Aws Data Engineering Tutorial

  aws data engineering tutorial: AWS Certified Data Analytics Study Guide with Online Labs Asif Abbasi, 2021-04-13 Virtual, hands-on learning labs allow you to apply your technical skills in realistic environments. So Sybex has bundled AWS labs from XtremeLabs with our popular AWS Certified Data Analytics Study Guide to give you the same experience working in these labs as you prepare for the Certified Data Analytics Exam that you would face in a real-life application. These labs in addition to the book are a proven way to prepare for the certification and for work as an AWS Data Analyst. AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam is intended for individuals who perform in a data analytics-focused role. This UPDATED exam validates an examinee's comprehensive understanding of using AWS services to design, build, secure, and maintain analytics solutions that provide insight from data. It assesses an examinee's ability to define AWS data analytics services and understand how they integrate with each other; and explain how AWS data analytics services fit in the data lifecycle of collection, storage, processing, and visualization. The book focuses on the following domains: • Collection • Storage and Data Management • Processing • Analysis and Visualization • Data Security This is your opportunity to take the next step in your career by expanding and validating your skills on the AWS cloud. AWS is the frontrunner in cloud computing products and services, and the AWS Certified Data Analytics Study Guide: Specialty exam will get you fully prepared through expert content, and real-world knowledge, key exam essentials, chapter review questions, and much more. Written by an AWS subject-matter expert, this study guide covers exam concepts, and provides key review on exam topics. Readers will also have access to Sybex's superior online interactive learning environment and test bank, including chapter tests, practice exams, a glossary of key terms, and electronic flashcards. And included with this version of the book, XtremeLabs virtual labs that run from your browser. The registration code is included with the book and gives you 6 months of unlimited access to XtremeLabs AWS Certified Data Analytics Labs with 3 unique lab modules based on the book.
  aws data engineering tutorial: 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.
  aws data engineering tutorial: Ace the AWS Certified Data Engineer Exam Etienne Noumen, 2024-06-18 Ace the AWS Certified Data Engineer Exam: Mastering AWS Services for Data Ingestion, Transformation, and Pipeline Orchestration Unlock the full potential of AWS and elevate your data engineering skills with “Ace the AWS Certified Data Engineer Exam.” This comprehensive guide is tailored for professionals seeking to master the AWS Certified Data Engineer - Associate certification. Authored by Etienne Noumen, a seasoned Professional Engineer with over 20 years of software engineering experience and 5+ years specializing in AWS data engineering, this book provides an in-depth and practical approach to conquering the certification exam. Inside this book, you will find: • Detailed Exam Coverage: Understand the core AWS services related to data engineering, including data ingestion, transformation, and pipeline orchestration. • Practice Quizzes: Challenge yourself with practice quizzes designed to simulate the actual exam, complete with detailed explanations for each answer. • Real-World Scenarios: Learn how to apply AWS services to real-world data engineering problems, ensuring you can translate theoretical knowledge into practical skills. • Hands-On Labs: Gain hands-on experience with step-by-step labs that guide you through using AWS services like AWS Glue, Amazon Redshift, Amazon S3, and more. • Expert Insights: Benefit from the expertise of Etienne Noumen, who shares valuable tips, best practices, and insights from his extensive career in data engineering. This book goes beyond rote memorization, encouraging you to develop a deep understanding of AWS data engineering concepts and their practical applications. Whether you are an experienced data engineer or new to the field, “Ace the AWS Certified Data Engineer Exam” will equip you with the knowledge and skills needed to excel. Prepare to advance your career, validate your expertise, and become a certified AWS Data Engineer. Embrace the journey of learning, practice consistently, and master the tools and techniques that will set you apart in the rapidly evolving world of cloud data solutions. Get your copy today and start your journey towards AWS certification success!
  aws data engineering tutorial: AWS Certified Data Engineer Study Guide Syed Humair, Chenjerai Gumbo, Adam Gatt, Asif Abbasi, Lakshmi Nair, 2024-11-27
  aws data engineering tutorial: Data Engineering with AWS Gareth Eagar, 2021-12-29 The missing expert-led manual for the AWS ecosystem — go from foundations to building data engineering pipelines effortlessly Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Learn about common data architectures and modern approaches to generating value from big data Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Learn how to architect and implement data lakes and data lakehouses for big data analytics from a data lakes expert Book DescriptionWritten by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.What you will learn Understand data engineering concepts and emerging technologies Ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Run complex SQL queries on data lake data using Amazon Athena Load data into a Redshift data warehouse and run queries Create a visualization of your data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Who this book is for This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.
  aws data engineering tutorial: 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
  aws data engineering tutorial: A Beginners Guide to Amazon Web Services Parul Dubey, Rohit Raja, 2024-01-18 Amazon Web Services (AWS) provides on-demand cloud computing platforms and application programming interfaces (APIs) to individuals, companies, and govern- ments, along with distributed computing processing capacity and software tools via AWS server farms. This text presents a hands-on approach for beginners to get started with Amazon Web Services (AWS) in a simple way. Key Features It discusses topics such as Amazon Elastic Compute Cloud, Elastic Load Balancing, Auto Scaling Groups, and Amazon Simple Storage Service. It showcases Amazon Web Services’ identity, access management resources, and attribute-based access control. It covers serverless computing services, Virtual Private Cloud, Amazon Aurora, and Amazon Comprehend. It explains Amazon Web Services Free Tier, Amazon Web Services Marketplace, and Amazon Elastic Container Service. It includes security in Amazon Web Services, the shared responsibilitymodel, and high-performance computing on Amazon Web Services. The text is primarily written for graduate students, professionals, and academic researchers working in the fields of computer science, engineering, and information technology. Parul Dubey is currently working as an Assistant professor in the Department of Artificial Intelligence at G H Raisoni College of Engineering, Nagpur, India. She has filed for 15 Indian patents. She is responsible for about 10 publications in conference proceedings, Scopus, and journals. She has contributed book chapters in an edited book published by CRC Press and other reputed publishers. She is also an AWS Certified Cloud Practitioner. Rohit Raja is working as an associate professor and head in the Department of Information Technology at Guru Ghasidas Vishwavidyalaya, Bilaspur, India. His research interests include facial recognition, signal processing, networking, and data mining. He has pub- lished 100 research papers in various international and national journals (including publications by the IEEE, Springer, etc.) and proceedings of reputed international and national conferences (again including publications by Springer and the IEEE).
  aws data engineering tutorial: Guide to Big Data Applications S. Srinivasan, 2017-05-25 This handbook brings together a variety of approaches to the uses of big data in multiple fields, primarily science, medicine, and business. This single resource features contributions from researchers around the world from a variety of fields, where they share their findings and experience. This book is intended to help spur further innovation in big data. The research is presented in a way that allows readers, regardless of their field of study, to learn from how applications have proven successful and how similar applications could be used in their own field. Contributions stem from researchers in fields such as physics, biology, energy, healthcare, and business. The contributors also discuss important topics such as fraud detection, privacy implications, legal perspectives, and ethical handling of big data.
  aws data engineering tutorial: Serverless ETL and Analytics with AWS Glue Vishal Pathak, Subramanya Vajiraya, Noritaka Sekiyama, Tomohiro Tanaka, Albert Quiroga, Ishan Gaur, 2022-08-30 Build efficient data lakes that can scale to virtually unlimited size using AWS Glue Key Features Book DescriptionOrganizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide serverless Spark, enabling you to create and manage data lakes in a serverless fashion. This guide shows you how AWS Glue can be used to solve real-world problems along with helping you learn about data processing, data integration, and building data lakes. Beginning with AWS Glue basics, this book teaches you how to perform various aspects of data analysis such as ad hoc queries, data visualization, and real-time analysis using this service. It also provides a walk-through of CI/CD for AWS Glue and how to shift left on quality using automated regression tests. You’ll find out how data security aspects such as access control, encryption, auditing, and networking are implemented, as well as getting to grips with useful techniques such as picking the right file format, compression, partitioning, and bucketing. As you advance, you’ll discover AWS Glue features such as crawlers, Lake Formation, governed tables, lineage, DataBrew, Glue Studio, and custom connectors. The concluding chapters help you to understand various performance tuning, troubleshooting, and monitoring options. By the end of this AWS book, you’ll be able to create, manage, troubleshoot, and deploy ETL pipelines using AWS Glue.What you will learn Apply various AWS Glue features to manage and create data lakes Use Glue DataBrew and Glue Studio for data preparation Optimize data layout in cloud storage to accelerate analytics workloads Manage metadata including database, table, and schema definitions Secure your data during access control, encryption, auditing, and networking Monitor AWS Glue jobs to detect delays and loss of data Integrate Spark ML and SageMaker with AWS Glue to create machine learning models Who this book is for ETL developers, data engineers, and data analysts
  aws data engineering tutorial: Kubeflow Operations Guide Josh Patterson, Michael Katzenellenbogen, Austin Harris, 2020-12-04 Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable. Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft. Dive into Kubeflow architecture and learn best practices for using the platform Understand the process of planning your Kubeflow deployment Install Kubeflow on an existing on-premises Kubernetes cluster Deploy Kubeflow on Google Cloud Platform step-by-step from the command line Use the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWS Deploy and manage Kubeflow across a network of Azure cloud data centers around the world Use KFServing to develop and deploy machine learning models
  aws data engineering tutorial: Infrastructure Monitoring with Amazon CloudWatch Ewere Diagboya, 2021-04-16 Explore real-world examples of issues with systems and find ways to resolve them using Amazon CloudWatch as a monitoring service Key FeaturesBecome well-versed with monitoring fundamentals such as understanding the building blocks and architecture of networkingLearn how to ensure your applications never face downtimeGet hands-on with observing serverless applications and servicesBook Description CloudWatch is Amazon's monitoring and observability service, designed to help those in the IT industry who are interested in optimizing resource utilization, visualizing operational health, and eventually increasing infrastructure performance. This book helps IT administrators, DevOps engineers, network engineers, and solutions architects to make optimum use of this cloud service for effective infrastructure productivity. You'll start with a brief introduction to monitoring and Amazon CloudWatch and its core functionalities. Next, you'll get to grips with CloudWatch features and their usability. Once the book has helped you develop your foundational knowledge of CloudWatch, you'll be able to build your practical skills in monitoring and alerting various Amazon Web Services, such as EC2, EBS, RDS, ECS, EKS, DynamoDB, AWS Lambda, and ELB, with the help of real-world use cases. As you progress, you'll also learn how to use CloudWatch to detect anomalous behavior, set alarms, visualize logs and metrics, define automated actions, and rapidly troubleshoot issues. Finally, the book will take you through monitoring AWS billing and costs. By the end of this book, you'll be capable of making decisions that enhance your infrastructure performance and maintain it at its peak. What you will learnUnderstand the meaning and importance of monitoringExplore the components of a basic monitoring systemUnderstand the functions of CloudWatch Logs, metrics, and dashboardsDiscover how to collect different types of metrics from EC2Configure Amazon EventBridge to integrate with different AWS servicesGet up to speed with the fundamentals of observability and the AWS services used for observabilityFind out about the role Infrastructure As Code (IaC) plays in monitoringGain insights into how billing works using different CloudWatch featuresWho this book is for This book is for developers, DevOps engineers, site reliability engineers, or any IT individual with hands-on intermediate-level experience in networking, cloud computing, and infrastructure management. A beginner-level understanding of AWS and application monitoring will also be helpful to grasp the concepts covered in the book more effectively.
  aws data engineering tutorial: LLM Engineer's Handbook Paul Iusztin, Maxime Labonne, 2024-10-22 Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices Key Features Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications Book DescriptionArtificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems. Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects. By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.What you will learn Implement robust data pipelines and manage LLM training cycles Create your own LLM and refine it with the help of hands-on examples Get started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoring Perform supervised fine-tuning and LLM evaluation Deploy end-to-end LLM solutions using AWS and other tools Design scalable and modularLLM systems Learn about RAG applications by building a feature and inference pipeline Who this book is for This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios
  aws data engineering tutorial: Effective Amazon Machine Learning Alexis Perrier, 2017-04-25 Learn to leverage Amazon's powerful platform for your predictive analytics needs About This Book Create great machine learning models that combine the power of algorithms with interactive tools without worrying about the underlying complexity Learn the What's next? of machine learning—machine learning on the cloud—with this unique guide Create web services that allow you to perform affordable and fast machine learning on the cloud Who This Book Is For This book is intended for data scientists and managers of predictive analytics projects; it will teach beginner- to advanced-level machine learning practitioners how to leverage Amazon Machine Learning and complement their existing Data Science toolbox. No substantive prior knowledge of Machine Learning, Data Science, statistics, or coding is required. What You Will Learn Learn how to use the Amazon Machine Learning service from scratch for predictive analytics Gain hands-on experience of key Data Science concepts Solve classic regression and classification problems Run projects programmatically via the command line and the Python SDK Leverage the Amazon Web Service ecosystem to access extended data sources Implement streaming and advanced projects In Detail Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets. Style and approach This book will include use cases you can relate to. In a very practical manner, you will explore the various capabilities of Amazon Machine Learning services, allowing you to implementing them in your environment with consummate ease.
  aws data engineering tutorial: 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.
  aws data engineering tutorial: 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.
  aws data engineering tutorial: Data Engineering with AWS Gareth Eagar, 2023-10-31 Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered. Key Features Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Stay up to date with a comprehensive revised chapter on Data Governance Build modern data platforms with a new section covering transactional data lakes and data mesh Book DescriptionThis book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability. You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS. By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!What you will learn Seamlessly ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Load data into a Redshift data warehouse and run queries with ease Visualize and explore data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Build transactional data lakes using Apache Iceberg with Amazon Athena Learn how a data mesh approach can be implemented on AWS Who this book is forThis book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.
  aws data engineering tutorial: Cloud Computing Demystified for Aspiring Professionals David Santana, Amit Malik, 2023-03-24 Gain in-depth knowledge of cloud computing concepts and apply them to accelerate your career in any cloud engineering role Key FeaturesGet to grips with key cloud computing concepts, cloud service providers, and best practicesExplore demonstrations for cloud computing models using real-world examplesAdopt the self-paced learning strategy and get industry-ready for cloud engineering rolesPurchase of the print or Kindle book includes a free eBook in the PDF formatBook Description If you want to upskill yourself in cloud computing domains to thrive in the IT industry, then you've come to the right place. Cloud Computing Demystified for Aspiring Professionals helps you to master cloud computing essentials and important technologies offered by cloud service providers needed to succeed in a cloud-centric job role. This book begins with an overview of transformation from traditional to modern-day cloud computing infrastructure, and various types and models of cloud computing. You'll learn how to implement secure virtual networks, virtual machines, and data warehouse resources including data lake services used in big data analytics — as well as when to use SQL and NoSQL databases and how to build microservices using multi-cloud Kubernetes services across AWS, Microsoft Azure, and Google Cloud. You'll also get step-by-step demonstrations of infrastructure, platform, and software cloud services and optimization recommendations derived from certified industry experts using hands-on tutorials, self-assessment questions, and real-world case studies. By the end of this book, you'll be ready to successfully implement cloud computing standardized concepts, services, and best practices in your workplace. What you will learnGain insights into cloud computing essentials and public, private, hybrid, and multi-cloud deployment modelsExplore core cloud computing services such as IaaS, PaaS, and SaaSDiscover major public cloud providers such as AWS, Microsoft, and GoogleUnlock the power of IaaS, PaaS, and SaaS with AWS, Azure, and GCPCreate secure networks, containers, Kubernetes, compute, databases, and API services on cloudDevelop industry-based cloud solutions using real-world examplesGet recommendations on exam preparation for cloud accreditationsWho this book is for The book is for aspiring cloud engineers, as well as college graduates, IT enthusiasts, and beginner-level cloud practitioners looking to get into cloud computing or transforming their career and upskilling themselves in a cloud engineering role in any industry. A basic understanding of networking, database development, and data analysis concepts and experience in programming languages such as Python and C# will help you get the most out of this book.
  aws data engineering tutorial: Cryptology and Network Security with Machine Learning Atul Chaturvedi,
  aws data engineering tutorial: The Data Science Framework Juan J. Cuadrado-Gallego, Yuri Demchenko, 2020-10-01 This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines.
  aws data engineering tutorial: Privacy and Security Policies in Big Data Tamane, Sharvari, Solanki, Vijender Kumar, Dey, Nilanjan, 2017-03-03 In recent years, technological advances have led to significant developments within a variety of business applications. In particular, data-driven research provides ample opportunity for enterprise growth, if utilized efficiently. Privacy and Security Policies in Big Data is a pivotal reference source for the latest research on innovative concepts on the management of security and privacy analytics within big data. Featuring extensive coverage on relevant areas such as kinetic knowledge, cognitive analytics, and parallel computing, this publication is an ideal resource for professionals, researchers, academicians, advanced-level students, and technology developers in the field of big data.
  aws data engineering tutorial: NoSQL Ganesh Chandra Deka, 2017-05-19 This book discusses the advanced databases for the cloud-based application known as NoSQL. It will explore the recent advancements in NoSQL database technology. Chapters on structured, unstructured and hybrid databases will be included to explore bigdata analytics, bigdata storage and processing. The book is likely to cover a wide range of topics such as cloud computing, social computing, bigdata and advanced databases processing techniques.
  aws data engineering tutorial: Practical MLOps Noah Gift, Alfredo Deza, 2021-09-14 Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
  aws data engineering tutorial: Multidisciplinary Computational Intelligence Techniques: Applications in Business, Engineering, and Medicine Ali, Shawkat, 2012-06-30 This book explores the complex world of computational intelligence, which utilizes computational methodologies such as fuzzy logic systems, neural networks, and evolutionary computation for the purpose of managing and using data effectively to address complicated real-world problems--
  aws data engineering tutorial: AWS Certified DevOps Engineer - Professional Certification and Beyond Adam Book, 2021-11-25 Explore the ins and outs of becoming an AWS certified DevOps professional engineer with the help of easy-to-follow practical examples and detailed explanations Key FeaturesDiscover how to implement and manage continuous delivery systems and methodologies on AWSExplore real-world scenarios and hands-on examples that will prepare you to take the DOP-C01 exam with confidenceLearn from enterprise DevOps scenarios to prepare fully for the AWS certification examBook Description The AWS Certified DevOps Engineer certification is one of the highest AWS credentials, vastly recognized in cloud computing or software development industries. This book is an extensive guide to helping you strengthen your DevOps skills as you work with your AWS workloads on a day-to-day basis. You'll begin by learning how to create and deploy a workload using the AWS code suite of tools, and then move on to adding monitoring and fault tolerance to your workload. You'll explore enterprise scenarios that'll help you to understand various AWS tools and services. This book is packed with detailed explanations of essential concepts to help you get to grips with the domains needed to pass the DevOps professional exam. As you advance, you'll delve into AWS with the help of hands-on examples and practice questions to gain a holistic understanding of the services covered in the AWS DevOps professional exam. Throughout the book, you'll find real-world scenarios that you can easily incorporate in your daily activities when working with AWS, making you a valuable asset for any organization. By the end of this AWS certification book, you'll have gained the knowledge needed to pass the AWS Certified DevOps Engineer exam, and be able to implement different techniques for delivering each service in real-world scenarios. What you will learnAutomate your pipelines, build phases, and deployments with AWS-native toolingDiscover how to implement logging and monitoring using AWS-native toolingGain a solid understanding of the services included in the AWS DevOps Professional examReinforce security practices on the AWS platform from an exam point of viewFind out how to automatically enforce standards and policies in AWS environmentsExplore AWS best practices and anti-patternsEnhance your core AWS skills with the help of exercises and practice testsWho this book is for This book is for AWS developers and SysOps administrators looking to advance their careers by achieving the highly sought-after DevOps Professional certification. Basic knowledge of AWS as well as its core services (EC2, S3, and RDS) is needed. Familiarity with DevOps concepts such as source control, monitoring, and logging, not necessarily in the AWS context, will be helpful.
  aws data engineering tutorial: Handbuch Data Engineering Joe Reis, Matt Housley, 2023-08-01 Der praxisnahe Überblick über die gesamte Data-Engineering-Landschaft Das Buch vermittelt grundlegende Konzepte des Data Engineering und beschreibt Best Practices für jede Phase des Datenlebenszyklus Mit dem Data-Engineering-Lifecycle bietet es einen konzeptionellen Rahmen, der langfristig Gültigkeit haben wird Es unterstützt Sie - jenseits des Hypes - bei der Auswahl der richtigen Datentechnologien, Architekturen und Prozesse und verfolgt den Cloud-First-Ansatz Data Engineering hat sich in den letzten zehn Jahren rasant weiterentwickelt, so dass viele Softwareentwickler, Data Scientists und Analysten nach einer zusammenfassenden Darstellung grundlegender Techniken suchen. Dieses praxisorientierte Buch bietet einen umfassenden Überblick über das Data Engineering und gibt Ihnen mit dem Data-Engineering-Lifecycle ein Framework an die Hand, das die Evaluierung und Auswahl der besten Technologien für reale Geschäftsprobleme erleichtert. Sie erfahren, wie Sie Systeme so planen und entwickeln, dass sie den Anforderungen Ihres Unternehmens und Ihrer Kunden optimal gerecht werden. Die Autoren Joe Reis und Matt Housley führen Sie durch den Data-Engineering-Lebenszyklus und zeigen Ihnen, wie Sie eine Vielzahl von Cloud-Technologien kombinieren können, um die Bedürfnisse von Datenkonsumenten zu erfüllen. Sie lernen, die Konzepte der Datengenerierung, -aufnahme, -orchestrierung, -transformation, -speicherung und -verwaltung anzuwenden, die in jeder Datenumgebung unabhängig von der verwendeten Technologie von entscheidender Bedeutung sind. Darüber hinaus erfahren Sie, wie Sie Data Governance und Sicherheit in den gesamten Datenlebenszyklus integrieren.
  aws data engineering tutorial: CLOUD COMPUTING ARCHITECTURE (DESIGN, IMPLEMENTATION, AND SECURITY STRATEGIES) ASHISH KUMAR SHYAMAKRISHNA SIDDHARTH CHAMARTHY RAMYA RAMACHANDRAN RAGHAV AGARWAL, 2024-10-24 In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Cloud Computing Architecture: Design, Implementation, and Security Strategies, is conceived to bridge the gap between emerging technological advancements in cloud computing and their strategic application in modern IT management. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of cloud technologies, particularly in terms of architecture, implementation, and security. From foundational theories to advanced applications, we delve into the critical aspects that drive successful cloud-based solutions in enterprise environments. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, IT managers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world cloud computing scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from technological development and cloud architecture design to the strategic management of security in cloud-based systems. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting innovative ideas and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that cloud computing architecture and security strategies play in shaping the future of digital businesses. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how cloud computing technologies and management practices can be harnessed together to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating secure, scalable cloud solutions that will define the future of enterprise IT. Thank you for joining us on this journey. Authors
  aws data engineering tutorial: Redux Quick Start Guide James Lee, Tao Wei, Suresh Kumar Mukhiya, 2019-02-28 Integrate Redux with React and other front-end JavaScript frameworks efficiently and manage application states effectively Key FeaturesGet better at building web applications with state management using ReduxLearn the fundamentals of Redux to structure your app more efficientlyThis guide will teach you develop complex apps that would be easier to maintainBook Description Starting with a detailed overview of Redux, we will follow the test-driven development (TDD) approach to develop single-page applications. We will set up JEST for testing and use JEST to test React, Redux, Redux-Sage, Reducers, and other components. We will then add important middleware and set up immutableJS in our application. We will use common data structures such as Map, List, Set, and OrderedList from the immutableJS framework. We will then add user interfaces using ReactJS, Redux-Form, and Ant Design. We will explore the use of react-router-dom and its functions. We will create a list of routes that we will need in order to create our application, and explore routing on the server site and create the required routes for our application. We will then debug our application and integrate Redux Dev tools. We will then set up our API server and create the API required for our application. We will dive into a modern approach to structuring our server site components in terms of Model, Controller, Helper functions, and utilities functions. We will explore the use of NodeJS with Express to build the REST API components. Finally, we will venture into the possibilities of extending the application for further research, including deployment and optimization. What you will learnFollow the test-driven development (TDD) approach to develop a single-page applicationAdd important middleware, such as Redux store middleware, redux-saga middleware, and language middleware, to your applicationUnderstand how to use immutableJS in your applicationBuild interactive components using ReactJSConfigure react-router-redux and explore the differences between react-router-dom and react-router-reduxUse Redux Dev tools to debug your applicationSet up our API server and create the API required for our applicationWho this book is for This book is meant for JavaScript developers interesting in learning state management and building easy to maintain web applications.
  aws data engineering tutorial: Innovative Mobile and Internet Services in Ubiquitous Computing Leonard Barolli,
  aws data engineering tutorial: SolidWorks 2013 Tutorial David C. Planchard, Marie P. Planchard, 2013 SolidWorks 2013 Tutorial with Video Instruction is targeted towards a technical school, two year college, four year university or industry professional that is a beginner or intermediate CAD user. The text provides a student who is looking for a step-by-step project based approach to learning SolidWorks with an enclosed 1.5 hour video instruction DVD, SolidWorks model files, and preparation for the CSWA exam. The book is divided into two sections. Chapters 1 - 7 explore the SolidWorks User Interface and CommandManager, Document and System properties, simple machine parts, simple and complex assemblies, design tables, configurations, multi-sheet, multi-view drawings, BOMs, Revision tables using basic and advanced features along with Intelligent Modeling Techniques, SustainabilityXpress, SimulationXpress and DFMXpress. Chapters 8 - 11 prepare you for the new Certified SolidWorks Associate Exam (CSWA). The CSWA certification indicates a foundation in and apprentice knowledge of 3D CAD and engineering practices and principles. Follow the step-by-step instructions and develop multiple assemblies that combine over 100 extruded machined parts and components. Formulate the skills to create, modify and edit sketches and solid features. Learn the techniques to reuse features, parts and assemblies through symmetry, patterns, copied components, design tables and configurations. Learn by doing, not just by reading! Desired outcomes and usage competencies are listed for each chapter. Know your objective up front. Follow the steps in each chapter to achieve your design goals. Work between multiple documents, features, commands, custom properties and document properties that represent how engineers and designers utilize SolidWorks in industry.
  aws data engineering tutorial: SolidWorks 2010 Tutorial David C. Planchard, Marie P. Planchard, 2010 Provides an introduction to SolidWorks 2010 through step-by-step tutorials that cover such topics as linkage assembly, front support assembly, the fundamentals of drawing, and pneumatic test module assembly.
  aws data engineering tutorial: Ace AWS Certified Solutions Architect Associate Exam (2024 Edition) Etienne Noumen, Unlock unparalleled technical depth with this book, expertly integrating the proven methodologies of Tutorials Dojo, the insights of Adrian Cantrill, and the hands-on approach of AWS Skills Builder. Unlock success with 'Ace the AWS Solutions Architect Associates SAA-C03 Certification Exam' by Etienne Noumen. With over 20 years in Software Engineering and a deep 5-year dive into AWS Cloud, Noumen delivers an unmatched guide packed with Quizzes, Flashcards, Practice Exams, and invaluable CheatSheets. Learn firsthand from testimonials of triumphs and recoveries, and master the exam with exclusive tips and tricks. This comprehensive roadmap is your ultimate ticket to acing the SAA-C03 exam! Become stronger in your current role or prepare to step into a new one by continuing to build the cloud solutions architecture skills companies are begging for right now. Demand for cloud solutions architect proficiency is only set to increase, so you can expect to see enormous ROI on any cloud learning efforts you embark on. What will you learn in this book? Design Secure Architectures Design Resilient Architectures Design High-Performing Architectures Design Cost-Optimized Architectures What are the requirements or prerequisites for reading this book? The target candidate should have at least 1 year of hands-on experience designing cloud solutions that use AWS services Who is this book for? IT Professionals, Solutions Architect, Cloud enthusiasts, Computer Science and Engineering Students, AWS Cloud Developer, Technology Manager and Executives, IT Project Managers What is taught in this book? AWS Certification Preparation for Solutions Architecture – Associate Level Keywords: AWS Solutions Architect SAA-C03 Certification Etienne Noumen AWS Cloud expertise Practice Exams AWS Flashcards AWS CheatSheets Testimonials Exam preparation AWS exam tips Cloud Engineering Certification guide AWS study guide Solutions Architect Associates Exam success strategies The book contains several testimonials like the one below: Successfully cleared the AWS Solutions Architect Associate SAA-C03 with a score of 824, surpassing my expectations. The exam presented a mix of question difficulties, with prominent topics being Kinesis, Lakeformation, Big Data tools, and S3. Given the declining cybersecurity job market in Europe post-2021, I'm contemplating a transition to cloud engineering. For preparation, I leveraged Stephane Mareek's course, Tutorial dojo's practice tests, and flashcards. My manager also shared his AWS skill builder account. Post evaluation, I found Mareek's practice tests to be outdated and more challenging than required, with his course delving too deeply into some areas. In contrast, Tutorial dojo's materials were simpler. My scores ranged from 65% on Mareek's tests to 75-80% on Tutorial dojo, with a 740 on the official AWS practice test. Sharing this for those on a similar journey. Sample Questions and Detailed Answers included: Latest AWS SAA Practice Exam - Question 1: A web application hosted on AWS uses an EC2 instance to serve content and an RDS MySQL instance for database needs. During a performance audit, you notice frequent read operations are causing performance bottlenecks. To optimize the read performance, which of the following strategies should you implement? (Select TWO.) A. Deploy an ElastiCache cluster to cache common queries and reduce the load on the RDS instance. B. Convert the RDS instance to a Multi-AZ deployment for improved read performance. C. Use RDS Read Replicas to offload read requests from the primary RDS instance. D. Increase the instance size of the RDS database to a larger instance type with more CPU and RAM. E. Implement Amazon Redshift to replace RDS for improved read and write operation performance. Correct Answer: A. Deploy an ElastiCache cluster to cache common queries and reduce the load on the RDS instance. C. Use RDS Read Replicas to offload read requests from the primary RDS instance. Explanation: Amazon RDS Read Replicas provide a way to scale out beyond the capacity of a single database deployment for read-heavy database workloads. You can create one or more replicas of a source DB Instance and serve high-volume application read traffic from multiple copies of your data, thereby increasing aggregate read throughput. Reference: Amazon RDS Read Replicas Latest AWS SAA Practice Exam - Question 2: Secure RDS Access with IAM Authentication A financial application suite leverages an ensemble of EC2 instances, an Application Load Balancer, and an RDS instance poised in a Multi-AZ deployment. The security requisites dictate that the RDS database be exclusively accessible to authenticated EC2 instances, preserving the confidentiality of customer data. The Architect must choose a security mechanism that aligns with AWS best practices and ensures stringent access control. What should the Architect implement to satisfy these security imperatives? Enable IAM Database Authentication for the RDS instance. Implement SSL encryption to secure the database connections. Assign a specific IAM Role to the EC2 instances granting RDS access. Utilize IAM combined with STS for restricted RDS access with a temporary credentialing system. Correct Answer: A. Enable IAM Database Authentication for the RDS instance. Here's the detailed explanation and reference link for the answer provided: Enable IAM Database Authentication for the RDS instance. IAM database authentication is used to control who can connect to your Amazon RDS database instances. When IAM database authentication is enabled, you don’t need to use a password to connect to a DB instance. Instead, you use an authentication token issued by AWS Security Token Service (STS). IAM database authentication works with MySQL and PostgreSQL. It provides enhanced security because the authentication tokens are time-bound and encrypted. Moreover, this method integrates the database access with the centralized IAM service, simplifying user management and access control. By using IAM Database Authentication, you satisfy the security requirements by ensuring that only authenticated EC2 instances (or more precisely, the applications running on them that assume an IAM role with the necessary permissions) can access the RDS database. This method also preserves the confidentiality of customer data by leveraging AWS’s robust identity and access management system. Reference: IAM Database Authentication for MySQL and PostgreSQL The other options provided are valuable security mechanisms but do not fulfill the requirements as directly or effectively as IAM Database Authentication for the given scenario: Implement SSL encryption to secure the database connections. While SSL (Secure Socket Layer) encryption secures the data in transit between the EC2 instances and the RDS instance, it does not provide an access control mechanism on its own. SSL encryption should be used in conjunction with IAM database authentication for a comprehensive security approach. Assign a specific IAM Role to the EC2 instances granting RDS access. Assigning an IAM role to EC2 instances to grant them access to RDS is a good practice and is required for the EC2 instances to use IAM Database Authentication. However, it is not the complete answer to the question of which security mechanism to implement. Utilize IAM combined with STS for restricted RDS access with a temporary credentialing system. AWS Security Token Service (STS) is indeed used when implementing IAM Database Authentication, as it provides the temporary credentials (authentication tokens) for database access. While the use of STS is inherent to the process of IAM Database Authentication, the answer needed to specify the enabling of IAM Database Authentication as the method to meet the security requirements. Latest AWS SAA Practice Exam - Question 3: A microservice application is being hosted in the ap-southeast-1 and ap-northeast-1 regions. The ap-southeast-1 region accounts for 80% of traffic, with the rest from ap-northeast-1. As part of the company’s business continuity plan, all traffic must be rerouted to the other region if one of the regions’ servers fails. Which solution can comply with the requirement? A. Set up an 80/20 weighted routing in the application load balancer and enable health checks. B. Set up an 80/20 weighted routing in the network load balancer and enable health checks. C. Set up an 80/20 weighted routing policy in AWS Route 53 and enable health checks. D. Set up a failover routing policy in AWS Route 53 and enable health checks. Correct Answer: C. Establish an 80/20 weighted routing policy in AWS Route 53 and incorporate health checks. Explanation: The correct solution for this scenario is to use AWS Route 53's weighted routing policy with health checks. This setup allows the distribution of traffic across multiple AWS regions based on assigned weights (in this case, 80% to ap-southeast-1 and 20% to ap-northeast-1) and automatically reroutes traffic if one region becomes unavailable due to server failure. Option C is correct because AWS Route 53’s weighted routing policy allows you to assign weights to resource record sets (RRS) which correspond to different AWS regions. When combined with health checks, Route 53 can monitor the health of the application in each region. If a region becomes unhealthy, Route 53 will reroute traffic to the healthy region based on the configured weights. Option A and B are incorrect because application and network load balancers operate at the regional level, not the global level. Therefore, they cannot reroute traffic between regions. Option D, while involving Route 53, suggests a failover routing policy, which is not suitable for distributing traffic with a specific percentage split across regions. Failover routing is typically used for active-passive failover, not for load distribution, which doesn't align with the requirement to handle traffic in an 80/20 proportion. The weighted routing policy of AWS Route 53, with appropriate health checks, satisfies the business requirement by distributing traffic in the specified ratio and ensuring business continuity by redirecting traffic in the event of a regional failure. Reference: https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html Get the Print version of the Book at Amazon at https://amzn.to/40ycS4c (Use Discount code Djamgatech2024 for 50% OFF)
  aws data engineering tutorial: Cloud Computing Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, 2010-12-17 The primary purpose of this book is to capture the state-of-the-art in Cloud Computing technologies and applications. The book will also aim to identify potential research directions and technologies that will facilitate creation a global market-place of cloud computing services supporting scientific, industrial, business, and consumer applications. We expect the book to serve as a reference for larger audience such as systems architects, practitioners, developers, new researchers and graduate level students. This area of research is relatively recent, and as such has no existing reference book that addresses it. This book will be a timely contribution to a field that is gaining considerable research interest, momentum, and is expected to be of increasing interest to commercial developers. The book is targeted for professional computer science developers and graduate students especially at Masters level. As Cloud Computing is recognized as one of the top five emerging technologies that will have a major impact on the quality of science and society over the next 20 years, its knowledge will help position our readers at the forefront of the field.
  aws data engineering tutorial: Serverless ETL and Analytics with AWS Glue Vishal Pathak, Subramanya Vajiraya, Noritaka Sekiyama, Tomohiro Tanaka, Albert Quiroga, Ishan Gaur, 2022-08-30 Build efficient data lakes that can scale to virtually unlimited size using AWS Glue Key Features Book DescriptionOrganizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide serverless Spark, enabling you to create and manage data lakes in a serverless fashion. This guide shows you how AWS Glue can be used to solve real-world problems along with helping you learn about data processing, data integration, and building data lakes. Beginning with AWS Glue basics, this book teaches you how to perform various aspects of data analysis such as ad hoc queries, data visualization, and real-time analysis using this service. It also provides a walk-through of CI/CD for AWS Glue and how to shift left on quality using automated regression tests. You’ll find out how data security aspects such as access control, encryption, auditing, and networking are implemented, as well as getting to grips with useful techniques such as picking the right file format, compression, partitioning, and bucketing. As you advance, you’ll discover AWS Glue features such as crawlers, Lake Formation, governed tables, lineage, DataBrew, Glue Studio, and custom connectors. The concluding chapters help you to understand various performance tuning, troubleshooting, and monitoring options. By the end of this AWS book, you’ll be able to create, manage, troubleshoot, and deploy ETL pipelines using AWS Glue.What you will learn Apply various AWS Glue features to manage and create data lakes Use Glue DataBrew and Glue Studio for data preparation Optimize data layout in cloud storage to accelerate analytics workloads Manage metadata including database, table, and schema definitions Secure your data during access control, encryption, auditing, and networking Monitor AWS Glue jobs to detect delays and loss of data Integrate Spark ML and SageMaker with AWS Glue to create machine learning models Who this book is for ETL developers, data engineers, and data analysts
  aws data engineering tutorial: MATLAB Kelly Bennett, 2014-09-08 MATLAB is an indispensable asset for scientists, researchers, and engineers. The richness of the MATLAB computational environment combined with an integrated development environment (IDE) and straightforward interface, toolkits, and simulation and modeling capabilities, creates a research and development tool that has no equal. From quick code prototyping to full blown deployable applications, MATLAB stands as a de facto development language and environment serving the technical needs of a wide range of users. As a collection of diverse applications, each book chapter presents a novel application and use of MATLAB for a specific result.
  aws data engineering tutorial: 97 Things Every Cloud Engineer Should Know Emily Freeman, Nathen Harvey, 2020-12-04 If you create, manage, operate, or configure systems running in the cloud, you're a cloud engineer--even if you work as a system administrator, software developer, data scientist, or site reliability engineer. With this book, professionals from around the world provide valuable insight into today's cloud engineering role. These concise articles explore the entire cloud computing experience, including fundamentals, architecture, and migration. You'll delve into security and compliance, operations and reliability, and software development. And examine networking, organizational culture, and more. You're sure to find 1, 2, or 97 things that inspire you to dig deeper and expand your own career. Three Keys to Making the Right Multicloud Decisions, Brendan O'Leary Serverless Bad Practices, Manases Jesus Galindo Bello Failing a Cloud Migration, Lee Atchison Treat Your Cloud Environment as If It Were On Premises, Iyana Garry What Is Toil, and Why Are SREs Obsessed with It?, Zachary Nickens Lean QA: The QA Evolving in the DevOps World, Theresa Neate How Economies of Scale Work in the Cloud, Jon Moore The Cloud Is Not About the Cloud, Ken Corless Data Gravity: The Importance of Data Management in the Cloud, Geoff Hughes Even in the Cloud, the Network Is the Foundation, David Murray Cloud Engineering Is About Culture, Not Containers, Holly Cummins
  aws data engineering tutorial: Feature Store for Machine Learning Jayanth Kumar M J, 2022-06-30 Learn how to leverage feature stores to make the most of your machine learning models Key Features • Understand the significance of feature stores in the ML life cycle • Discover how features can be shared, discovered, and re-used • Learn to make features available for online models during inference Book Description Feature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed and curated features for ML models. This makes them available for model training, inference (batch and online), and reuse in other ML pipelines. Knowing how to utilize feature stores to their fullest potential can save you a lot of time and effort, and this book will teach you everything you need to know to get started. Feature Store for Machine Learning is for data scientists who want to learn how to use feature stores to share and reuse each other's work and expertise. You'll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model. While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work. With a hands-on approach to implementation and associated methodologies, you'll get up and running in no time. By the end of this book, you'll have understood why feature stores are essential and how to use them in your ML projects, both on your local system and on the cloud. What you will learn • Understand the significance of feature stores in a machine learning pipeline • Become well-versed with how to curate, store, share and discover features using feature stores • Explore the different components and capabilities of a feature store • Discover how to use feature stores with batch and online models • Accelerate your model life cycle and reduce costs • Deploy your first feature store for production use cases Who this book is for If you have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using them, then this book is for you. Data/machine learning engineers and data scientists who build machine learning models for production systems in any domain, those supporting data engineers in productionizing ML models, and platform engineers who build data science (ML) platforms for the organization will also find plenty of practical advice in the later chapters of this book.
  aws data engineering tutorial: Combining DataOps, MLOps and DevOps Dr. Kalpesh Parikh, Amit Johri, 2022-05-16 Accelerate the delivery of software, data, and machine learning KEY FEATURES ● Each chapter harmonizes the DevOps, Data Engineering, and Optimized Machine Learning cultures. ● Equips readers with AGILE skills to continuously re-prioritize production backlogs. ● Containerization, Docker, Kubernetes, DataOps, and MLOps are all rolled together. DESCRIPTION This book instructs readers on how to operationalize the creation of systems, software applications, and business information using the best practices of DevOps, DataOps, and MLOps, among other things. From software unit packaging code and its dependencies to automating the software development lifecycle and deployment, the book provides a learning roadmap that begins with the basics and progresses to advanced topics. This book teaches you how to create a culture of cooperation, affinity, and tooling at scale using DevOps, Docker, Kubernetes, Data Engineering, and Machine Learning. Microservices design, setting up clusters and maintaining them, processing data pipelines, and automating operations with machine learning are all topics that will aid you in your career. When you use each of the xOps methods described in the book, you will notice a clear shift in your understanding of system development. Throughout the book, you will see how every stage of software development is modernized with the most up-to-date technologies and the most effective project management approaches. WHAT YOU WILL LEARN ● Learn about the Packaging code and all its dependencies in a container. ● Utilize DevOps to automate every stage of software development. ● Learn how to create Microservices that are focused on a specific issue. ● Utilize Kubernetes to containerize applications in a variety of settings. ● Using DataOps, you can align people, processes, and technology. WHO THIS BOOK IS FOR This book is meant for the Software Engineering team, Data Professionals, IT Operations and Application Development Team with prior knowledge in software development. TABLE OF CONTENTS 1. Container – Containerization is the New Virtualization 2. Docker with Containers for Developing and Deploying Software 3. DevOps to Build at Scale a Culture of Collaboration, Affinity, and Tooling 4. Docker Containers for Microservices Architecture Design 5. Kubernetes – The Cluster Manager for Container 6. Data Engineering with DataOps 7. MLOps: Engineering Machine Learning Operations 8. xOps Best Practices
  aws data engineering tutorial: Effective Data Science Infrastructure Ville Tuulos, 2022-08-30 Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. About the technology Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the book Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's inside Handle compute and orchestration in the cloud Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem Architect complex applications that require large datasets and models, and a team of data scientists About the reader For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the author At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Table of Contents 1 Introducing data science infrastructure 2 The toolchain of data science 3 Introducing Metaflow 4 Scaling with the compute layer 5 Practicing scalability and performance 6 Going to production 7 Processing data 8 Using and operating models 9 Machine learning with the full stack
  aws data engineering tutorial: Applied Strength of Materials Robert Mott, Joseph A. Untener, 2016-11-17 Designed for a first course in strength of materials, Applied Strength of Materials has long been the bestseller for Engineering Technology programs because of its comprehensive coverage, and its emphasis on sound fundamentals, applications, and problem-solving techniques. The combination of clear and consistent problem-solving techniques, numerous end-of-chapter problems, and the integration of both analysis and design approaches to strength of materials principles prepares students for subsequent courses and professional practice. The fully updated Sixth Edition. Built around an educational philosophy that stresses active learning, consistent reinforcement of key concepts, and a strong visual component, Applied Strength of Materials, Sixth Edition continues to offer the readers the most thorough and understandable approach to mechanics of materials.
  aws data engineering tutorial: SolidWorks 2012 Tutorial David C. Planchard, Marie P. Planchard, 2012 SolidWorks 2012 Tutorial with Video Instruction is target towards a technical school, two year college, four year university or industry professional that is a beginner or intermediate CAD user. The text provides a student who is looking for a step-by-step project based approach to learning SolidWorks with an enclosed 1.5 hour video instruction DVD, SolidWorks model files, and preparation for the CSWA exam. The book is divided into two sections. Chapters 1 - 7 explore the SolidWorks User Interface and CommandManager, Document and System properties, simple machine parts, simple and complex assemblies, design tables, configurations, multi-sheet, multi-view drawings, BOMs, Revision tables using basic and advanced features along with Intelligent Modeling Techniques, SustainabilityXpress, SimulationXpress and DFMXpress. Chapters 8 - 11 prepare you for the new Certified SolidWorks Associate Exam (CSWA). The CSWA certification indicates a foundation in and apprentice knowledge of 3D CAD and engineering practices and principles. Follow the step-by-step instructions and develop multiple assemblies that combine over 100 extruded machined parts and components. Formulate the skills to create, modify and edit sketches and solid features. Learn the techniques to reuse features, parts and assemblies through symmetry, patterns, copied components, design tables and configurations. Learn by doing, not just by reading! Desired outcomes and usage competencies are listed for each chapter. Know your objective up front. Follow the steps in each chapter to achieve your design goals. Work between multiple documents, features, commands, custom properties and document properties that represent how engineers and designers utilize SolidWorks in industry.
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