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| data engineering on google cloud platform: Data Engineering with Google Cloud Platform Adi Wijaya, 2022-03-31 Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book. |
| data engineering on google cloud platform: Official Google Cloud Certified Professional Data Engineer Study Guide Dan Sullivan, 2020-06-10 The proven Study Guide that prepares you for this new Google Cloud exam The Google Cloud Certified Professional Data Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data Engineer Study Guide is your ace in the hole for deploying and managing analytics and machine learning applications. Build and operationalize storage systems, pipelines, and compute infrastructure Understand machine learning models and learn how to select pre-built models Monitor and troubleshoot machine learning models Design analytics and machine learning applications that are secure, scalable, and highly available. This exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform. |
| data engineering on google cloud platform: Data Engineering with Google Cloud Platform Adi Wijaya, 2024-04-30 Become a successful data engineer by building and deploying your own data pipelines on Google Cloud, including making key architectural decisions Key Features Get up to speed with data governance on Google Cloud Learn how to use various Google Cloud products like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream Boost your confidence by getting Google Cloud data engineering certification guidance from real exam experiences Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe second edition of Data Engineering with Google Cloud builds upon the success of the first edition by offering enhanced clarity and depth to data professionals navigating the intricate landscape of data engineering. Beyond its foundational lessons, this new edition delves into the essential realm of data governance within Google Cloud, providing you with invaluable insights into managing and optimizing data resources effectively. Written by a Data Strategic Cloud Engineer at Google, this book helps you stay ahead of the curve by guiding you through the latest technological advancements in the Google Cloud ecosystem. You’ll cover essential aspects, from exploring Cloud Composer 2 to the evolution of Airflow 2.5. Additionally, you’ll explore how to work with cutting-edge tools like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream to perform data governance on datasets. By the end of this book, you'll be equipped to navigate the ever-evolving world of data engineering on Google Cloud, from foundational principles to cutting-edge practices.What you will learn Load data into BigQuery and materialize its output Focus on data pipeline orchestration using Cloud Composer Formulate Airflow jobs to orchestrate and automate a data warehouse Establish a Hadoop data lake, generate ephemeral clusters, and execute jobs on the Dataproc cluster Harness Pub/Sub for messaging and ingestion for event-driven systems Apply Dataflow to conduct ETL on streaming data Implement data governance services on Google Cloud Who this book is for Data analysts, IT practitioners, software engineers, or any data enthusiasts looking to have a successful data engineering career will find this book invaluable. Additionally, experienced data professionals who want to start using Google Cloud to build data platforms will get clear insights on how to navigate the path. Whether you're a beginner who wants to explore the fundamentals or a seasoned professional seeking to learn the latest data engineering concepts, this book is for you. |
| data engineering on google cloud platform: Google Cloud Platform for Data Engineering Alasdair Gilchrist, Google Cloud Platform for Data Engineering is designed to take the beginner through a journey to become a competent and certified GCP data engineer. The book, therefore, is split into three parts; the first part covers fundamental concepts of data engineering and data analysis from a platform and technology-neutral perspective. Reading part 1 will bring a beginner up to speed with the generic concepts, terms and technologies we use in data engineering. The second part, which is a high-level but comprehensive introduction to all the concepts, components, tools and services available to us within the Google Cloud Platform. Completing this section will provide the beginner to GCP and data engineering with a solid foundation on the architecture and capabilities of the GCP. Part 3, however, is where we delve into the moderate to advanced techniques that data engineers need to know and be able to carry out. By this time the raw beginner you started the journey at the beginning of part 1 will be a knowledgable albeit inexperienced data engineer. However, by the conclusion of part 3, they will have gained the advanced knowledge of data engineering techniques and practices on the GCP to pass not only the certification exam but also most interviews and practical tests with confidence. In short part 3, will provide the prospective data engineer with detailed knowledge on setting up and configuring DataProc - GCPs version of the Spark/Hadoop ecosystem for big data. They will also learn how to build and test streaming and batch data pipelines using pub/sub/ dataFlow and BigQuery. Furthermore, they will learn how to integrate all the ML and AI Platform components and APIs. They will be accomplished in connecting data analysis and visualisation tools such as Datalab, DataStudio and AI notebooks amongst others. They will also by now know how to build and train a TensorFlow DNN using APIs and Keras and optimise it to run large public data sets. Also, they will know how to provision and use Kubeflow and Kube Pipelines within Google Kubernetes engines to run container workloads as well as how to take advantage of serverless technologies such as Cloud Run and Cloud Functions to build transparent and seamless data processing platforms. The best part of the book though is its compartmental design which means that anyone from a beginner to an intermediate can join the book at whatever point they feel comfortable. |
| data engineering on google cloud platform: Google Cloud Professional Data Engineer , 2024-10-26 Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| data engineering on google cloud platform: Google Cloud Professional Data Engineer Exam Practice Questions and Dumps Zoom Books, A Professional Data Engineer authorize data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to blueprint, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuous train pre-existing machine learning models. Here we’ve brought best Exam practice questions for Google Cloud so that you can prepare well for Professional Data Engineer exam. Unlike other online simulation practice tests, you get an eBook version that is easy to read & remember these questions. You can simply rely on these questions for successfully certifying this exam. |
| data engineering on google cloud platform: Data Analytics with Google Cloud Platform Murari Ramuka, 2019-12-16 Step-by-step guide to different data movement and processing techniques, using Google Cloud Platform Services DESCRIPTION Modern businesses are awash with data, making data-driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with enough knowledge of Cloud Computing in conjunction with Google Cloud Data platform to succeed in the role of a Cloud data expert. The current market is trending towards the latest cloud technologies, which is the need of the hour. Google being the pioneer, is dominating this space with the right set of cloud services being offered as part of GCP (Google Cloud Platform). At this juncture, this book will be very vital and will cover all the services that are being offered by GCP, putting emphasis on Data services. This book starts with sophisticated knowledge on Cloud Computing. It also explains different types of data services/technology and machine learning algorithm/Pre-Trained API through real-business problems, which are built on the Google Cloud Platform (GCP). With some of the latest business examples and hands-on guide, this book will enable the developers entering the data analytics fields to implement an end-to-end data pipeline, using GCP Data services. Through the course of the book, you will come across multiple industry-wise use cases, like Building Datawarehouse using Big Query, a sample real-time data analytics solution on machine learning and Artificial Intelligence that helped with the business decision, by employing a variety of data science approaches on Google Cloud environment. Whether yourÊbusinessÊis at the early stage of cloud implementation in its journey or well on its way to digital transformation,ÊGoogle Cloud'sÊsolutions and technologies will always help chart a path to success. This book can be used to develop the GCP concepts in an easy way. It contains many examples showcasing the implementation of a GCP service. It enables the learning of the basic and advance concepts of Google Cloud Data Platform. This book is divided into 7 chapters and provides a detailed description of the core concepts of each of the Data services offered by Google Cloud. KEY FEATURES Learn the basic concept of Cloud Computing along with different Cloud service provides with their supported Models (IaaS/PaaS/SaaS) Learn the basics of Compute Engine, App Engine, Container Engine, Project and Billing setup in the Google Cloud Platform Learn how and when to use Cloud DataFlow, Cloud DataProc and Cloud DataPrepÊ Build real-time data pipeline to support real-time analytics using Pub/Sub messaging service Setting up a fully managed GCP Big Data Cluster using Cloud DataProc for runningÊApache SparkÊandÊApache HadoopÊclusters in a simpler, more cost-efficient manner Learn how to use Cloud Data Studio for visualizing the data on top of Big Query Implement and understand real-world business scenarios for Machine Learning, Data Pipeline Engineering WHAT WILL YOU LEARN By the end of the book, you will have come across different data services and platforms offered by Google Cloud, and how those services/features can be enabled to serve business needs. You will also see a few case studies to put your knowledge to practice and solve business problems such as building a real-time streaming pipeline engine, Scalable Data Warehouse on Cloud, fully managed Hadoop cluster on Cloud and enabling TensorFlow/Machine Learning APIÕs to support real-life business problems. Remember to practice additional examples to master these techniques. WHO IS THIS BOOK FOR This book is for professionals as well as graduates who want to build a career in Google Cloud data analytics technologies. While no prior knowledge of Cloud Computing or related technologies is assumed, it will be helpful to have some data background and experience. One stop shop for those who wish to get an initial to advance understanding of the GCP data platform. The target audience will be data engineers/professionals who are new, as well as those who are acquainted with the tools and techniques related to cloud and data space.ÊÊ _Ê Ê Ê Individuals who have basic data understanding (i.e. Data and cloud) and have done some work in the field ofÊ data analytics, can refer/use this book to master their knowledge/understanding. _Ê Ê Ê The highlight of this book is that it will start with theÊ basic cloud computing fundamentals and will move on to cover the advance concepts on GCP cloud data analytics and hence can be referred across multiple different levels of audiences.Ê Table of Contents 1. GCP Overview and Architecture 2. Data Storage in GCPÊ 3. Data Processing in GCP with Pub/Sub and DataflowÊ 4. Data Processing in GCP with DataPrep and Dataflow 5. Big Query and Data Studio 6. Machine Learning with GCP 7. Sample Use cases and Examples |
| data engineering on google cloud platform: Data Engineering with Google Cloud Platform - Second Edition ADI. WIJAYA, 2024-04-30 This book will help you delve into data governance on Google Cloud. You'll also cover latest technological advancements in the domain and be able to build and deploy data pipelines confidently. |
| data engineering on google cloud platform: Data Engineering with Google Cloud Platform Adi Wijaya, 2024-04-30 Become a successful data engineer by building and deploying your own data pipelines on Google Cloud, including making key architectural decisions Key Features Get up to speed with data governance on Google Cloud Learn how to use various Google Cloud products like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream Boost your confidence by getting Google Cloud data engineering certification guidance from real exam experiences Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe second edition of Data Engineering with Google Cloud builds upon the success of the first edition by offering enhanced clarity and depth to data professionals navigating the intricate landscape of data engineering. Beyond its foundational lessons, this new edition delves into the essential realm of data governance within Google Cloud, providing you with invaluable insights into managing and optimizing data resources effectively. Written by a Data Strategic Cloud Engineer at Google, this book helps you stay ahead of the curve by guiding you through the latest technological advancements in the Google Cloud ecosystem. You’ll cover essential aspects, from exploring Cloud Composer 2 to the evolution of Airflow 2.5. Additionally, you’ll explore how to work with cutting-edge tools like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream to perform data governance on datasets. By the end of this book, you'll be equipped to navigate the ever-evolving world of data engineering on Google Cloud, from foundational principles to cutting-edge practices.What you will learn Load data into BigQuery and materialize its output Focus on data pipeline orchestration using Cloud Composer Formulate Airflow jobs to orchestrate and automate a data warehouse Establish a Hadoop data lake, generate ephemeral clusters, and execute jobs on the Dataproc cluster Harness Pub/Sub for messaging and ingestion for event-driven systems Apply Dataflow to conduct ETL on streaming data Implement data governance services on Google Cloud Who this book is for Data analysts, IT practitioners, software engineers, or any data enthusiasts looking to have a successful data engineering career will find this book invaluable. Additionally, experienced data professionals who want to start using Google Cloud to build data platforms will get clear insights on how to navigate the path. Whether you're a beginner who wants to explore the fundamentals or a seasoned professional seeking to learn the latest data engineering concepts, this book is for you. |
| data engineering on google cloud platform: Official Google Cloud Certified Associate Cloud Engineer Study Guide Dan Sullivan, 2019-04-01 The Only Official Google Cloud Study Guide The Official Google Cloud Certified Associate Cloud Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Engineering certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Official Google Cloud Certified Associate Cloud Engineer Study Guide is your ace in the hole for deploying and managing Google Cloud Services. Select the right Google service from the various choices based on the application to be built Compute with Cloud VMs and managing VMs Plan and deploying storage Network and configure access and security Google Cloud Platform is a leading public cloud that provides its users to many of the same software, hardware, and networking infrastructure used to power Google services. Businesses, organizations, and individuals can launch servers in minutes, store petabytes of data, and implement global virtual clouds with the Google Cloud Platform. Certified Associate Cloud Engineers have demonstrated the knowledge and skills needed to deploy and operate infrastructure, services, and networks in the Google Cloud. This exam guide is designed to help you understand the Google Cloud Platform in depth so that you can meet the needs of those operating resources in the Google Cloud. |
| data engineering on google cloud platform: Google Cloud Platform for Data Engineering Alasdair Gilchrist, Google Cloud Platform for Data Engineering is designed to take the beginner through a journey to become a competent and certified GCP data engineer. The book, therefore, is split into three parts; the first part covers fundamental concepts of data engineering and data analysis from a platform and technology-neutral perspective. Reading part 1 will bring a beginner up to speed with the generic concepts, terms and technologies we use in data engineering. The second part, which is a high-level but comprehensive introduction to all the concepts, components, tools and services available to us within the Google Cloud Platform. Completing this section will provide the beginner to GCP and data engineering with a solid foundation on the architecture and capabilities of the GCP. Part 3, however, is where we delve into the moderate to advanced techniques that data engineers need to know and be able to carry out. By this time the raw beginner you started the journey at the beginning of part 1 will be a knowledgable albeit inexperienced data engineer. However, by the conclusion of part 3, they will have gained the advanced knowledge of data engineering techniques and practices on the GCP to pass not only the certification exam but also most interviews and practical tests with confidence. In short part 3, will provide the prospective data engineer with detailed knowledge on setting up and configuring DataProc - GCPs version of the Spark/Hadoop ecosystem for big data. They will also learn how to build and test streaming and batch data pipelines using pub/sub/ dataFlow and BigQuery. Furthermore, they will learn how to integrate all the ML and AI Platform components and APIs. They will be accomplished in connecting data analysis and visualisation tools such as Datalab, DataStudio and AI notebooks amongst others. They will also by now know how to build and train a TensorFlow DNN using APIs and Keras and optimise it to run large public data sets. Also, they will know how to provision and use Kubeflow and Kube Pipelines within Google Kubernetes engines to run container workloads as well as how to take advantage of serverless technologies such as Cloud Run and Cloud Functions to build transparent and seamless data processing platforms. The best part of the book though is its compartmental design which means that anyone from a beginner to an intermediate can join the book at whatever point they feel comfortable. |
| data engineering on google cloud platform: Google Cloud Professional Data Engineer , 2024-10-26 Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| data engineering on google cloud platform: Google Cloud Certified Associate Cloud Engineer Study Guide Dan Sullivan, 2023-02-02 Quickly and efficiently prepare for the Google Associate Cloud Engineer certification with the proven Sybex method In the newly updated Second Edition of Google Cloud Certified Associate Cloud Engineer Study Guide, expert engineer and tech educator Dan Sullivan delivers an essential handbook for anyone preparing for the challenging Associate Cloud Engineer exam offered by Google and for those seeking to upgrade their Google Cloud engineering skillset. The book provides readers with coverage of every domain and competency tested by the Associate Cloud Engineer exam, including how to select the right Google compute service from the wide variety of choices, how to choose the best storage option for your services, and how to implement appropriate security controls and network functionality. This guide also offers: A strong emphasis on transforming readers into competent, job-ready applicants, with a focus on building skills in high demand by contemporary employers Concrete test-taking strategies, techniques, and tips to help readers conquer exam anxiety Complimentary access to a comprehensive online learning environment, complete with practice tests A must-have resource for practicing and aspiring Google Cloud engineers, Google Cloud Certified Associate Cloud Engineer Study Guide allows you to prepare for this challenging certification efficiently and completely. |
| data engineering on google cloud platform: Data Science on the Google Cloud Platform Valliappa Lakshmanan, 2017-12-12 Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You’ll learn how to: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines |
| data engineering on google cloud platform: Official Google Cloud Certified Professional Machine Learning Engineer Study Guide Mona Mona, Pratap Ramamurthy, 2023-10-27 Expert, guidance for the Google Cloud Machine Learning certification exam In Google Cloud Certified Professional Machine Learning Study Guide, a team of accomplished artificial intelligence (AI) and machine learning (ML) specialists delivers an expert roadmap to AI and ML on the Google Cloud Platform based on new exam curriculum. With Sybex, you’ll prepare faster and smarter for the Google Cloud Certified Professional Machine Learning Engineer exam and get ready to hit the ground running on your first day at your new job as an ML engineer. The book walks readers through the machine learning process from start to finish, starting with data, feature engineering, model training, and deployment on Google Cloud. It also discusses best practices on when to pick a custom model vs AutoML or pretrained models with Vertex AI platform. All technologies such as Tensorflow, Kubeflow, and Vertex AI are presented by way of real-world scenarios to help you apply the theory to practical examples and show you how IT professionals design, build, and operate secure ML cloud environments. The book also shows you how to: Frame ML problems and architect ML solutions from scratch Banish test anxiety by verifying and checking your progress with built-in self-assessments and other practical tools Use the Sybex online practice environment, complete with practice questions and explanations, a glossary, objective maps, and flash cards A can’t-miss resource for everyone preparing for the Google Cloud Certified Professional Machine Learning certification exam, or for a new career in ML powered by the Google Cloud Platform, this Sybex Study Guide has everything you need to take the next step in your career. |
| data engineering on google cloud platform: Google Compute Engine Marc Cohen, Kathryn Hurley, Paul Newson, 2014-12-15 Learn how to run large-scale, data-intensive workloads with Compute Engine, Google’s cloud platform. Written by Google engineers, this tutorial walks you through the details of this Infrastructure as a Service by showing you how to develop a project with it from beginning to end. You’ll learn best practices for using Compute Engine, with a focus on solving practical problems. With programming examples written in Python and JavaScript, you’ll also learn how to use Compute Engine with Docker containers and other platforms, frameworks, tools, and services. Discover how this IaaS helps you gain unparalleled performance and scalability with Google’s advanced storage and computing technologies. Access and manage Compute Engine resources with a web UI, command-line interface, or RESTful interface Configure, customize, and work with Linux VM instances Explore storage options: persistent disk, Cloud Storage, Cloud SQL (MySQL in the cloud), or Cloud Datastore NoSQL service Use multiple private networks, and multiple instances on each network Build, deploy, and test a simple but comprehensive cloud computing application step-by-step Use Compute Engine with Docker, Node.js, ZeroMQ, Web Starter Kit, AngularJS, WebSocket, and D3.js |
| data engineering on google cloud platform: Data Science on the Google Cloud Platform Valliappa Lakshmanan, 2022-03-29 Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines |
| data engineering on google cloud platform: 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 on google cloud platform: Google Cloud Platform for Architects Vitthal Srinivasan, Janani Ravi, Judy Raj, 2018-06-26 Get acquainted with GCP and manage robust, highly available, and dynamic solutions to drive business objective Key Features Identify the strengths, weaknesses and ideal use-cases for individual services offered on the Google Cloud Platform Make intelligent choices about which cloud technology works best for your use-case Leverage Google Cloud Platform to analyze and optimize technical and business processes Book Description Using a public cloud platform was considered risky a decade ago, and unconventional even just a few years ago. Today, however, use of the public cloud is completely mainstream - the norm, rather than the exception. Several leading technology firms, including Google, have built sophisticated cloud platforms, and are locked in a fierce competition for market share. The main goal of this book is to enable you to get the best out of the GCP, and to use it with confidence and competence. You will learn why cloud architectures take the forms that they do, and this will help you become a skilled high-level cloud architect. You will also learn how individual cloud services are configured and used, so that you are never intimidated at having to build it yourself. You will also learn the right way and the right situation in which to use the important GCP services. By the end of this book, you will be able to make the most out of Google Cloud Platform design. What you will learn Set up GCP account and utilize GCP services using the cloud shell, web console, and client APIs Harness the power of App Engine, Compute Engine, Containers on the Kubernetes Engine, and Cloud Functions Pick the right managed service for your data needs, choosing intelligently between Datastore, BigTable, and BigQuery Migrate existing Hadoop, Spark, and Pig workloads with minimal disruption to your existing data infrastructure, by using Dataproc intelligently Derive insights about the health, performance, and availability of cloud-powered applications with the help of monitoring, logging, and diagnostic tools in Stackdriver Who this book is for If you are a Cloud architect who is responsible to design and manage robust cloud solutions with Google Cloud Platform, then this book is for you. System engineers and Enterprise architects will also find this book useful. A basic understanding of distributed applications would be helpful, although not strictly necessary. Some working experience on other public cloud platforms would help too. |
| data engineering on google cloud platform: Journey to Become a Google Cloud Machine Learning Engineer Dr. Logan Song, 2022-09-20 Prepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skills Key FeaturesA comprehensive yet easy-to-follow Google Cloud machine learning study guideExplore full-spectrum and step-by-step practice examples to develop hands-on skillsRead through and learn from in-depth discussions of Google ML certification exam questionsBook Description This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate. What you will learnProvision Google Cloud services related to data science and machine learningProgram with the Python programming language and data science librariesUnderstand machine learning concepts and model development processesExplore deep learning concepts and neural networksBuild, train, and deploy ML models with Google BigQuery ML, Keras, and Google Cloud Vertex AIDiscover the Google Cloud ML Application Programming Interface (API)Prepare to achieve Google Cloud Professional Machine Learning Engineer certificationWho this book is for Anyone from the cloud computing, data analytics, and machine learning domains, such as cloud engineers, data scientists, data engineers, ML practitioners, and engineers, will be able to acquire the knowledge and skills and achieve the Google Cloud professional ML Engineer certification with this study guide. Basic knowledge of Google Cloud Platform and Python programming is required to get the most out of this book. |
| data engineering on google cloud platform: Google Cloud Platform in Action John J. (JJ) Geewax, 2018-08-15 Summary Google Cloud Platform in Action teaches you to build and launch applications that scale, leveraging the many services on GCP to move faster than ever. You'll learn how to choose exactly the services that best suit your needs, and you'll be able to build applications that run on Google Cloud Platform and start more quickly, suffer fewer disasters, and require less maintenance. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Thousands of developers worldwide trust Google Cloud Platform, and for good reason. With GCP, you can host your applications on the same infrastructure that powers Search, Maps, and the other Google tools you use daily. You get rock-solid reliability, an incredible array of prebuilt services, and a cost-effective, pay-only-for-what-you-use model. This book gets you started. About the Book Google Cloud Platform in Action teaches you how to deploy scalable cloud applications on GCP. Author and Google software engineer JJ Geewax is your guide as you try everything from hosting a simple WordPress web app to commanding cloud-based AI services for computer vision and natural language processing. Along the way, you'll discover how to maximize cloud-based data storage, roll out serverless applications with Cloud Functions, and manage containers with Kubernetes. Broad, deep, and complete, this authoritative book has everything you need. What's inside The many varieties of cloud storage and computing How to make cost-effective choices Hands-on code examples Cloud-based machine learning About the Reader Written for intermediate developers. No prior cloud or GCP experience required. About the Author JJ Geewax is a software engineer at Google, focusing on Google Cloud Platform and API design. Table of Contents PART 1 - GETTING STARTED What is cloud? Trying it out: deploying WordPress on Google Cloud The cloud data center PART 2 - STORAGE Cloud SQL: managed relational storage Cloud Datastore: document storage Cloud Spanner: large-scale SQL Cloud Bigtable: large-scale structured data Cloud Storage: object storage PART 3 - COMPUTING Compute Engine: virtual machines Kubernetes Engine: managed Kubernetes clusters App Engine: fully managed applications Cloud Functions: serverless applications Cloud DNS: managed DNS hosting PART 4 - MACHINE LEARNING Cloud Vision: image recognition Cloud Natural Language: text analysis Cloud Speech: audio-to-text conversion Cloud Translation: multilanguage machine translation Cloud Machine Learning Engine: managed machine learning PART 5 - DATA PROCESSING AND ANALYTICS BigQuery: highly scalable data warehouse Cloud Dataflow: large-scale data processing Cloud Pub/Sub: managed event publishing |
| data engineering on google cloud platform: Official Google Cloud Certified Associate Cloud Engineer Study Guide Dan Sullivan, 2019-04-16 The Only Official Google Cloud Study Guide The Official Google Cloud Certified Associate Cloud Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Engineering certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Official Google Cloud Certified Associate Cloud Engineer Study Guide is your ace in the hole for deploying and managing Google Cloud Services. Select the right Google service from the various choices based on the application to be built Compute with Cloud VMs and managing VMs Plan and deploying storage Network and configure access and security Google Cloud Platform is a leading public cloud that provides its users to many of the same software, hardware, and networking infrastructure used to power Google services. Businesses, organizations, and individuals can launch servers in minutes, store petabytes of data, and implement global virtual clouds with the Google Cloud Platform. Certified Associate Cloud Engineers have demonstrated the knowledge and skills needed to deploy and operate infrastructure, services, and networks in the Google Cloud. This exam guide is designed to help you understand the Google Cloud Platform in depth so that you can meet the needs of those operating resources in the Google Cloud. |
| data engineering on google cloud platform: Google BigQuery: The Definitive Guide Valliappa Lakshmanan, Jordan Tigani, 2019-10-23 Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently. Valliappa Lakshmanan, tech lead for Google Cloud Platform, and Jordan Tigani, engineering director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you’re not familiar with or prefer to focus on specific tasks, this reference is indispensable. |
| data engineering on google cloud platform: Learning Google BigQuery Eric Brown, Thirukkumaran Haridass, 2017-12-22 Get a fundamental understanding of how Google BigQuery works by analyzing and querying large datasets About This Book Get started with BigQuery API and write custom applications using it Learn how BigQuery API can be used for storing, managing, and query massive datasets with ease A practical guide with examples and use-cases to teach you everything you need to know about Google BigQuery Who This Book Is For If you are a developer, data analyst, or a data scientist looking to run complex queries over thousands of records in seconds, this book will help you. No prior experience of working with BigQuery is assumed. What You Will Learn Get a hands-on introduction to Google Cloud Platform and its services Understand the different data types supported by Google BigQuery Migrate your enterprise data to BigQuery and query it using the legacy and standard SQL techniques Use partition tables in your project and query external data sources and wild card tables Create tables and data sets dynamically using the BigQuery API Perform real-time inserting of records for analytics using Python and C# Visualize your BigQuery data by connecting it to third party tools such as Tableau and R Master the Google Cloud Pub/Sub for implementing real-time reporting and analytics of your Big Data In Detail Google BigQuery is a popular cloud data warehouse for large-scale data analytics. This book will serve as a comprehensive guide to mastering BigQuery, and how you can utilize it to quickly and efficiently get useful insights from your Big Data. You will begin with getting a quick overview of the Google Cloud Platform and the various services it supports. Then, you will be introduced to the Google BigQuery API and how it fits within in the framework of GCP. The book covers useful techniques to migrate your existing data from your enterprise to Google BigQuery, as well as readying and optimizing it for analysis. You will perform basic as well as advanced data querying using BigQuery, and connect the results to various third party tools for reporting and visualization purposes such as R and Tableau. If you're looking to implement real-time reporting of your streaming data running in your enterprise, this book will also help you. This book also provides tips, best practices and mistakes to avoid while working with Google BigQuery and services that interact with it. By the time you're done with it, you will have set a solid foundation in working with BigQuery to solve even the trickiest of data problems. Style and Approach This book follows a step-by-step approach to teach readers the concepts of Google BigQuery using SQL. To explain various data querying processes, large-scale datasets are used wherever required. |
| data engineering on google cloud platform: Google Cloud Certified Professional Cloud Network Engineer Guide Maurizio Ipsale, Mirko Gilioli, 2022-01-13 Gain practical skills to design, deploy, and manage networks on Google Cloud and prepare to gain Professional Cloud Network Engineer certification Key FeaturesGain hands-on experience in implementing VPCs, hybrid connectivity, network services, and securityEstablish a secure network architecture by learning security best practicesLeverage this comprehensive guide to gain Professional Cloud Network Engineer certificationBook Description Google Cloud, the public cloud platform from Google, has a variety of networking options, which are instrumental in managing a networking architecture. This book will give you hands-on experience of implementing and securing networks in Google Cloud Platform (GCP). You will understand the basics of Google Cloud infrastructure and learn to design, plan, and prototype a network on GCP. After implementing a Virtual Private Cloud (VPC), you will configure network services and implement hybrid connectivity. Later, the book focuses on security, which forms an important aspect of a network. You will also get to grips with network security and learn to manage and monitor network operations in GCP. Finally, you will learn to optimize network resources and delve into advanced networking. The book also helps you to reinforce your knowledge with the help of mock tests featuring exam-like questions. By the end of this book, you will have gained a complete understanding of networking in Google Cloud and learned everything you need to pass the certification exam. What you will learnUnderstand the fundamentals of Google Cloud architectureImplement and manage network architectures in Google Cloud PlatformGet up to speed with VPCs and configure VPC networks, subnets, and routersUnderstand the command line interface and GCP console for networkingGet to grips with logging and monitoring to troubleshoot network and securityUse the knowledge you gain to implement advanced networks on GCPWho this book is for This Google Cloud certification book is for cloud network engineers, cloud architects, cloud engineers, administrators, and anyone who is looking to design, implement, and manage network architectures in Google Cloud Platform. You can use this book as a guide for passing the Professional Cloud Network Engineer certification exam. You need to have at least a year of experience in Google Cloud, basic enterprise-level network design experience, and a fundamental understanding of Cloud Shell to get started with this book. |
| data engineering on google cloud platform: Official Google Cloud Certified Professional Cloud Architect Study Guide Dan Sullivan, 2019-10-10 Sybex's proven Study Guide format teaches Google Cloud Architect job skills and prepares you for this important new Cloud exam. The Google Cloud Certified Professional Cloud Architect Study Guide is the essential resource for anyone preparing for this highly sought-after, professional-level certification. Clear and accurate chapters cover 100% of exam objectives—helping you gain the knowledge and confidence to succeed on exam day. A pre-book assessment quiz helps you evaluate your skills, while chapter review questions emphasize critical points of learning. Detailed explanations of crucial topics include analyzing and defining technical and business processes, migration planning, and designing storage systems, networks, and compute resources. Written by Dan Sullivan—a well-known author and software architect specializing in analytics, machine learning, and cloud computing—this invaluable study guide includes access to the Sybex interactive online learning environment, which includes complete practice tests, electronic flash cards, a searchable glossary, and more. Providing services suitable for a wide range of applications, particularly in high-growth areas of analytics and machine learning, Google Cloud is rapidly gaining market share in the cloud computing world. Organizations are seeking certified IT professionals with the ability to deploy and operate infrastructure, services, and networks in the Google Cloud. Take your career to the next level by validating your skills and earning certification. Design and plan cloud solution architecture Manage and provision cloud infrastructure Ensure legal compliance and security standards Understand options for implementing hybrid clouds Develop solutions that meet reliability, business, and technical requirements The Google Cloud Certified Professional Cloud Architect Study Guide is a must-have for IT professionals preparing for certification to deploy and manage Google cloud services. |
| data engineering on google cloud platform: Visualizing Google Cloud Priyanka Vergadia, 2022-03-11 Easy-to-follow visual walkthrough of every important part of the Google Cloud Platform The Google Cloud Platform incorporates dozens of specialized services that enable organizations to offload technological needs onto the cloud. From routine IT operations like storage to sophisticated new capabilities including artificial intelligence and machine learning, the Google Cloud Platform offers enterprises the opportunity to scale and grow efficiently. In Visualizing Google Cloud: Illustrated References for Cloud Engineers & Architects, Google Cloud expert Priyanka Vergadia delivers a fully illustrated, visual guide to matching the best Google Cloud Platform services to your own unique use cases. After a brief introduction to the major categories of cloud services offered by Google, the author offers approximately 100 solutions divided into eight categories of services included in Google Cloud Platform: Compute Storage Databases Data Analytics Data Science, Machine Learning and Artificial Intelligence Application Development and Modernization with Containers Networking Security You’ll find richly illustrated flowcharts and decision diagrams with straightforward explanations in each category, making it easy to adopt and adapt Google’s cloud services to your use cases. With coverage of the major categories of cloud models—including infrastructure-, containers-, platforms-, functions-, and serverless—and discussions of storage types, databases and Machine Learning choices, Visualizing Google Cloud: Illustrated References for Cloud Engineers & Architects is perfect for every Google Cloud enthusiast, of course. It is for anyone who is planning a cloud migration or new cloud deployment. It is for anyone preparing for cloud certification, and for anyone looking to make the most of Google Cloud. It is for cloud solutions architects, IT decision-makers, and cloud data and ML engineers. In short, this book is for YOU. |
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| data engineering on google cloud platform: Google Cloud Certified Associate Cloud Engineer All-in-One Exam Guide Jack Hyman, 2020-11-05 This study guide offers 100% coverage of every objective for the Google Cloud Certified Associate Cloud Engineer exam Take the challenging Google Cloud Certified Associate Cloud Engineer exam with confidence using the comprehensive information contained in this effective self-study guide. The book serves as an introduction to Google Cloud Platform (GCP) and shows you how to pass the test. Beyond exam preparation, the guide also serves as a valuable on-the-job reference. Written by a recognized expert in the field, Google Cloud Certified Associate Cloud Engineer All-In-One Exam Guide is based on proven pedagogy and features special elements that teach and reinforce practical skills. The book contains accurate practice questions and detailed explanations. You will discover how to plan set up, and configure GCP; ensure effective operation; and administer access and security. Covers every topic on the exam—demonstrated through exercises, sample exams, and practice use cases Provides online access to TotalTester customizable exam engine with additional practice questions Written by a cloud computing expert, educator, and experienced author |
| data engineering on google cloud platform: Google Cloud Certified Professional Cloud Architect Study Guide Dan Sullivan, 2022-03-22 An indispensable guide to the newest version of the Google Certified Professional Cloud Architect certification The newly revised Second Edition of the Google Cloud Certified Professional Cloud Architect Study Guide delivers a proven and effective roadmap to success on the latest Professional Cloud Architect accreditation exam from Google. You'll learn the skills you need to excel on the test and in the field, with coverage of every exam objective and competency, including focus areas of the latest exam such as Kubernetes, Anthos, and multi-cloud architectures. The book explores the design, analysis, development, operations, and migration components of the job, with intuitively organized lessons that align with the real-world job responsibilities of a Google Cloud professional and with the PCA exam topics. Architects need more than the ability to recall facts about cloud services, they need to be able to reason about design decisions. This study guide is unique in how it helps you learn to think like an architect: understand requirements, assess constraints, choose appropriate architecture patterns, and consider the operational characteristics of the systems you design. Review questions and practice exams use scenario-based questions like those on the certification exam to build the test taking skills you will need. In addition to comprehensive material on compute resources, storage systems, networks, security, legal and regulatory compliance, reliability design, technical and business processes, and more, you'll get: The chance to begin or advance your career as an in-demand Google Cloud IT professional Invaluable opportunities to develop and practice the skills you'll need as a Google Cloud Architect Access to the Sybex online learning center, with chapter review questions, full-length practice exams, hundreds of electronic flashcards, and a glossary of key terms The ideal resource for anyone preparing for the Professional Cloud Architect certification from Google, Google Cloud Certified Professional Cloud Architect Study Guide, 2nd Edition is also a must-read resource for aspiring and practicing cloud professionals seeking to expand or improve their technical skillset and improve their effectiveness in the field. |
| data engineering on google cloud platform: The Definitive Guide to Modernizing Applications on Google Cloud Steve (Satish) Sangapu, Dheeraj Panyam, Jason Marston, 2022-01-06 Get to grips with the tools, services, and functions needed for application migration to help you move from legacy applications to cloud-native on Google Cloud Key FeaturesDiscover how a sample legacy application can be transformed into a cloud-native application on Google CloudLearn where to start and how to apply application modernization techniques and toolingWork with real-world use cases and instructions to modernize an application on Google CloudBook Description Legacy applications, which comprise 75–80% of all enterprise applications, often end up being stuck in data centers. Modernizing these applications to make them cloud-native enables them to scale in a cloud environment without taking months or years to start seeing the benefits. This book will help software developers and solutions architects to modernize their applications on Google Cloud and transform them into cloud-native applications. This book helps you to build on your existing knowledge of enterprise application development and takes you on a journey through the six Rs: rehosting, replatforming, rearchitecting, repurchasing, retiring, and retaining. You'll learn how to modernize a legacy enterprise application on Google Cloud and build on existing assets and skills effectively. Taking an iterative and incremental approach to modernization, the book introduces the main services in Google Cloud in an easy-to-understand way that can be applied immediately to an application. By the end of this Google Cloud book, you'll have learned how to modernize a legacy enterprise application by exploring various interim architectures and tooling to develop a cloud-native microservices-based application. What you will learnDiscover the principles and best practices for building cloud-native applicationsStudy the six Rs of migration strategy and learn when to choose which strategyRehost a legacy enterprise application on Google Compute EngineReplatform an application to use Google Load Balancer and Google Cloud SQLRefactor into a single-page application (SPA) supported by REST servicesReplatform an application to use Google Identity Platform and Firebase AuthenticationRefactor to microservices using the strangler patternAutomate the deployment process using a CI/CD pipeline with Google Cloud BuildWho this book is for This book is for software developers and solutions architects looking to gain experience in modernizing their enterprise applications to run on Google Cloud and transform them into cloud-native applications. Basic knowledge of Java and Spring Boot is necessary. Prior knowledge of Google Cloud is useful but not mandatory. |
| data engineering on google cloud platform: Hands On Google Cloud SQL and Cloud Spanner Navin Sabharwal, Shakuntala Gupta Edward, 2019-12-19 Discover the methodologies and best practices for getting started with Google Cloud Platform relational services – CloudSQL and CloudSpanner. The book begins with the basics of working with the Google Cloud Platform along with an introduction to the database technologies available for developers from Google Cloud. You'll then take an in-depth hands on journey into Google CloudSQL and CloudSpanner, including choosing the right platform for your application needs, planning, provisioning, designing and developing your application. Sample applications are given that use Python to connect to CloudSQL and CloudSpanner, along with helpful features provided by the engines. You''ll also implement practical best practices in the last chapter. Hands On Google Cloud SQL and Cloud Spanner is a great starting point to apply GCP data offerings in your technology stack and the code used allows you to try out the examples and extend them in interesting ways. What You'll Learn Get started with Big Data technologies on the Google Cloud Platform Review CloudSQL and Cloud Spanner from basics to administration Apply best practices and use Google’s CloudSQL and CloudSpanner offering Work with code in Python notebooks and scripts Who This Book Is For Application architects, database architects, software developers, data engineers, cloud architects. |
| data engineering on google cloud platform: Cloud Analytics with Google Cloud Platform Sanket Thodge, 2018-04-10 Combine the power of analytics and cloud computing for faster and efficient insights Key Features Master the concept of analytics on the cloud: and how organizations are using it Learn the design considerations and while applying a cloud analytics solution Design an end-to-end analytics pipeline on the cloud Book Description With the ongoing data explosion, more and more organizations all over the world are slowly migrating their infrastructure to the cloud. These cloud platforms also provide their distinct analytics services to help you get faster insights from your data. This book will give you an introduction to the concept of analytics on the cloud, and the different cloud services popularly used for processing and analyzing data. If you’re planning to adopt the cloud analytics model for your business, this book will help you understand the design and business considerations to be kept in mind, and choose the best tools and alternatives for analytics, based on your requirements. The chapters in this book will take you through the 70+ services available in Google Cloud Platform and their implementation for practical purposes. From ingestion to processing your data, this book contains best practices on building an end-to-end analytics pipeline on the cloud by leveraging popular concepts such as machine learning and deep learning. By the end of this book, you will have a better understanding of cloud analytics as a concept as well as a practical know-how of its implementation What you will learn Explore the basics of cloud analytics and the major cloud solutions Learn how organizations are using cloud analytics to improve the ROI Explore the design considerations while adopting cloud services Work with the ingestion and storage tools of GCP such as Cloud Pub/Sub Process your data with tools such as Cloud Dataproc, BigQuery, etc Over 70 GCP tools to build an analytics engine for cloud analytics Implement machine learning and other AI techniques on GCP Who this book is for This book is targeted at CIOs, CTOs, and even analytics professionals looking for various alternatives to implement their analytics pipeline on the cloud. Data professionals looking to get started with cloud-based analytics will also find this book useful. Some basic exposure to cloud platforms such as GCP will be helpful, but not mandatory. |
| data engineering on google cloud platform: Building Google Cloud Platform Solutions Ted Hunter, Steven Porter, Legorie Rajan PS, 2019-03-26 Build cost-effective and robust cloud solutions with Google Cloud Platform (GCP) using these simple and practical recipes Key FeaturesExplore the various service offerings of the GCPHost a Python application on Google Compute EngineSecurely maintain application states with Cloud Storage, Datastore, and BigtableBook Description GCP is a cloud computing platform with a wide range of products and services that enable you to build and deploy cloud-hosted applications. This Learning Path will guide you in using GCP and designing, deploying, and managing applications on Google Cloud. You will get started by learning how to use App Engine to access Google's scalable hosting and build software that runs on this framework. With the help of Google Compute Engine, you’ll be able to host your workload on virtual machine instances. The later chapters will help you to explore ways to implement authentication and security, Cloud APIs, and command-line and deployment management. As you hone your skills, you’ll understand how to integrate your new applications with various data solutions on GCP, including Cloud SQL, Bigtable, and Cloud Storage. Following this, the book will teach you how to streamline your workflow with tools, including Source Repositories, Container Builder, and Stackdriver. You'll also understand how to deploy and debug services with IntelliJ, implement continuous delivery pipelines, and configure robust monitoring and alerts for your production systems. By the end of this Learning Path, you'll be well versed with GCP’s development tools and be able to develop, deploy, and manage highly scalable and reliable applications. This Learning Path includes content from the following Packt products: Google Cloud Platform for Developers Ted Hunter and Steven PorterGoogle Cloud Platform Cookbook by Legorie Rajan PSWhat you will learnHost an application using Google Cloud FunctionsMigrate a MySQL database to Cloud SpannerConfigure a network for a highly available application on GCPLearn simple image processing using Storage and Cloud FunctionsAutomate security checks using Policy ScannerDeploy and run services on App Engine and Container EngineMinimize downtime and mitigate issues with Stackdriver Monitoring and DebuggerIntegrate with big data solutions, including BigQuery, Dataflow, and Pub/SubWho this book is for This Learning Path is for IT professionals, engineers, and developers who want to implement Google Cloud in their organizations. Administrators and architects planning to make their organization more efficient with Google Cloud will also find this Learning Path useful. Basic understanding of GCP and its services is a must. |
| data engineering on google cloud platform: Professional Cloud Architect Google Cloud Certification Guide Konrad Clapa, Brian Gerrard, Yujun Liang, 2021-12-23 Become a Professional Cloud Architect by exploring the essential concepts, tools, and services in GCP and working through practice tests designed to help you take the exam confidently Key FeaturesPlan and design a GCP cloud solution architectureEnsure the security and reliability of your cloud solutions and operationsAssess your knowledge by taking mock tests with up-to-date exam questionsBook Description Google Cloud Platform (GCP) is one of the industry leaders thanks to its array of services that can be leveraged by organizations to bring the best out of their infrastructure. This book is a comprehensive guide for learning methods to effectively utilize GCP services and help you become acquainted with the topics required to pass Google's Professional Cloud Architect certification exam. Following the Professional Cloud Architect's official exam syllabus, you'll first be introduced to the GCP. The book then covers the core services that GCP offers, such as computing and storage, and takes you through effective methods of scaling and automating your cloud infrastructure. As you progress through the chapters, you'll get to grips with containers and services and discover best practices related to the design and process. This revised second edition features new topics such as Cloud Run, Anthos, Data Fusion, Composer, and Data Catalog. By the end of this book, you'll have gained the knowledge required to take and pass the Google Cloud Certification – Professional Cloud Architect exam and become an expert in GCP services. What you will learnUnderstand the benefits of being a Google Certified Professional Cloud ArchitectFind out how to enroll for the Professional Cloud Architect examMaster the compute options in GCPExplore security and networking options in GCPGet to grips with managing and monitoring your workloads in GCPUnderstand storage, big data, and machine learning servicesBecome familiar with exam scenarios and passing strategiesWho this book is for If you are a cloud architect, cloud engineer, administrator, or any IT professional looking to learn how to implement Google Cloud services in your organization and become a GCP Certified Professional Cloud Architect, this book is for you. Basic knowledge of server infrastructure, including Linux and Windows Servers, is assumed. A solid understanding of network and storage will help you to make the most out of this book. |
| data engineering on google cloud platform: Informationweek , 2008 |
| data engineering on google cloud platform: Google Cloud Platform Cookbook Legorie Rajan PS, 2018-04-16 Practical recipes to implement cost-effective and scalable cloud solutions for your organization Key Features Implement Google Cloud services in your organization Leverage Google Cloud components to secure your organization’s data A recipe-based guide that promises hands-on experience in deploying a highly scalable and available environment Book Description Google Cloud Platform is a cloud computing platform that offers products and services to host applications using state-of-the art infrastructure and technology. You can build and host applications and websites, store data, and analyze data on Google's scalable infrastructure. This book follows a recipe-based approach, giving you hands-on experience to make the most of Google Cloud services. This book starts with practical recipes that explain how to utilize Google Cloud's common services. Then, you'll see how to make full use of Google Cloud components such as networking, security, management, and developer tools. Next, we'll deep dive into implementing core Google Cloud services into your organization, with practical recipes on App Engine, Compute Engine microservices with Cloud Functions, virtual networks, and Cloud Storage. Later, we'll provide recipes on implementing authentication and security, Cloud APIs, command-line management, deployment management, and the Cloud SDK. Finally, we'll cover administration troubleshooting tasks with the Compute and Container Engines and we'll show how to monitor your organization's efficiency with best practices. By the end of this book, you'll have a complete understanding of how to implement Google Cloud services in your organization with ease. What you will learn Host a Python application on Google Compute Engine Host an application using Google Cloud Functions Migrate a MySQL DB to Cloud Spanner Configure a network for a highly available application on GCP Learn simple image processing using Storage and Cloud Functions Automate security checks using Policy Scanner Understand tools for monitoring a production environment in GCP Learn to manage multiple projects using service accounts Who this book is for This book is for IT professionals, engineers, and developers looking at implementing Google Cloud in their organizations. Administrators and architects planning to make their organization more efficient with Google Cloud will also find this book useful. Basic understanding of Cloud services and the Google Cloud platform is necessary. |
| data engineering on google cloud platform: Become GCP Cloud Digital Leader Certified in 7 Days Abhinav Krishna Kaiser, |
| data engineering on google cloud platform: Data Engineering for Modern Applications Dr. RVS Praveen, 2024-09-23 A resource designed for anybody interested in comprehending the whole lifecycle of data management in the current digital era is Data Engineering for Modern Applications. The book is organised into parts that systematically address key subjects. An introduction to data engineering principles is given first, followed by a thorough examination of data pipelines, storage options, and data transformation techniques. Data orchestration systems, cloud services, and distributed computing are just a few of the specialised tools and platforms that are being addressed in depth as the discipline of data engineering develops. This book places a lot of emphasis on using data engineering concepts in practical situations. The purpose of the chapters is to demonstrate best practices for creating, implementing, and overseeing scalable and effective data pipelines. Data Engineering for Modern Applications offers a useful framework that is easily applicable in a range of fields by including real-world examples and case studies. The book also discusses how data engineering supports AI and machine learning, outlining the procedures that guarantee data availability, consistency, and quality for these cutting-edge applications. This book serves as a manual for engineers, data scientists, and business professionals who are dedicated to using data in a future where decisions are made based on facts. This thorough guide will provide readers with the knowledge and self-assurance they need to address data difficulties, adjust to new technologies, and eventually help current data-driven systems be implemented successfully. |
| data engineering on google cloud platform: Google Cloud Platform for Data Science Dr. Shitalkumar R. Sukhdeve, Sandika S. Sukhdeve, 2023-12-03 This book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform. Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models. The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects. Readers will learn how to set up a Google Colaboratory account and run Jupyter notebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL. What You Will Learn Set up a GCP account and project Explore BigQuery and its use cases, including machine learning Understand Google Cloud AI Platform and its capabilities Use Vertex AI for training and deploying machine learning models Explore Google Cloud Dataproc and its use cases for big data processing Create and share data visualizations and reports with Looker Data Studio Explore Google Cloud Dataflow and its use cases for batch and stream data processing Run data processing pipelines on Cloud Dataflow Explore Google Cloud Storage and its use cases for data storage Get an introduction to Google Cloud SQL and its use cases for relational databases Get an introduction to Google Cloud Pub/Sub and its use cases for real-time data streaming Who This Book Is For Data scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projects |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
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The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
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5. Competition Increasing competition between AWS, Microsoft Azure, and Google Cloud Platform in pricing, reliability, and against other vendors. Expect a shift towards pay-as-you-use …
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Google cloud platform (GCP) launched in in 2011 by Google to provide cloud computing services to its customers. Services provided by GCP includes storage, big data ,databases,
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A Review on Amazon Web Service (AWS), Microsoft Azure …
Google cloud platform (GCP) launched in in 2011 by Google to provide cloud computing services to its customers. Services provided by GCP includes storage, big data ,databases, analytics, …
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Professional Cloud Architect Exam Guide | English
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Data Security and Privacy Protection Issues in Cloud Computing
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A Comparative Economic Analysis for Cloud Computing
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