested in resolving cloud-based operational issues. IT professionals looking to enhance their careers in administering Google Cloud services and users who want to learn about applying SRE principles and implementing DevOps in GCP will also benefit from this book. Basic knowledge of cloud computing, GCP services, and CI/CD and hands-on experience with Unix/Linux infrastructure is recommended. You'll also find this book useful if you're interested in achieving Professional Cloud DevOps Engineer certification. |
| data engineering vs devops: The DevOps Handbook Gene Kim, Jez Humble, Patrick Debois, John Willis, 2016-10-06 Increase profitability, elevate work culture, and exceed productivity goals through DevOps practices. More than ever, the effective management of technology is critical for business competitiveness. For decades, technology leaders have struggled to balance agility, reliability, and security. The consequences of failure have never been greater―whether it's the healthcare.gov debacle, cardholder data breaches, or missing the boat with Big Data in the cloud. And yet, high performers using DevOps principles, such as Google, Amazon, Facebook, Etsy, and Netflix, are routinely and reliably deploying code into production hundreds, or even thousands, of times per day. Following in the footsteps of The Phoenix Project, The DevOps Handbook shows leaders how to replicate these incredible outcomes, by showing how to integrate Product Management, Development, QA, IT Operations, and Information Security to elevate your company and win in the marketplace. |
| data engineering vs devops: 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 vs devops: Tools and Techniques for Software Development in Large Organizations: Emerging Research and Opportunities Pendyala, Vishnu, 2019-12-20 The development of software has expanded substantially in recent years. As these technologies continue to advance, well-known organizations have begun implementing these programs into the ways they conduct business. These large companies play a vital role in the economic environment, so understanding the software that they utilize is pertinent in many aspects. Researching and analyzing the tools that these corporations use will assist in the practice of software engineering and give other organizations an outline of how to successfully implement their own computational methods. Tools and Techniques for Software Development in Large Organizations: Emerging Research and Opportunities is an essential reference source that discusses advanced software methods that prominent companies have adopted to develop high quality products. This book will examine the various devices that organizations such as Google, Cisco, and Facebook have implemented into their production and development processes. Featuring research on topics such as database management, quality assurance, and machine learning, this book is ideally designed for software engineers, data scientists, developers, programmers, professors, researchers, and students seeking coverage on the advancement of software devices in today’s major corporations. |
| data engineering vs devops: 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 vs devops: Modern Software Engineering David Farley, 2021-11-16 Improve Your Creativity, Effectiveness, and Ultimately, Your Code In Modern Software Engineering, continuous delivery pioneer David Farley helps software professionals think about their work more effectively, manage it more successfully, and genuinely improve the quality of their applications, their lives, and the lives of their colleagues. Writing for programmers, managers, and technical leads at all levels of experience, Farley illuminates durable principles at the heart of effective software development. He distills the discipline into two core exercises: learning and exploration and managing complexity. For each, he defines principles that can help you improve everything from your mindset to the quality of your code, and describes approaches proven to promote success. Farley's ideas and techniques cohere into a unified, scientific, and foundational approach to solving practical software development problems within realistic economic constraints. This general, durable, and pervasive approach to software engineering can help you solve problems you haven't encountered yet, using today's technologies and tomorrow's. It offers you deeper insight into what you do every day, helping you create better software, faster, with more pleasure and personal fulfillment. Clarify what you're trying to accomplish Choose your tools based on sensible criteria Organize work and systems to facilitate continuing incremental progress Evaluate your progress toward thriving systems, not just more legacy code Gain more value from experimentation and empiricism Stay in control as systems grow more complex Achieve rigor without too much rigidity Learn from history and experience Distinguish good new software development ideas from bad ones Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data engineering vs devops: 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 vs devops: Data Engineering on Azure Vlad Riscutia, 2021-09-21 Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data |
| data engineering vs devops: Python for DevOps Noah Gift, Kennedy Behrman, Alfredo Deza, Grig Gheorghiu, 2019-12-12 Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform. Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to get stuff done in Python? This is your guide. Python foundations, including a brief introduction to the language How to automate text, write command-line tools, and automate the filesystem Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing Cloud computing, infrastructure as code, Kubernetes, and serverless Machine learning operations and data engineering from a DevOps perspective Building, deploying, and operationalizing a machine learning project |
| data engineering vs devops: Mastering Data Engineering and Analytics with Databricks Manoj Kumar, 2024-09-30 TAGLINE Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges KEY FEATURES ● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow. ● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action. ● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines. ● Offers proven strategies to optimize workflows and avoid common pitfalls. DESCRIPTION In today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. WHAT WILL YOU LEARN ● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases. ● Optimize query performance and efficiently manage cloud resources for cost-effective data processing. ● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation. ● Build and deploy real-time data processing solutions for timely and actionable insights. ● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. WHO IS THIS BOOK FOR? This book is designed for data engineering students, aspiring data engineers, experienced data professionals, cloud data architects, data scientists and analysts looking to expand their skill sets, as well as IT managers seeking to master data engineering and analytics with Databricks. A basic understanding of data engineering concepts, familiarity with data analytics, and some experience with cloud computing or programming languages such as Python or SQL will help readers fully benefit from the book’s content. TABLE OF CONTENTS SECTION 1 1. Introducing Data Engineering with Databricks 2. Setting Up a Databricks Environment for Data Engineering 3. Working with Databricks Utilities and Clusters SECTION 2 4. Extracting and Loading Data Using Databricks 5. Transforming Data with Databricks 6. Handling Streaming Data with Databricks 7. Creating Delta Live Tables 8. Data Partitioning and Shuffling 9. Performance Tuning and Best Practices 10. Workflow Management 11. Databricks SQL Warehouse 12. Data Storage and Unity Catalog 13. Monitoring Databricks Clusters and Jobs 14. Production Deployment Strategies 15. Maintaining Data Pipelines in Production 16. Managing Data Security and Governance 17. Real-World Data Engineering Use Cases with Databricks 18. AI and ML Essentials 19. Integrating Databricks with External Tools Index |
| data engineering vs devops: 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 vs devops: Summary of Joe Reis & Matt Housley's Fundamentals of Data Engineering Milkyway Media, 2024-03-21 Buy now to get the main key ideas from Joe Reis & Matt Housley's Fundamentals of Data Engineering 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. Their guide aims to help aspiring and current data engineers navigate the evolving landscape of the field, offering insights into best practices and approaches for managing data from its source to its final use. |
| data engineering vs devops: Proceedings of the 2nd International Conference on Data Engineering and Communication Technology Anand J. Kulkarni, Suresh Chandra Satapathy, Tai Kang, Ali Husseinzadeh Kashan, 2018-10-03 This book features research work presented at the 2nd International Conference on Data Engineering and Communication Technology (ICDECT) held on December 15–16, 2017 at Symbiosis International University, Pune, Maharashtra, India. It discusses advanced, multi-disciplinary research into smart computing, information systems and electronic systems, focusing on innovation paradigms in system knowledge, intelligence and sustainability that can be applied to provide feasible solutions to varied problems in society, the environment and industry. It also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in a variety of disciplines of computer science and electronics engineering. |
| data engineering vs devops: Docker Certified Associate (DCA): Exam Guide Francisco Javier Ramirez Urea, 2020-09-28 Pass the DCA exam and enhance your DevOps skills by achieving faster deployments, reduced downtime, and continuous integration and continuous delivery Key FeaturesStrengthen your knowledge of container fundamentals and exploit Docker networking, storage, and image managementLeverage Docker Swarm to deploy and scale applications in a clusterBuild your Docker skills with the help of sample questions and mock testsBook Description Developers have changed their deployment artifacts from application binaries to container images, and they now need to build container-based applications as containers are part of their new development workflow. This Docker book is designed to help you learn about the management and administrative tasks of the Containers as a Service (CaaS) platform. The book starts by getting you up and running with the key concepts of containers and microservices. You'll then cover different orchestration strategies and environments, along with exploring the Docker Enterprise platform. As you advance, the book will show you how to deploy secure, production-ready, container-based applications in Docker Enterprise environments. Later, you'll delve into each Docker Enterprise component and learn all about CaaS management. Throughout the book, you'll encounter important exam-specific topics, along with sample questions and detailed answers that will help you prepare effectively for the exam. By the end of this Docker containers book, you'll have learned how to efficiently deploy and manage container-based environments in production, and you will have the skills and knowledge you need to pass the DCA exam. What you will learnUnderstand the key concepts of containerization and its advantagesDiscover how to build secure images and run customized Docker containersExplore orchestration with Docker Swarm and KubernetesBecome well versed with networking and application publishing methodsUnderstand the Docker container runtime environment and customizationsDeploy services on Docker Enterprise with Universal Control PlaneGet to grips with effectively managing images using Docker Trusted RegistryWho this book is for If you are a system administrator, a developer, a DevOps engineer, or any professional interested in enhancing your career portfolio by gaining Docker certification, this book is for you. In order to understand container networking and the use of load balancers and proxies to provide a full-featured Containers-as-a-Service environment, Linux and Windows user knowledge with some networking skills will be necessary. |
| data engineering vs devops: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. |
| data engineering vs devops: Foundations of data engineering: concepts, principles and practices Dr. RVS Praveen, 2024-09-23 Foundations of Data Engineering: Concepts, Principles and Practices offers a comprehensive introduction to the processes and systems that make data-driven decision-making possible. In today’s data-centric world, companies rely heavily on vast amounts of data to inform strategies, optimize operations, and innovate. This book explains the essential building blocks of data engineering, covering topics like data pipelines, ETL (Extract, Transform, Load) processes, data storage, and distributed computing. The text is structured to guide readers through the end-to-end lifecycle of data, from ingestion to transformation and analysis. It emphasizes best practices in designing robust, scalable data pipelines that ensure high-quality, reliable data is delivered to downstream analytics and machine learning systems. Topics such as batch and real-time data processing are covered, with in-depth discussions on tools and technologies like Apache Kafka, Hadoop, Spark, and cloud-based solutions like Google Cloud and AWS. For those new to the field or looking to expand their knowledge, this book also addresses the importance of data governance, ensuring data integrity, security, and compliance. Readers will gain insights into the challenges of big data and how modern engineering approaches can handle growing data volumes efficiently. With case studies and practical examples throughout, Foundations of Data Engineering: Concepts, Principles and Practices is a valuable resource for aspiring data engineers, analysts, and anyone involved in the data ecosystem looking to build scalable, reliable data solutions. |
| data engineering vs devops: The Elements of Big Data Value Edward Curry, Andreas Metzger, Sonja Zillner, Jean-Christophe Pazzaglia, Ana García Robles, 2021-08-01 This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation. |
| data engineering vs devops: Advances in Data Science and Analytics M. Niranjanamurthy, Hemant Kumar Gianey, Amir H. Gandomi, 2022-11-01 ADVANCES in DATA SCIENCE and ANALYTICS Presenting the concepts and advances of data science and analytics, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across multiple disciplines and industries, for both the engineer and the student, focusing on machining learning, big data, business intelligence, and analytics. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning, and big data. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. For the purposes of this volume, data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. Although data mining and other related areas have been around for a few decades, data science and analytics are still quickly evolving, and the processes and technologies change, almost on a day-to-day basis. This volume provides an overview of some of the most important advances in these areas today, including practical coverage of the daily applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library. |
| data engineering vs devops: Data Observability for Data Engineering Michele Pinto, Sammy El Khammal, 2023-12-29 Discover actionable steps to maintain healthy data pipelines to promote data observability within your teams with this essential guide to elevating data engineering practices Key Features Learn how to monitor your data pipelines in a scalable way Apply real-life use cases and projects to gain hands-on experience in implementing data observability Instil trust in your pipelines among data producers and consumers alike Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the age of information, strategic management of data is critical to organizational success. The constant challenge lies in maintaining data accuracy and preventing data pipelines from breaking. Data Observability for Data Engineering is your definitive guide to implementing data observability successfully in your organization. This book unveils the power of data observability, a fusion of techniques and methods that allow you to monitor and validate the health of your data. You’ll see how it builds on data quality monitoring and understand its significance from the data engineering perspective. Once you're familiar with the techniques and elements of data observability, you'll get hands-on with a practical Python project to reinforce what you've learned. Toward the end of the book, you’ll apply your expertise to explore diverse use cases and experiment with projects to seamlessly implement data observability in your organization. Equipped with the mastery of data observability intricacies, you’ll be able to make your organization future-ready and resilient and never worry about the quality of your data pipelines again.What you will learn Implement a data observability approach to enhance the quality of data pipelines Collect and analyze key metrics through coding examples Apply monkey patching in a Python module Manage the costs and risks associated with your data pipeline Understand the main techniques for collecting observability metrics Implement monitoring techniques for analytics pipelines in production Build and maintain a statistics engine continuously Who this book is for This book is for data engineers, data architects, data analysts, and data scientists who have encountered issues with broken data pipelines or dashboards. Organizations seeking to adopt data observability practices and managers responsible for data quality and processes will find this book especially useful to increase the confidence of data consumers and raise awareness among producers regarding their data pipelines. |
| data engineering vs devops: Data Engineering with Databricks Cookbook Pulkit Chadha, 2024-05-31 Work through 70 recipes for implementing reliable data pipelines with Apache Spark, optimally store and process structured and unstructured data in Delta Lake, and use Databricks to orchestrate and govern your data Key Features Learn data ingestion, data transformation, and data management techniques using Apache Spark and Delta Lake Gain practical guidance on using Delta Lake tables and orchestrating data pipelines Implement reliable DataOps and DevOps practices, and enforce data governance policies on Databricks Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a Senior Solutions Architect at Databricks, Data Engineering with Databricks Cookbook will show you how to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, starting with comprehensive introduction to data ingestion and loading with Apache Spark. What makes this book unique is its recipe-based approach, which will help you put your knowledge to use straight away and tackle common problems. You’ll be introduced to various data manipulation and data transformation solutions that can be applied to data, find out how to manage and optimize Delta tables, and get to grips with ingesting and processing streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Advanced recipes later in the book will teach you how to use Databricks to implement DataOps and DevOps practices, as well as how to orchestrate and schedule data pipelines using Databricks Workflows. You’ll also go through the full process of setup and configuration of the Unity Catalog for data governance. By the end of this book, you’ll be well-versed in building reliable and scalable data pipelines using modern data engineering technologies.What you will learn Perform data loading, ingestion, and processing with Apache Spark Discover data transformation techniques and custom user-defined functions (UDFs) in Apache Spark Manage and optimize Delta tables with Apache Spark and Delta Lake APIs Use Spark Structured Streaming for real-time data processing Optimize Apache Spark application and Delta table query performance Implement DataOps and DevOps practices on Databricks Orchestrate data pipelines with Delta Live Tables and Databricks Workflows Implement data governance policies with Unity Catalog Who this book is for This book is for data engineers, data scientists, and data practitioners who want to learn how to build efficient and scalable data pipelines using Apache Spark, Delta Lake, and Databricks. To get the most out of this book, you should have basic knowledge of data architecture, SQL, and Python programming. |
| data engineering vs devops: Accelerate Nicole Forsgren, PhD, Jez Humble, Gene Kim, 2018-03-27 Winner of the Shingo Publication Award Accelerate your organization to win in the marketplace. How can we apply technology to drive business value? For years, we've been told that the performance of software delivery teams doesn't matter―that it can't provide a competitive advantage to our companies. Through four years of groundbreaking research to include data collected from the State of DevOps reports conducted with Puppet, Dr. Nicole Forsgren, Jez Humble, and Gene Kim set out to find a way to measure software delivery performance―and what drives it―using rigorous statistical methods. This book presents both the findings and the science behind that research, making the information accessible for readers to apply in their own organizations. Readers will discover how to measure the performance of their teams, and what capabilities they should invest in to drive higher performance. This book is ideal for management at every level. |
| data engineering vs devops: Machine Learning on Kubernetes Faisal Masood, Ross Brigoli, 2022-06-24 Build a Kubernetes-based self-serving, agile data science and machine learning ecosystem for your organization using reliable and secure open source technologies Key Features Build a complete machine learning platform on Kubernetes Improve the agility and velocity of your team by adopting the self-service capabilities of the platform Reduce time-to-market by automating data pipelines and model training and deployment Book Description MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization. You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow. By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built. What you will learn Understand the different stages of a machine learning project Use open source software to build a machine learning platform on Kubernetes Implement a complete ML project using the machine learning platform presented in this book Improve on your organization's collaborative journey toward machine learning Discover how to use the platform as a data engineer, ML engineer, or data scientist Find out how to apply machine learning to solve real business problems Who this book is for This book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way. |
| data engineering vs devops: Engineering MLOps Emmanuel Raj, 2021-04-19 Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook Description Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization. What you will learnFormulate data governance strategies and pipelines for ML training and deploymentGet to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelinesDesign a robust and scalable microservice and API for test and production environmentsCurate your custom CD processes for related use cases and organizationsMonitor ML models, including monitoring data drift, model drift, and application performanceBuild and maintain automated ML systemsWho this book is for This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book. |
| data engineering vs devops: Mathematics and Computer Science, Volume 1 Sharmistha Ghosh, M. Niranjanamurthy, Krishanu Deyasi, Biswadip Basu Mallik, Santanu Das, 2023-07-19 MATHEMATICS AND COMPUTER SCIENCE This first volume in a new multi-volume set gives readers the basic concepts and applications for diverse ideas and innovations in the field of computing together with its growing interactions with mathematics. This new edited volume from Wiley-Scrivener is the first of its kind to present scientific and technological innovations by leading academicians, eminent researchers, and experts around the world in the areas of mathematical sciences and computing. The chapters focus on recent advances in computer science, and mathematics, and where the two intersect to create value for end users through practical applications of the theory. The chapters herein cover scientific advancements across a diversified spectrum that includes differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning and artificial intelligence, big data analytics, Internet of Things, cryptography, fuzzy automata, statistics, and many more. Readers of this book will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. Whether for the engineer, scientist, student, academic, or other industry professional, this is a must-have for any library. |
| data engineering vs devops: Mathematics and Computer Science, Volume 2 Sharmistha Ghosh, M. Niranjanamurthy, Krishanu Deyasi, Biswadip Basu Mallik, Santanu Das, 2023-08-15 MATHEMATICS AND COMPUTER SCIENCE This second volume in a new multi-volume set builds on the basic concepts and fundamentals laid out in the previous volume, presenting the reader with more advanced and cutting-edge topics being developed in this exciting field. This second volume in a new series from Wiley-Scrivener is the first of its kind to present scientific and technological innovations by leading academicians, eminent researchers, and experts around the world in the areas of mathematical sciences and computing. Building on what was presented in volume one, the chapters focus on more advanced topics in computer science, mathematics, and where the two intersect to create value for end users through practical applications. The chapters herein cover scientific advancements across a diversified spectrum that includes differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning and artificial intelligence, big data analytics, Internet of Things, cryptography, fuzzy automata, statistics, and many more. Readers of this book will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. Whether for the engineer, scientist, student, academic, or other industry professional, this is a must-have for any library. |
| data engineering vs devops: MCA Microsoft Certified Associate Azure Data Engineer Study Guide Benjamin Perkins, 2023-08-02 Prepare for the Azure Data Engineering certification—and an exciting new career in analytics—with this must-have study aide In the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203, accomplished data engineer and tech educator Benjamin Perkins delivers a hands-on, practical guide to preparing for the challenging Azure Data Engineer certification and for a new career in an exciting and growing field of tech. In the book, you’ll explore all the objectives covered on the DP-203 exam while learning the job roles and responsibilities of a newly minted Azure data engineer. From integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions, you’ll get up to speed quickly and efficiently with Sybex’s easy-to-use study aids and tools. This Study Guide also offers: Career-ready advice for anyone hoping to ace their first data engineering job interview and excel in their first day in the field Indispensable tips and tricks to familiarize yourself with the DP-203 exam structure and help reduce test anxiety Complimentary access to Sybex’s expansive online study tools, accessible across multiple devices, and offering access to hundreds of bonus practice questions, electronic flashcards, and a searchable, digital glossary of key terms A one-of-a-kind study aid designed to help you get straight to the crucial material you need to succeed on the exam and on the job, the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203 belongs on the bookshelves of anyone hoping to increase their data analytics skills, advance their data engineering career with an in-demand certification, or hoping to make a career change into a popular new area of tech. |
| data engineering vs devops: AI for You Shalini Kapoor, Sameep Mehta, 2022-12-05 Artificial Intelligence is all around us. It is set to transform the way we run businesses. Yet people fear it and businesses struggle to derive maximum value from it. Learning from the best practices of industry leaders, AI For You brings together frameworks and tools for infusing AI in business processes. The book demystifies AI, simplifies the complexities around AI technologies and describes how to take AI from lab to field while satisfying the concerns of different stakeholders. A must-read for builders, consumers, sponsors and sellers of AI, AI For You lays down the building blocks for the AI revolution while attempting to close the gap between the promise of AI and its actual impact. |
| data engineering vs devops: Data Engineering and Data Science Kukatlapalli Pradeep Kumar, Aynur Unal, Vinay Jha Pillai, Hari Murthy, M. Niranjanamurthy, 2023-08-29 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 vs devops: 97 Things Every Data Engineer Should Know Tobias Macey, 2021-06-11 Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail |
| data engineering vs devops: 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 vs devops: Azure Data Engineer Associate Certification Guide Newton Alex, 2022-02-28 Become well-versed with data engineering concepts and exam objectives to achieve Azure Data Engineer Associate certification Key Features Understand and apply data engineering concepts to real-world problems and prepare for the DP-203 certification exam Explore the various Azure services for building end-to-end data solutions Gain a solid understanding of building secure and sustainable data solutions using Azure services Book DescriptionAzure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other. Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you’ll work on sample questions and answers to familiarize yourself with the pattern of the exam. By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.What you will learn Gain intermediate-level knowledge of Azure the data infrastructure Design and implement data lake solutions with batch and stream pipelines Identify the partition strategies available in Azure storage technologies Implement different table geometries in Azure Synapse Analytics Use the transformations available in T-SQL, Spark, and Azure Data Factory Use Azure Databricks or Synapse Spark to process data using Notebooks Design security using RBAC, ACL, encryption, data masking, and more Monitor and optimize data pipelines with debugging tips Who this book is for This book is for data engineers who want to take the DP-203: Azure Data Engineer Associate exam and are looking to gain in-depth knowledge of the Azure cloud stack. The book will also help engineers and product managers who are new to Azure or interviewing with companies working on Azure technologies, to get hands-on experience of Azure data technologies. A basic understanding of cloud technologies, extract, transform, and load (ETL), and databases will help you get the most out of this book. |
| data engineering vs devops: Security Issues and Privacy Concerns in Industry 4.0 Applications Shibin David, R. S. Anand, V. Jeyakrishnan, M. Niranjanamurthy, 2021-08-03 SECURITY ISSUES AND PRIVACY CONCERNS IN INDUSTRY 4.0 APPLICATIONS Written and edited by a team of international experts, this is the most comprehensive and up-to-date coverage of the security and privacy issues surrounding Industry 4.0 applications, a must-have for any library. The scope of Security Issues and Privacy Concerns in Industry 4.0 Applications is to envision the need for security in Industry 4.0 applications and the research opportunities for the future. This book discusses the security issues in Industry 4.0 applications for research development. It will also enable the reader to develop solutions for the security threats and attacks that prevail in the industry. The chapters will be framed on par with advancements in the industry in the area of Industry 4.0 with its applications in additive manufacturing, cloud computing, IoT (Internet of Things), and many others. This book helps a researcher and an industrial specialist to reflect on the latest trends and the need for technological change in Industry 4.0. Smart water management using IoT, cloud security issues with network forensics, regional language recognition for industry 4.0, IoT-based health care management systems, artificial intelligence for fake profile detection, and packet drop detection in agriculture-based IoT are covered in this outstanding new volume. Leading innovations such as smart drone for railway track cleaning, everyday life-supporting blockchain and big data, effective prediction using machine learning, classification of dog breed based on CNN, load balancing using the SPE approach and cyber culture impact on media consumers are also addressed. Whether a reference for the veteran engineer or an introduction to the technologies covered in the book for the student, this is a must-have for any library. |
| data engineering vs devops: Database Reliability Engineering Laine Campbell, Charity Majors, 2017-10-26 The infrastructure-as-code revolution in IT is also affecting database administration. With this practical book, developers, system administrators, and junior to mid-level DBAs will learn how the modern practice of site reliability engineering applies to the craft of database architecture and operations. Authors Laine Campbell and Charity Majors provide a framework for professionals looking to join the ranks of today’s database reliability engineers (DBRE). You’ll begin by exploring core operational concepts that DBREs need to master. Then you’ll examine a wide range of database persistence options, including how to implement key technologies to provide resilient, scalable, and performant data storage and retrieval. With a firm foundation in database reliability engineering, you’ll be ready to dive into the architecture and operations of any modern database. This book covers: Service-level requirements and risk management Building and evolving an architecture for operational visibility Infrastructure engineering and infrastructure management How to facilitate the release management process Data storage, indexing, and replication Identifying datastore characteristics and best use cases Datastore architectural components and data-driven architectures |
| data engineering vs devops: Azure Modern Data Architecture Anouar BEN ZAHRA, Key Features Discover the key drivers of successful Azure architecture Practical guidance Focus on scalability and performance Expert authorship Book Description This book presents a guide to design and implement scalable, secure, and efficient data solutions in the Azure cloud environment. It provides Data Architects, developers, and IT professionals who are responsible for designing and implementing data solutions in the Azure cloud environment with the knowledge and tools needed to design and implement data solutions using the latest Azure data services. It covers a wide range of topics, including data storage, data processing, data analysis, and data integration. In this book, you will learn how to select the appropriate Azure data services, design a data processing pipeline, implement real-time data processing, and implement advanced analytics using Azure Databricks and Azure Synapse Analytics. You will also learn how to implement data security and compliance, including data encryption, access control, and auditing. Whether you are building a new data architecture from scratch or migrating an existing on premises solution to Azure, the Azure Data Architecture Guidelines are an essential resource for any organization looking to harness the power of data in the cloud. With these guidelines, you will gain a deep understanding of the principles and best practices of Azure data architecture and be equipped to build data solutions that are highly scalable, secure, and cost effective. What You Need to Use this Book? To use this book, it is recommended that readers have a basic understanding of data architecture concepts and data management principles. Some familiarity with cloud computing and Azure services is also helpful. The book is designed for data architects, data engineers, data analysts, and anyone involved in designing, implementing, and managing data solutions on the Azure cloud platform. It is also suitable for students and professionals who want to learn about Azure data architecture and its best practices. |
| data engineering vs devops: Data Engineering with AWS Cookbook Trâm Ngọc Phạm, Gonzalo Herreros González, Viquar Khan, Huda Nofal, 2024-11-29 Master AWS data engineering services and techniques for orchestrating pipelines, building layers, and managing migrations Key Features Get up to speed with the different AWS technologies for data engineering Learn the different aspects and considerations of building data lakes, such as security, storage, and operations Get hands on with key AWS services such as Glue, EMR, Redshift, QuickSight, and Athena for practical learning Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionPerforming data engineering with Amazon Web Services (AWS) combines AWS's scalable infrastructure with robust data processing tools, enabling efficient data pipelines and analytics workflows. This comprehensive guide to AWS data engineering will teach you all you need to know about data lake management, pipeline orchestration, and serving layer construction. Through clear explanations and hands-on exercises, you’ll master essential AWS services such as Glue, EMR, Redshift, QuickSight, and Athena. Additionally, you’ll explore various data platform topics such as data governance, data quality, DevOps, CI/CD, planning and performing data migration, and creating Infrastructure as Code. As you progress, you will gain insights into how to enrich your platform and use various AWS cloud services such as AWS EventBridge, AWS DataZone, and AWS SCT and DMS to solve data platform challenges. Each recipe in this book is tailored to a daily challenge that a data engineer team faces while building a cloud platform. By the end of this book, you will be well-versed in AWS data engineering and have gained proficiency in key AWS services and data processing techniques. You will develop the necessary skills to tackle large-scale data challenges with confidence.What you will learn Define your centralized data lake solution, and secure and operate it at scale Identify the most suitable AWS solution for your specific needs Build data pipelines using multiple ETL technologies Discover how to handle data orchestration and governance Explore how to build a high-performing data serving layer Delve into DevOps and data quality best practices Migrate your data from on-premises to AWS Who this book is for If you're involved in designing, building, or overseeing data solutions on AWS, this book provides proven strategies for addressing challenges in large-scale data environments. Data engineers as well as big data professionals looking to enhance their understanding of AWS features for optimizing their workflow, even if they're new to the platform, will find value. Basic familiarity with AWS security (users and roles) and command shell is recommended. |
| data engineering vs devops: Software Development Phil Gilberts, This book consists of 4 titles, which are these: 1 - Data Engineering: Welcome to the world of data engineering, where the raw material of the digital age—data—is transformed into actionable insights that drive decisions, innovations, and advancements across industries. This book is your gateway into understanding and mastering the essential principles, practices, and technologies that underpin the field of data engineering. 2 - Information Technology: Information Technology (IT) refers to the use of computers, software, and networks to manage, process, store, and communicate information. It encompasses a broad range of activities and applications, including hardware and software development, network design and management, data storage and analysis, and cybersecurity. 3 - Software Engineering: Software Engineering encompasses a methodical approach to developing and maintaining software systems. It involves several key phases, each crucial to ensuring the success of the project. During the Requirements Analysis phase, software engineers collaborate with stakeholders to understand and document the system's needs and constraints. This ensures a clear understanding of what the software should accomplish. 4 - Wordpress: WordPress is a widely-used content management system (CMS) that has been empowering millions of websites since its launch in 2003. Initially created as a blogging platform, WordPress has grown into a comprehensive tool suitable for a variety of web projects, ranging from personal blogs and small business websites to large-scale e-commerce platforms and corporate sites. |
| data engineering vs devops: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
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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 barriers …
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 to …
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 collected, …
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 …
