Aws Data Engineering Course

  aws data engineering course: 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 course: 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 course: 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 course: 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 course: 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 course: 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 course: Data Engineering with Alteryx Paul Houghton, 2022-06-30 Build and deploy data pipelines with Alteryx by applying practical DataOps principles Key Features • Learn DataOps principles to build data pipelines with Alteryx • Build robust data pipelines with Alteryx Designer • Use Alteryx Server and Alteryx Connect to share and deploy your data pipelines Book Description Alteryx is a GUI-based development platform for data analytic applications. Data Engineering with Alteryx will help you leverage Alteryx's code-free aspects which increase development speed while still enabling you to make the most of the code-based skills you have. This book will teach you the principles of DataOps and how they can be used with the Alteryx software stack. You'll build data pipelines with Alteryx Designer and incorporate the error handling and data validation needed for reliable datasets. Next, you'll take the data pipeline from raw data, transform it into a robust dataset, and publish it to Alteryx Server following a continuous integration process. By the end of this Alteryx book, you'll be able to build systems for validating datasets, monitoring workflow performance, managing access, and promoting the use of your data sources. What you will learn • Build a working pipeline to integrate an external data source • Develop monitoring processes for the pipeline example • Understand and apply DataOps principles to an Alteryx data pipeline • Gain skills for data engineering with the Alteryx software stack • Work with spatial analytics and machine learning techniques in an Alteryx workflow Explore Alteryx workflow deployment strategies using metadata validation and continuous integration • Organize content on Alteryx Server and secure user access Who this book is for If you're a data engineer, data scientist, or data analyst who wants to set up a reliable process for developing data pipelines using Alteryx, this book is for you. You'll also find this book useful if you are trying to make the development and deployment of datasets more robust by following the DataOps principles. Familiarity with Alteryx products will be helpful but is not necessary.
  aws data engineering course: Data Engineering for Machine Learning Pipelines Pavan Kumar Narayanan,
  aws data engineering course: 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.
  aws data engineering course: Financial Data Engineering Tamer Khraisha, 2024-10-09 Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical and comprehensive understanding of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer developing a data infrastructure for a financial product possesses not only technical data engineering skills but also a solid understanding of financial domain-specific challenges, methodologies, data ecosystems, providers, formats, technological constraints, identifiers, entities, standards, regulatory requirements, and governance. This book offers a comprehensive, practical, domain-driven approach to financial data engineering, featuring real-world use cases, industry practices, and hands-on projects. You'll learn: The data engineering landscape in the financial sector Specific problems encountered in financial data engineering The structure, players, and particularities of the financial data domain Approaches to designing financial data identification and entity systems Financial data governance frameworks, concepts, and best practices The financial data engineering lifecycle from ingestion to production The varieties and main characteristics of financial data workflows How to build financial data pipelines using open source tools and APIs Tamer Khraisha, PhD, is a senior data engineer and scientific author with more than a decade of experience in the financial sector.
  aws data engineering course: 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 course: Data Science for Decision Makers Jon Howells, 2024-07-26 Bridge the gap between business and data science by learning how to interpret machine learning and AI models, manage data teams, and achieve impactful results Key Features Master the concepts of statistics and ML to interpret models and guide decisions Identify valuable AI use cases and manage data science projects from start to finish Empower top data science teams to solve complex problems and build AI products Purchase of the print Kindle book includes a free PDF eBook Book DescriptionAs data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI. This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently. By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements. By the end of this book, you’ll be able to characterize the data within your organization and frame business problems as data science problems.What you will learn Discover how to interpret common statistical quantities and make data-driven decisions Explore ML concepts as well as techniques in supervised, unsupervised, and reinforcement learning Find out how to evaluate statistical and machine learning models Understand the data science lifecycle, from development to monitoring of models in production Know when to use ML, statistical modeling, or traditional BI methods Manage data teams and data science projects effectively Who this book is for This book is designed for executives who want to understand and apply data science methods to enhance decision-making. It is also for individuals who work with or manage data scientists and machine learning engineers, such as chief data officers (CDOs), data science managers, and technical project managers.
  aws data engineering course: Modern Data Architecture on AWS Behram Irani, 2023-08-31 Discover all the essential design and architectural patterns in one place to help you rapidly build and deploy your modern data platform using AWS services Key Features Learn to build modern data platforms on AWS using data lakes and purpose-built data services Uncover methods of applying security and governance across your data platform built on AWS Find out how to operationalize and optimize your data platform on AWS Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.What you will learn Familiarize yourself with the building blocks of modern data architecture on AWS Discover how to create an end-to-end data platform on AWS Design data architectures for your own use cases using AWS services Ingest data from disparate sources into target data stores on AWS Build data pipelines, data sharing mechanisms, and data consumption patterns using AWS services Find out how to implement data governance using AWS services Who this book is for This book is for data architects, data engineers, and professionals creating data platforms. The book's use case–driven approach helps you conceptualize possible solutions to specific use cases, while also providing you with design patterns to build data platforms for any organization. It's beneficial for technical leaders and decision makers to understand their organization's data architecture and how each platform component serves business needs. A basic understanding of data & analytics architectures and systems is desirable along with beginner’s level understanding of AWS Cloud.
  aws data engineering course: AWS certification guide - AWS Certified Machine Learning - Specialty , AWS Certification Guide - AWS Certified Machine Learning – Specialty Unleash the Potential of AWS Machine Learning Embark on a comprehensive journey into the world of machine learning on AWS with this essential guide, tailored for those pursuing the AWS Certified Machine Learning – Specialty certification. This book is a valuable resource for professionals seeking to harness the power of AWS for machine learning applications. Inside, You'll Explore: Foundational to Advanced ML Concepts: Understand the breadth of AWS machine learning services and tools, from SageMaker to DeepLens, and learn how to apply them in various scenarios. Practical Machine Learning Scenarios: Delve into real-world examples and case studies, illustrating the practical applications of AWS machine learning technologies in different industries. Targeted Exam Preparation: Navigate the certification exam with confidence, thanks to detailed insights into the exam format, including specific chapters aligned with the certification objectives and comprehensive practice questions. Latest Trends and Best Practices: Stay at the forefront of machine learning advancements with up-to-date coverage of the latest AWS features and industry best practices. Written by a Machine Learning Expert Authored by an experienced practitioner in AWS machine learning, this guide combines in-depth knowledge with practical insights, providing a rich and comprehensive learning experience. Your Comprehensive Resource for ML Certification Whether you are deepening your existing machine learning skills or embarking on a new specialty in AWS, this book is your definitive companion, offering an in-depth exploration of AWS machine learning services and preparing you for the Specialty certification exam. Advance Your Machine Learning Career Beyond preparing for the exam, this guide is about mastering the complexities of AWS machine learning. It's a pathway to developing expertise that can be applied in innovative and transformative ways across various sectors. Start Your Specialized Journey in AWS Machine Learning Set off on your path to becoming an AWS Certified Machine Learning specialist. This guide is your first step towards mastering AWS machine learning and unlocking new opportunities in this exciting and rapidly evolving field. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
  aws data engineering course: Data Quality Fundamentals Barr Moses, Lior Gavish, Molly Vorwerck, 2022-09-01 Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you. Many data engineering teams today face the good pipelines, bad data problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelines Write scripts to make data checks and identify broken pipelines with data observability Learn how to set and maintain data SLAs, SLIs, and SLOs Develop and lead data quality initiatives at your company Learn how to treat data services and systems with the diligence of production software Automate data lineage graphs across your data ecosystem Build anomaly detectors for your critical data assets
  aws data engineering course: Data Engineering Best Practices Richard J. Schiller, David Larochelle, 2024-10-11 Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.
  aws data engineering course: Architecting Data and Machine Learning Platforms Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner, 2023-10-12 All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach
  aws data engineering course: 97 Things Every Data Engineer Should Know Tobias Macey, 2021-06-11 Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail
  aws data engineering course: Data Engineering with Scala and Spark Eric Tome, Rupam Bhattacharjee, David Radford, 2024-01-31 Take your data engineering skills to the next level by learning how to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data Key Features Transform data into a clean and trusted source of information for your organization using Scala Build streaming and batch-processing pipelines with step-by-step explanations Implement and orchestrate your pipelines by following CI/CD best practices and test-driven development (TDD) Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount. This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You’ll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You’ll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.What you will learn Set up your development environment to build pipelines in Scala Get to grips with polymorphic functions, type parameterization, and Scala implicits Use Spark DataFrames, Datasets, and Spark SQL with Scala Read and write data to object stores Profile and clean your data using Deequ Performance tune your data pipelines using Scala Who this book is for This book is for data engineers who have experience in working with data and want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies.
  aws data engineering course: Machine Learning Engineering on AWS Joshua Arvin Lat, 2022-10-27 Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle Key FeaturesGain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and moreUse container and serverless services to solve a variety of ML engineering requirementsDesign, build, and secure automated MLOps pipelines and workflows on AWSBook Description There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS. By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements. What you will learnFind out how to train and deploy TensorFlow and PyTorch models on AWSUse containers and serverless services for ML engineering requirementsDiscover how to set up a serverless data warehouse and data lake on AWSBuild automated end-to-end MLOps pipelines using a variety of servicesUse AWS Glue DataBrew and SageMaker Data Wrangler for data engineeringExplore different solutions for deploying deep learning models on AWSApply cost optimization techniques to ML environments and systemsPreserve data privacy and model privacy using a variety of techniquesWho this book is for This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
  aws data engineering course: Advanced Data Analytics with AWS Joseph Conley , 2024-04-17 Master the Fundamentals of Data Analytics at Scale KEY FEATURES ● Comprehensive guide to constructing data engineering workflows spanning diverse data sources ● Expert techniques for transforming and visualizing data to extract actionable insights ● Advanced methodologies for analyzing data and employing machine learning to uncover intricate patterns DESCRIPTION Embark on a transformative journey into the realm of data analytics with AWS with this practical and incisive handbook. Begin your exploration with an insightful introduction to the fundamentals of data analytics, setting the stage for your AWS adventure. The book then covers collecting data efficiently and effectively on AWS, laying the groundwork for insightful analysis. It will dive deep into processing data, uncovering invaluable techniques to harness the full potential of your datasets. The book will equip you with advanced data analysis skills, unlocking the ability to discern complex patterns and insights. It covers additional use cases for data analysis on AWS, from predictive modeling to sentiment analysis, expanding your analytical horizons. The final section of the book will utilize the power of data virtualization and interaction, revolutionizing the way you engage with and derive value from your data. Gain valuable insights into emerging trends and technologies shaping the future of data analytics, and conclude your journey with actionable next steps, empowering you to continue your data analytics odyssey with confidence. WHAT WILL YOU LEARN ● Construct streamlined data engineering workflows capable of ingesting data from diverse sources and formats. ● Employ data transformation tools to efficiently cleanse and reshape data, priming it for analysis. ● Perform ad-hoc queries for preliminary data exploration, uncovering initial insights. ● Utilize prepared datasets to craft compelling, interactive data visualizations that communicate actionable insights. ● Develop advanced machine learning and Generative AI workflows to delve into intricate aspects of complex datasets, uncovering deeper insights. WHO IS THIS BOOK FOR? This book is ideal for aspiring data engineers, analysts, and data scientists seeking to deepen their understanding and practical skills in data engineering, data transformation, visualization, and advanced analytics. It is also beneficial for professionals and students looking to leverage AWS services for their data-related tasks. TABLE OF CONTENTS 1. Introduction to Data Analytics and AWS 2. Getting Started with AWS 3. Collecting Data with AWS 4. Processing Data on AWS 5. Descriptive Analytics on AWS 6. Advanced Data Analysis on AWS 7. Additional Use Cases for Data Analysis 8. Data Visualization and Interaction on AWS 9. The Future of Data Analytics 10. Conclusion and Next Steps Index
  aws data engineering course: AWS Certified Developer Official Study Guide Nick Alteen, Jennifer Fisher, Casey Gerena, Wes Gruver, Asim Jalis, Heiwad Osman, Marife Pagan, Santosh Patlolla, Michael Roth, 2019-09-24 Foreword by Werner Vogels, Vice President and Corporate Technology Officer, Amazon The AWS exam has been updated. Your study guide should be, too. The AWS Certified Developer Official Study Guide–Associate Exam is your ultimate preparation resource for the latest exam! Covering all exam objectives, this invaluable resource puts a team of AWS experts at your side with expert guidance, clear explanations, and the wisdom of experience with AWS best practices. You’ll master core services and basic architecture, and equip yourself to develop, deploy, and debug cloud-based applications using AWS. The AWS Developer certification is earned by those who demonstrate the technical knowledge and skill associated with best practices for building secure, reliable cloud-based applications using AWS technology. This book is your official exam prep companion, providing everything you need to know to pass with flying colors. Study the AWS Certified Developer Exam objectives Gain expert insight on core AWS services and best practices Test your understanding of key concepts with challenging chapter questions Access online study tools including electronic flashcards, a searchable glossary, practice exams, and more Cloud computing offers businesses the opportunity to replace up-front capital infrastructure expenses with low, variable costs that scale as they grow. This customized responsiveness has negated the need for far-future infrastructure planning, putting thousands of servers at their disposal as needed—and businesses have responded, propelling AWS to the number-one spot among cloud service providers. Now these businesses need qualified AWS developers, and the AWS certification validates the exact skills and knowledge they’re looking for. When you’re ready to get serious about your cloud credentials, the AWS Certified Developer Official Study Guide–Associate Exam is the resource you need to pass the exam with flying colors. NOTE: As of October 7, 2019, the accompanying code for hands-on exercises in the book is available for downloading from the secure Resources area in the online test bank. You'll find code for Chapters 1, 2, 11, and 12.
  aws data engineering course: 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 course: Data Wrangling on AWS Navnit Shukla, Sankar M, Sampat Palani, 2023-07-31 Revamp your data landscape and implement highly effective data pipelines in AWS with this hands-on guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Execute extract, transform, and load (ETL) tasks on data lakes, data warehouses, and databases Implement effective Pandas data operation with data wrangler Integrate pipelines with AWS data services Book DescriptionData wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools. First, you’ll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You’ll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you’ll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you’ll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects. By the end of this book, you’ll be well-equipped to perform data wrangling using AWS services.What you will learn Explore how to write simple to complex transformations using AWS data wrangler Use abstracted functions to extract and load data from and into AWS datastores Configure AWS Glue DataBrew for data wrangling Develop data pipelines using AWS data wrangler Integrate AWS security features into Data Wrangler using identity and access management (IAM) Optimize your data with AWS SageMaker Who this book is for This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. Basic knowledge of Python, Pandas, and a familiarity with AWS tools such as AWS Glue, Amazon Athena is required to get the most out of this book.
  aws data engineering course: Big Data Infrastructure Technologies for Data Analytics Yuri Demchenko,
  aws data engineering course: Power Engineering and Intelligent Systems Vivek Shrivastava, Jagdish Chand Bansal, B. K. Panigrahi, 2023-12-15 The book presents a collection of the high-quality research articles in the field of power engineering, grid integration, energy management, soft computing, artificial intelligence, signal and image processing, data science techniques, and their real-world applications. The papers are presented at International Conference on Power Engineering and Intelligent Systems (PEIS 2023), held during June 24–25, 2023, at National Institute of Technology Delhi, India.
  aws data engineering course: 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 course: Frontiers of Engineering National Academy of Engineering, 2011-03-04 This volume highlights the papers presented at the National Academy of Engineering's 2010 U.S. Frontiers of Engineering Symposium. Every year, the symposium brings together 100 outstanding young leaders in engineering to share their cutting-edge research and technical work. The 2010 symposium was held September 23 - 25, and hosted by IBM at the IBM Learning Center in Armonk, New York. Speakers were asked to prepare extended summaries of their presentations, which are reprinted here. The intent of this book is to convey the excitement of this unique meeting and to highlight cutting-edge developments in engineering research and technical work.
  aws data engineering course: Beginning Apache Spark Using Azure Databricks Robert Ilijason, 2020-06-11 Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster. This book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. You will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics use. Finally, you will see how services such as Databricks provide the power of Apache Spark, without you having to know anything about configuring hardware or software. By removing the need for expensive experts and hardware, your resources can instead be allocated to actually finding business value in the data. This book guides you through some advanced topics such as analytics in the cloud, data lakes, data ingestion, architecture, machine learning, and tools, including Apache Spark, Apache Hadoop, Apache Hive, Python, and SQL. Valuable exercises help reinforce what you have learned. What You Will Learn Discover the value of big data analytics that leverage the power of the cloudGet started with Databricks using SQL and Python in either Microsoft Azure or AWSUnderstand the underlying technology, and how the cloud and Apache Spark fit into the bigger picture See how these tools are used in the real world Run basic analytics, including machine learning, on billions of rows at a fraction of a cost or free Who This Book Is For Data engineers, data scientists, and cloud architects who want or need to run advanced analytics in the cloud. It is assumed that the reader has data experience, but perhaps minimal exposure to Apache Spark and Azure Databricks. The book is also recommended for people who want to get started in the analytics field, as it provides a strong foundation.
  aws data engineering course: Ace the AWS Solutions Architect Associates SAA-C03 Certification Exam 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! There are 3 reasons to strengthen your cloud skills: 1- Cloud roles pay well. The average base salary for a Solutions Architect in the U.S. is $140,000. 2- Cloud skills are in demand. Cloud computing has been one of the most in-demand hard skills for 7 years running. 3- Learning cloud can get you a raise. The average raise received by IT pros who gained new skills and/or certifications is $15 – 30K. AWS certification is globally recognized as the premier way to demonstrate your AWS cloud skills. The AWS Certified Solutions Architect – Associate Level (SAA-C03) exam validates your ability to effectively demonstrate knowledge of how to architect and deploy secure and robust applications on AWS technologies. It is a required exam for the AWS Certified Solutions Architect – Professional Level certification. In order to prepare for this exam, We suggest purchasing our AWS Certified Solutions Architect – Associate Level Exam Preparation eBook. This AWS Cloud Solutions Architect Associates Certification App covers all of the key concepts you need to know for the AWS Solutions Architect Associate Exam. Solution architecture is a practice of defining and describing an architecture of a system delivered in context of a specific solution and as such it may encompass description of an entire system or only its specific parts. Definition of a solution architecture is typically led by a solution architect. The AWS Certified Solutions Architect - Associate (SAA, SAA-C03) exam is intended for individuals who perform in a solutions architect role. The exam validates a candidate's ability to use AWS technologies to design solutions based on the AWS Well-Architected Framework including: Design solutions that incorporate AWS services to meet current business requirements and future projected needs Design architectures that are secure, resilient, high-performing, and cost-optimized Review existing solutions and determine improvements 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 Key tools, technologies, and concepts covered • Compute • Cost management • Database • Disaster recovery • High performance • Management and governance • Microservices and component decoupling • Migration and data transfer • Networking, connectivity, and content delivery • Resiliency • Security • Serverless and event-driven design principles • Storage Some New AWS services covered: AWS Data Exchange, AWS Data Pipeline, AWS Lake Formation, Amazon Managed Streaming for Apache Kafka, Amazon AppFlow, AWS Outposts, VMware Cloud on AWS, AWS Wavelength, Amazon Neptune, Amazon Quantum Ledger Database, Amazon Timestream, AWS Amplify, Amazon Comprehend, Amazon Forecast, Amazon Fraud Detector, Amazon Kendra, AWS License Manager, Amazon Managed Grafana, Amazon Managed Service for Prometheus, AWS Proton, Amazon Elastic Transcoder, Amazon Kinesis Video Streams, AWS Application Discovery Service, AWS WAF Serverless, AWS AppSync, etc. Table of contents: Design Secure Architectures – Description Design Secure Architectures - Cheat Sheets Design Secure Architectures - Flashcards Design Secure Architectures – Illustrations Design Secure Architectures – Quiz Design Resilient Architectures – Quiz Design High-Performing Architectures – Description Design High-Performing Architectures - Cheat Sheets Design High-Performing Architectures- Illustrations Design High-Performing Architectures - Quiz Design Cost-Optimized Architectures – Description Design Cost-Optimized Architectures - Cheat Sheets Design Cost-Optimized Architectures: Illustrations Design Cost-Optimized Architectures – Quiz Top 50 AWS Recommended Security Best Practices AWS SAA FAQs Practice Exam – 69 Questions & Answers Passed AWS SAA-C03 Testimonials AWS Networking – ENI vs EFA vs ENA What are the top 10 tips and tricks to do to Ace the 2023 AWS Certified Solutions Architect SAA-C03 Exam? An Insightful Overview of SAA-C03 Exam Topics Encountered Reflecting on My SAA-C03 Exam Journey: From Setback to Success Mobile App Version of the AWS Solutions Architect Associates SAA-C03 Certification Exam Prep Book: Android: https://play.google.com/store/apps/details?id=com.awssolutionarchitectassociateexampreppro.app iOs: https://apps.apple.com/ca/app/solution-architect-assoc-pro/id1501465417 Windows 10/11: https://www.microsoft.com/en-ca/store/p/aws-cert-solution-architect-associate-prep-pro/9pcn58wdr1qr 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, Tutorialdojo'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, Tutorialdojo's materials were simpler. My scores ranged from 65% on Mareek's tests to 75-80% on Tutorialdojo, with a 740 on the official AWS practice test. Sharing this for those on a similar journey. Get your copy now and clear the exam at your first attempt.
  aws data engineering course: Training Students to Extract Value from Big Data National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, 2015-01-16 As the availability of high-throughput data-collection technologies, such as information-sensing mobile devices, remote sensing, internet log records, and wireless sensor networks has grown, science, engineering, and business have rapidly transitioned from striving to develop information from scant data to a situation in which the challenge is now that the amount of information exceeds a human's ability to examine, let alone absorb, it. Data sets are increasingly complex, and this potentially increases the problems associated with such concerns as missing information and other quality concerns, data heterogeneity, and differing data formats. The nation's ability to make use of data depends heavily on the availability of a workforce that is properly trained and ready to tackle high-need areas. Training students to be capable in exploiting big data requires experience with statistical analysis, machine learning, and computational infrastructure that permits the real problems associated with massive data to be revealed and, ultimately, addressed. Analysis of big data requires cross-disciplinary skills, including the ability to make modeling decisions while balancing trade-offs between optimization and approximation, all while being attentive to useful metrics and system robustness. To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and characteristics of a prospective data-science student; what to teach, that is, the technical and practical content that should be taught to the student; and how to teach, that is, the structure and organization of a data-science program. Training Students to Extract Value from Big Data summarizes a workshop convened in April 2014 by the National Research Council's Committee on Applied and Theoretical Statistics to explore how best to train students to use big data. The workshop explored the need for training and curricula and coursework that should be included. One impetus for the workshop was the current fragmented view of what is meant by analysis of big data, data analytics, or data science. New graduate programs are introduced regularly, and they have their own notions of what is meant by those terms and, most important, of what students need to know to be proficient in data-intensive work. This report provides a variety of perspectives about those elements and about their integration into courses and curricula.
  aws data engineering course: Designing Cloud Data Platforms Danil Zburivsky, Lynda Partner, 2021-03-17 In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. Summary Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is a hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you’ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You’ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyze it. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Well-designed pipelines, storage systems, and APIs eliminate the complicated scaling and maintenance required with on-prem data centers. Once you learn the patterns for designing cloud data platforms, you’ll maximize performance no matter which cloud vendor you use. About the book In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. What's inside Best practices for structured and unstructured data sets Cloud-ready machine learning tools Metadata and real-time analytics Defensive architecture, access, and security About the reader For data professionals familiar with the basics of cloud computing, and Hadoop or Spark. About the author Danil Zburivsky has over 10 years of experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. Table of Contents 1 Introducing the data platform 2 Why a data platform and not just a data warehouse 3 Getting bigger and leveraging the Big 3: Amazon, Microsoft Azure, and Google 4 Getting data into the platform 5 Organizing and processing data 6 Real-time data processing and analytics 7 Metadata layer architecture 8 Schema management 9 Data access and security 10 Fueling business value with data platforms
  aws data engineering course: Automated Machine Learning on AWS Trenton Potgieter, Jonathan Dahlberg, 2022-04-15 Automate the process of building, training, and deploying machine learning applications to production with AWS solutions such as SageMaker Autopilot, AutoGluon, Step Functions, Amazon Managed Workflows for Apache Airflow, and more Key FeaturesExplore the various AWS services that make automated machine learning easierRecognize the role of DevOps and MLOps methodologies in pipeline automationGet acquainted with additional AWS services such as Step Functions, MWAA, and more to overcome automation challengesBook Description AWS provides a wide range of solutions to help automate a machine learning workflow with just a few lines of code. With this practical book, you'll learn how to automate a machine learning pipeline using the various AWS services. Automated Machine Learning on AWS begins with a quick overview of what the machine learning pipeline/process looks like and highlights the typical challenges that you may face when building a pipeline. Throughout the book, you'll become well versed with various AWS solutions such as Amazon SageMaker Autopilot, AutoGluon, and AWS Step Functions to automate an end-to-end ML process with the help of hands-on examples. The book will show you how to build, monitor, and execute a CI/CD pipeline for the ML process and how the various CI/CD services within AWS can be applied to a use case with the Cloud Development Kit (CDK). You'll understand what a data-centric ML process is by working with the Amazon Managed Services for Apache Airflow and then build a managed Airflow environment. You'll also cover the key success criteria for an MLSDLC implementation and the process of creating a self-mutating CI/CD pipeline using AWS CDK from the perspective of the platform engineering team. By the end of this AWS book, you'll be able to effectively automate a complete machine learning pipeline and deploy it to production. What you will learnEmploy SageMaker Autopilot and Amazon SageMaker SDK to automate the machine learning processUnderstand how to use AutoGluon to automate complicated model building tasksUse the AWS CDK to codify the machine learning processCreate, deploy, and rebuild a CI/CD pipeline on AWSBuild an ML workflow using AWS Step Functions and the Data Science SDKLeverage the Amazon SageMaker Feature Store to automate the machine learning software development life cycle (MLSDLC)Discover how to use Amazon MWAA for a data-centric ML processWho this book is for This book is for the novice as well as experienced machine learning practitioners looking to automate the process of building, training, and deploying machine learning-based solutions into production, using both purpose-built and other AWS services. A basic understanding of the end-to-end machine learning process and concepts, Python programming, and AWS is necessary to make the most out of this book.
  aws data engineering course: Numerical Simulation - Advanced Techniques for Science and Engineering Ali Soofastaei, 2023-11-15 Numerical simulation is a powerful tool used in various fields of science and engineering to model complex systems and predict their behavior. It involves developing mathematical models that describe the behavior of a system and using computer algorithms to solve these models numerically. By doing so, researchers and engineers can study the behavior of a system in detail, which may only be possible with analytical methods. Numerical simulation has many advantages over traditional analytical methods. It allows researchers and engineers to study complex systems’ behavior in detail and predict their behavior in different scenarios. It also allows for the optimization of systems and the identification of design flaws before they are built. However, numerical simulation has its limitations. It requires significant computational resources, and the accuracy of the results depends on the quality of the mathematical models and the discretization methods used. Nevertheless, numerical simulation remains a valuable tool in many fields and its importance is likely to grow as computational resources become more powerful and widely available. Numerical simulation is widely used in physics, engineering, computer science, and mathematics. In physics, for example, numerical simulation is used to study the behavior of complex systems such as weather patterns, fluid dynamics, and particle interactions. In engineering, it is used to design and optimize systems such as aircraft, cars, and buildings. In computer science, numerical simulation models and optimization algorithms and data structures. In mathematics, it is used to study complex mathematical models and to solve complex equations. This book familiarizes readers with the practical application of the numerical simulation technique to solve complex analytical problems in different industries and sciences.
  aws data engineering course: Cyber Forensics Up and Running Tarun Vashishth, 2023-12-12 Empowering you to investigate, analyze, and secure the digital realm KEY FEATURES ● Comprehensive coverage of all digital forensics concepts. ● Real-world case studies and examples to illustrate techniques. ● Step-by-step instructions for setting up and using essential forensic tools. ● In-depth exploration of volatile and non-volatile data analysis. DESCRIPTION Digital forensics is the art and science of extracting the hidden truth and this book is your hands-on companion, bringing the world of digital forensics to life. Starting with the core principles of digital forensics, the book explores the significance of various case types, the interconnectedness of the field with cybersecurity, and the ever-expanding digital world's challenges. As you progress, you will explore data acquisition, image formats, digital evidence preservation, file carving, metadata extraction, and the practical use of essential forensic tools like HxD, The Sleuth Kit, Autopsy, Volatility, and PowerForensics. The book offers step-by-step instructions, real-world case studies, and practical examples, ensuring that beginners can confidently set up and use forensic tools. Experienced professionals, on the other hand, will find advanced insights into memory analysis, network forensics, anti-forensic techniques, and more. This book empowers you to become a digital detective, capable of uncovering data secrets, investigating networks, exploring volatile and non-volatile evidence, and understanding the intricacies of modern browsers and emails. WHAT YOU WILL LEARN ● Learn how to set up and use digital forensic tools, including virtual environments. ● Learn about live forensics, incident response, and timeline examination. ● In-depth exploration of Windows Registry and USBs. ● Network forensics, PCAPs, and malware scenarios. ● Memory forensics, malware detection, and file carving. ● Advance tools like PowerForensics and Autopsy. WHO THIS BOOK IS FOR Whether you are a tech-savvy detective, a curious student, or a seasoned cybersecurity pro seeking to amplify your skillset. Network admins, law enforcement officers, incident responders, aspiring analysts, and even legal professionals will find invaluable tools and techniques within these pages. TABLE OF CONTENTS 1. Introduction to Essential Concepts of Digital Forensics 2. Digital Forensics Lab Setup 3. Data Collection: Volatile and Non-Volatile 4. Forensics Analysis: Live Response 5. File System and Log Analysis 6. Windows Registry and Artifacts 7. Network Data Collection and Analysis 8. Memory Forensics: Techniques and Tools 9. Browser and Email Forensics 10. Advanced Forensics Tools, Commands and Methods 11. Anti-Digital Forensics Techniques and Methods
  aws data engineering course: Mastering MLOps Architecture: From Code to Deployment Raman Jhajj, 2023-12-12 Harness the power of MLOps for managing real time machine learning project cycle KEY FEATURES ● Comprehensive coverage of MLOps concepts, architecture, tools and techniques. ● Practical focus on building end-to-end ML Systems for Continual Learning with MLOps. ● Actionable insights on CI/CD, monitoring, continual model training and automated retraining. DESCRIPTION MLOps, a combination of DevOps, data engineering, and machine learning, is crucial for delivering high-quality machine learning results due to the dynamic nature of machine learning data. This book delves into MLOps, covering its core concepts, components, and architecture, demonstrating how MLOps fosters robust and continuously improving machine learning systems. By covering the end-to-end machine learning pipeline from data to deployment, the book helps readers implement MLOps workflows. It discusses techniques like feature engineering, model development, A/B testing, and canary deployments. The book equips readers with knowledge of MLOps tools and infrastructure for tasks like model tracking, model governance, metadata management, and pipeline orchestration. Monitoring and maintenance processes to detect model degradation are covered in depth. Readers can gain skills to build efficient CI/CD pipelines, deploy models faster, and make their ML systems more reliable, robust and production-ready. Overall, the book is an indispensable guide to MLOps and its applications for delivering business value through continuous machine learning and AI. WHAT YOU WILL LEARN ● Architect robust MLOps infrastructure with components like feature stores. ● Leverage MLOps tools like model registries, metadata stores, pipelines. ● Build CI/CD workflows to deploy models faster and continually. ● Monitor and maintain models in production to detect degradation. ● Create automated workflows for retraining and updating models in production. WHO THIS BOOK IS FOR Machine learning specialists, data scientists, DevOps professionals, software development teams, and all those who want to adopt the DevOps approach in their agile machine learning experiments and applications. Prior knowledge of machine learning and Python programming is desired. TABLE OF CONTENTS 1. Getting Started with MLOps 2. MLOps Architecture and Components 3. MLOps Infrastructure and Tools 4. What are Machine Learning Systems? 5. Data Preparation and Model Development 6. Model Deployment and Serving 7. Continuous Delivery of Machine Learning Models 8. Continual Learning 9. Continuous Monitoring, Logging, and Maintenance
  aws data engineering course: 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 course: Building and Automating Penetration Testing Labs in the Cloud Joshua Arvin Lat, 2023-10-13 Take your penetration testing career to the next level by discovering how to set up and exploit cost-effective hacking lab environments on AWS, Azure, and GCP Key Features Explore strategies for managing the complexity, cost, and security of running labs in the cloud Unlock the power of infrastructure as code and generative AI when building complex lab environments Learn how to build pentesting labs that mimic modern environments on AWS, Azure, and GCP Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe significant increase in the number of cloud-related threats and issues has led to a surge in the demand for cloud security professionals. This book will help you set up vulnerable-by-design environments in the cloud to minimize the risks involved while learning all about cloud penetration testing and ethical hacking. This step-by-step guide begins by helping you design and build penetration testing labs that mimic modern cloud environments running on AWS, Azure, and Google Cloud Platform (GCP). Next, you’ll find out how to use infrastructure as code (IaC) solutions to manage a variety of lab environments in the cloud. As you advance, you’ll discover how generative AI tools, such as ChatGPT, can be leveraged to accelerate the preparation of IaC templates and configurations. You’ll also learn how to validate vulnerabilities by exploiting misconfigurations and vulnerabilities using various penetration testing tools and techniques. Finally, you’ll explore several practical strategies for managing the complexity, cost, and risks involved when dealing with penetration testing lab environments in the cloud. By the end of this penetration testing book, you’ll be able to design and build cost-effective vulnerable cloud lab environments where you can experiment and practice different types of attacks and penetration testing techniques.What you will learn Build vulnerable-by-design labs that mimic modern cloud environments Find out how to manage the risks associated with cloud lab environments Use infrastructure as code to automate lab infrastructure deployments Validate vulnerabilities present in penetration testing labs Find out how to manage the costs of running labs on AWS, Azure, and GCP Set up IAM privilege escalation labs for advanced penetration testing Use generative AI tools to generate infrastructure as code templates Import the Kali Linux Generic Cloud Image to the cloud with ease Who this book is forThis book is for security engineers, cloud engineers, and aspiring security professionals who want to learn more about penetration testing and cloud security. Other tech professionals working on advancing their career in cloud security who want to learn how to manage the complexity, costs, and risks associated with building and managing hacking lab environments in the cloud will find this book useful.
  aws data engineering course: Navigating the Math Major Carrie Diaz Eaton, Allison Henrich, Steven Klee, Jennifer Townsend, 2024-06-14 Are you a mathematics major or thinking about becoming one? This friendly guidebook is for you, no matter where you are in your studies. For those just starting out, there are: interactive exercises to help you chart your personalized course, brief overviews of the typical courses you will encounter during your studies, recommended extracurricular activities that can enrich your mathematical journey. Mathematics majors looking for effective ways to support their success will discover: practical examples of dealing with setbacks and challenges in mathematics, a primer on study skills, including particular advice like how to effectively read mathematical literature and learn mathematically focused programming. Students thinking about life after graduation will find: advice for seeking jobs outside academia, guidance for applying to graduate programs, a collection of interviews with former mathematics majors now working in a wide variety of careers—they share their experience and practical advice for breaking into their field. Packed with a wealth of information, Navigating the Math Major is your comprehensive resource to the undergraduate mathematics degree program.
  aws data engineering course: The Self-Taught Cloud Computing Engineer Dr. Logan Song, 2023-09-22 Transform into a cloud-savvy professional by mastering cloud technologies through hands-on projects and expert guidance, paving the way for a thriving cloud computing career Key Features Learn all about cloud computing at your own pace with this easy-to-follow guide Develop a well-rounded skill set, encompassing fundamentals, data, machine learning, and security Work on real-world industrial projects and business use cases, and chart a path for your personal cloud career advancement Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Self-Taught Cloud Computing Engineer is a comprehensive guide to mastering cloud computing concepts by building a broad and deep cloud knowledge base, developing hands-on cloud skills, and achieving professional cloud certifications. Even if you’re a beginner with a basic understanding of computer hardware and software, this book serves as the means to transition into a cloud computing career. Starting with the Amazon cloud, you’ll explore the fundamental AWS cloud services, then progress to advanced AWS cloud services in the domains of data, machine learning, and security. Next, you’ll build proficiency in Microsoft Azure Cloud and Google Cloud Platform (GCP) by examining the common attributes of the three clouds while distinguishing their unique features. You’ll further enhance your skills through practical experience on these platforms with real-life cloud project implementations. Finally, you’ll find expert guidance on cloud certifications and career development. By the end of this cloud computing book, you’ll have become a cloud-savvy professional well-versed in AWS, Azure, and GCP, ready to pursue cloud certifications to validate your skills.What you will learn Develop the core skills needed to work with cloud computing platforms such as AWS, Azure, and GCP Gain proficiency in compute, storage, and networking services across multi-cloud and hybrid-cloud environments Integrate cloud databases, big data, and machine learning services in multi-cloud environments Design and develop data pipelines, encompassing data ingestion, storage, processing, and visualization in the clouds Implement machine learning pipelines in a multi-cloud environment Secure cloud infrastructure ecosystems with advanced cloud security services Who this book is for Whether you're new to cloud computing or a seasoned professional looking to expand your expertise, this book is for anyone in the information technology domain who aspires to thrive in the realm of cloud computing. With this comprehensive roadmap, you’ll have the tools to build a successful cloud computing career.
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Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift. This course …

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AWS now provides a robust, cloud-based service — Amazon SageMaker AI — so that developers of all skill levels can use machine learning technology. SageMaker AI is a fully managed machine

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AWS Security Documentation shows how to configure AWS services to meet your security and compliance objectives. AWS customers benefit from a data center and network architecture …

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Jan 1, 2016 · Big Data Analytics Options on AWS AWS Whitepaper • Amazon Kinesis Video Streams enables you to build custom applications that process or analyze streaming video. • …

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