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| aws data engineer training hyderabad: Azure Data Engineer Associate Certification Guide Newton Alex, 2022-02-28 Become well-versed with data engineering concepts and exam objectives to achieve Azure Data Engineer Associate certification Key Features Understand and apply data engineering concepts to real-world problems and prepare for the DP-203 certification exam Explore the various Azure services for building end-to-end data solutions Gain a solid understanding of building secure and sustainable data solutions using Azure services Book DescriptionAzure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other. Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you’ll work on sample questions and answers to familiarize yourself with the pattern of the exam. By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.What you will learn Gain intermediate-level knowledge of Azure the data infrastructure Design and implement data lake solutions with batch and stream pipelines Identify the partition strategies available in Azure storage technologies Implement different table geometries in Azure Synapse Analytics Use the transformations available in T-SQL, Spark, and Azure Data Factory Use Azure Databricks or Synapse Spark to process data using Notebooks Design security using RBAC, ACL, encryption, data masking, and more Monitor and optimize data pipelines with debugging tips Who this book is for This book is for data engineers who want to take the DP-203: Azure Data Engineer Associate exam and are looking to gain in-depth knowledge of the Azure cloud stack. The book will also help engineers and product managers who are new to Azure or interviewing with companies working on Azure technologies, to get hands-on experience of Azure data technologies. A basic understanding of cloud technologies, extract, transform, and load (ETL), and databases will help you get the most out of this book. |
| aws data engineer training hyderabad: 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 engineer training hyderabad: 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 engineer training hyderabad: AWS Certified Data Engineer Study Guide Syed Humair, Chenjerai Gumbo, Adam Gatt, Asif Abbasi, Lakshmi Nair, 2024-11-27 |
| aws data engineer training hyderabad: 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 engineer training hyderabad: 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 engineer training hyderabad: Data Science on AWS Chris Fregly, Antje Barth, 2021 If you use data to make critical business decisions, this book is for you. Whether you're a data analyst, research scientist, data engineer, ML engineer, data scientist, application developer, or systems developer, this guide helps you broaden your understanding of the modern data science stack, create your own machine learning pipelines, and deploy them to applications at production scale. The AWS data science stack unifies data science, data engineering, and application development to help you level up your skills beyond your current role. Authors Antje Barth and Chris Fregly show you how to build your own ML pipelines from existing APIs, submit them to the cloud, and integrate results into your application in minutes instead of days. Innovate quickly and save money with AWS's on-demand, serverless, and cloud-managed services Implement open source technologies such as Kubeflow, Kubernetes, TensorFlow, and Apache Spark on AWS Build and deploy an end-to-end, continuous ML pipeline with the AWS data science stack Perform advanced analytics on at-rest and streaming data with AWS and Spark Integrate streaming data into your ML pipeline for continuous delivery of ML models using AWS and Apache Kafka. |
| aws data engineer training hyderabad: AWS Certified Machine Learning Study Guide Shreyas Subramanian, Stefan Natu, 2021-11-19 Succeed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide As the most popular cloud service in the world today, Amazon Web Services offers a wide range of opportunities for those interested in the development and deployment of artificial intelligence and machine learning business solutions. The AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam delivers hyper-focused, authoritative instruction for anyone considering the pursuit of the prestigious Amazon Web Services Machine Learning certification or a new career as a machine learning specialist working within the AWS architecture. From exam to interview to your first day on the job, this study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. And with the practice exams and assessments, electronic flashcards, and supplementary online resources that accompany this Study Guide, you’ll be prepared for success in every subject area covered by the exam. You’ll also find: An intuitive and organized layout perfect for anyone taking the exam for the first time or seasoned professionals seeking a refresher on machine learning on the AWS Cloud Authoritative instruction on a widely recognized certification that unlocks countless career opportunities in machine learning and data science Access to the Sybex online learning resources and test bank, with chapter review questions, a full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning. |
| aws data engineer training hyderabad: Azure Data Engineer Associate Certification Guide Newton Alex, 2022-02-28 Become well-versed with data engineering concepts and exam objectives to achieve Azure Data Engineer Associate certification Key Features Understand and apply data engineering concepts to real-world problems and prepare for the DP-203 certification exam Explore the various Azure services for building end-to-end data solutions Gain a solid understanding of building secure and sustainable data solutions using Azure services Book DescriptionAzure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other. Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you’ll work on sample questions and answers to familiarize yourself with the pattern of the exam. By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.What you will learn Gain intermediate-level knowledge of Azure the data infrastructure Design and implement data lake solutions with batch and stream pipelines Identify the partition strategies available in Azure storage technologies Implement different table geometries in Azure Synapse Analytics Use the transformations available in T-SQL, Spark, and Azure Data Factory Use Azure Databricks or Synapse Spark to process data using Notebooks Design security using RBAC, ACL, encryption, data masking, and more Monitor and optimize data pipelines with debugging tips Who this book is for This book is for data engineers who want to take the DP-203: Azure Data Engineer Associate exam and are looking to gain in-depth knowledge of the Azure cloud stack. The book will also help engineers and product managers who are new to Azure or interviewing with companies working on Azure technologies, to get hands-on experience of Azure data technologies. A basic understanding of cloud technologies, extract, transform, and load (ETL), and databases will help you get the most out of this book. |
| aws data engineer training hyderabad: Data Science on AWS Chris Fregly, Antje Barth, 2021-07-31 If you use data to make critical business decisions, this book is for you. Whether you're a data analyst, research scientist, data engineer, ML engineer, data scientist, application developer, or systems developer, this guide helps you broaden your understanding of the modern data science stack, create your own machine learning pipelines, and deploy them to applications at production scale. The AWS data science stack unifies data science, data engineering, and application development to help you level up your skills beyond your current role. Authors Antje Barth and Chris Fregly show you how to build your own ML pipelines from existing APIs, submit them to the cloud, and integrate results into your application in minutes instead of days. Innovate quickly and save money with AWS's on-demand, serverless, and cloud-managed services Implement open source technologies such as Kubeflow, Kubernetes, TensorFlow, and Apache Spark on AWS Build and deploy an end-to-end, continuous ML pipeline with the AWS data science stack Perform advanced analytics on at-rest and streaming data with AWS and Spark Integrate streaming data into your ML pipeline for continuous delivery of ML models using AWS and Apache Kafka |
| aws data engineer training hyderabad: AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide Somanath Nanda, Weslley Moura, 2024-02-29 Prepare confidently for the AWS MLS-C01 certification with this comprehensive and up-to-date exam guide, accompanied by web-based tools such as mock exams, flashcards, and exam tips Key Features Gain proficiency in AWS machine learning services to excel in the MLS-C01 exam Build model training and inference pipelines and deploy machine learning models to the AWS cloud Practice on the go with the mobile-friendly bonus website, accessible with the book Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe AWS Certified Machine Learning Specialty (MLS-C01) exam evaluates your ability to execute machine learning tasks on AWS infrastructure. This comprehensive book aligns with the latest exam syllabus, offering practical examples to support your real-world machine learning projects on AWS. Additionally, you'll get lifetime access to supplementary online resources, including mock exams with exam-like timers, detailed solutions, interactive flashcards, and invaluable exam tips, all accessible across various devices—PCs, tablets, and smartphones. Throughout the book, you’ll learn data preparation techniques for machine learning, covering diverse methods for data manipulation and transformation across different variable types. Addressing challenges such as missing data and outliers, the book guides you through an array of machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, accompanied by requisite machine learning algorithms essential for exam success. The book helps you master the deployment of models in production environments and their subsequent monitoring. Equipped with insights from this book and the accompanying mock exams, you'll be fully prepared to achieve the AWS MLS-C01 certification.What you will learn Identify ML frameworks for specific tasks Apply CRISP-DM to build ML pipelines Combine AWS services to build AI/ML solutions Apply various techniques to transform your data, such as one-hot encoding, binary encoder, ordinal encoding, binning, and text transformations Visualize relationships, comparisons, compositions, and distributions in the data Use data preparation techniques and AWS services for batch and real-time data processing Create training and inference ML pipelines with Sage Maker Deploy ML models in a production environment efficiently Who this book is for This book is designed for both students and professionals preparing for the AWS Certified Machine Learning Specialty exam or enhance their understanding of machine learning, with a specific emphasis on AWS. Familiarity with machine learning basics and AWS services is recommended to fully benefit from this book. |
| aws data engineer training hyderabad: Data Analytics in the AWS Cloud Joe Minichino, 2023-04-06 A comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS, accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you’ll explore every relevant aspect of data analytics—from data engineering to analysis, business intelligence, DevOps, and MLOps—as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You’ll also find: Real-world use cases of AWS architectures that demystify the applications of data analytics Accessible introductions to data acquisition, importation, storage, visualization, and reporting Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenance A can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform. |
| aws data engineer training hyderabad: AWS Certified Data Analytics Study Guide Asif Abbasi, 2020-11-20 Move your career forward with AWS certification! Prepare for the AWS Certified Data Analytics Specialty Exam with this thorough study guide This comprehensive study guide will help assess your technical skills and prepare for the updated AWS Certified Data Analytics exam. Earning this AWS certification will confirm your expertise in designing and implementing AWS services to derive value from data. The AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam is designed for business analysts and IT professionals who perform complex Big Data analyses. This AWS Specialty Exam guide gets you ready for certification testing with expert content, real-world knowledge, key exam concepts, and topic reviews. Gain confidence by studying the subject areas and working through the practice questions. Big data concepts covered in the guide include: Collection Storage Processing Analysis Visualization Data security AWS certifications allow professionals to demonstrate skills related to leading Amazon Web Services technology. The AWS Certified Data Analytics Specialty (DAS-C01) Exam specifically evaluates your ability to design and maintain Big Data, leverage tools to automate data analysis, and implement AWS Big Data services according to architectural best practices. An exam study guide can help you feel more prepared about taking an AWS certification test and advancing your professional career. In addition to the guide’s content, you’ll have access to an online learning environment and test bank that offers practice exams, a glossary, and electronic flashcards. |
| aws data engineer training hyderabad: Data Science mit AWS Chris Fregly, Antje Barth, 2022-04-13 Von der ersten Idee bis zur konkreten Anwendung: Ihre Data-Science-Projekte in der AWS-Cloud realisieren Der US-Besteller zu Amazon Web Services jetzt auf Deutsch Beschreibt alle wichtigen Konzepte und die wichtigsten AWS-Dienste mit vielen Beispielen aus der Praxis Deckt den kompletten End-to-End-Prozess von der Entwicklung der Modelle bis zum ihrem konkreten Einsatz ab Mit Best Practices für alle Aspekte der Modellerstellung einschließlich Training, Deployment, Sicherheit und MLOps Mit diesem Buch lernen Machine-Learning- und KI-Praktiker, wie sie erfolgreich Data-Science-Projekte mit Amazon Web Services erstellen und in den produktiven Einsatz bringen. Es bietet einen detaillierten Einblick in den KI- und Machine-Learning-Stack von Amazon, der Data Science, Data Engineering und Anwendungsentwicklung vereint. Chris Fregly und Antje Barth beschreiben verständlich und umfassend, wie Sie das breite Spektrum an AWS-Tools nutzbringend für Ihre ML-Projekte einsetzen. Der praxisorientierte Leitfaden zeigt Ihnen konkret, wie Sie ML-Pipelines in der Cloud erstellen und die Ergebnisse dann innerhalb von Minuten in Anwendungen integrieren. Sie erfahren, wie Sie alle Teilschritte eines Workflows zu einer wiederverwendbaren MLOps-Pipeline bündeln, und Sie lernen zahlreiche reale Use Cases zum Beispiel aus den Bereichen Natural Language Processing, Computer Vision oder Betrugserkennung kennen. Im gesamten Buch wird zudem erläutert, wie Sie Kosten senken und die Performance Ihrer Anwendungen optimieren können. |
| aws data engineer training hyderabad: Data Engineering on Azure Vlad Riscutia, 2021-08-17 Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data |
| aws data engineer training hyderabad: Official Google Cloud Certified Professional Data Engineer Study Guide Dan Sullivan, 2020-05-18 The proven Study Guide that prepares you for this new Google Cloud exam The Google Cloud Certified Professional Data Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data Engineer Study Guide is your ace in the hole for deploying and managing analytics and machine learning applications. Build and operationalize storage systems, pipelines, and compute infrastructure Understand machine learning models and learn how to select pre-built models Monitor and troubleshoot machine learning models Design analytics and machine learning applications that are secure, scalable, and highly available. This exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform. |
| aws data engineer training hyderabad: Data Engineering with Apache Spark, Delta Lake, and Lakehouse Manoj Kukreja, Danil Zburivsky, 2021-10-22 Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected. |
| aws data engineer training hyderabad: Data Engineering Fundamentals Sandeep Kumar Pandey, 2024-08-28 Data Engineering Fundamental: A Step by Step Approach Unlock the Power of Data with Practical Guidance from a Data Engineering Expert In today's data-driven world, organizations thrive on the ability to harness, process, and analyze data effectively. Data Engineering Fundamental: A Step by Step Approach is the ultimate guide for aspiring data engineers, data analysts, and professionals seeking to build a robust foundation in data engineering. This comprehensive book breaks down the core concepts of data engineering, offering a practical, hands-on approach to mastering key tools and techniques. From data pipelines and ETL processes to cloud technologies and database optimization, you'll explore a wide range of topics essential for managing and transforming data at scale. Key features include: Real-World Case Studies: Apply your learning to scenarios faced by data engineers in leading industries. Step-by-Step Guides: Detailed instructions to walk you through complex data engineering processes. Tool Mastery: In-depth coverage of popular platforms such as AWS, Azure, Databricks, and SQL databases. Best Practices: Learn how to design, optimize, and maintain efficient data pipelines. |
| aws data engineer training hyderabad: 97 Things Every Data Engineer Should Know Tobias Macey, 2021 Take advantage of the sky-high demand for data engineers today. With this in-depth book, current and aspiring engineers will learn powerful, real-world best practices for managing data big and small. Contributors from Google, Microsoft, IBM, Facebook, Databricks, and GitHub share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey from MIT Open Learning, 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. Projects include: Building pipelines Stream processing Data privacy and security Data governance and lineage Data storage and architecture Ecosystem of modern tools Data team makeup and culture Career advice. |
| aws data engineer training hyderabad: Data Engineering with Google Cloud Platform Adi Wijaya, 2022-03-31 Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book. |
| aws data engineer training hyderabad: AWS Certified Solutions Architect Associate Training Notes Neal Davis, 2020-12-17 Preparing for the AWS Certified Solutions Architect Associate exam - and looking for a way to fast-track your exam success? Then these training notes for the AWS Certified Solutions Architect Associate are for you. - EVERYTHING YOU NEED TO KNOW - Deep dive into the SAA-C02 exam objectives with over 300 pages of detailed facts, tables and diagrams - ALWAYS UP TO DATE - These key exam essentials reflect the latest SAA-C02 exam and are regularly updated - BONUS QUIZ QUESTIONS - Review your knowledge at the end of each major chapter with 80 quiz questions - ONLINE PRACTICE TEST - Simulate the real exam with 65 scored and timed practice questions in Digital Cloud Training's online learning environment - CLEAR LANGUAGE - Focused and to the point presented in an easy-to-read format - GAIN THE EDGE ON EXAM DAY - Learn from AWS subject-matter experts with these essential cheat sheets - SAVE VALUABLE TIME - Get straight to the facts you need to know to successfully pass your AWS Certified Solutions Architect Associate exam with confidence. The AWS Solutions Architect certification is highly regarded in the world of Cloud Computing and requires a foundational knowledge of Amazon Web Services, its architectural principles, value proposition, billing and pricing and key services. Based on the SAA-C02 exam, these training notes will shortcut your study time and maximize your chances of passing the AWS Certified Solutions Architect Associate exam the first time. Please note that this document does not read like an instructional text. It provides a raw, point-to-point list of facts backed by tables and diagrams to help with understanding and retaining the important facts. For easy navigation, the information on each AWS service in this document is organized into the same categories as they are in the AWS Management Console. The scope of coverage of services, and what information is included for each service, is based on feedback from Digital Cloud Training's pool of over 500,000 students who have recently taken the AWS exam. 2022 BONUS MATERIAL - 80 QUIZ QUESTIONS: Review your knowledge and test your understanding with a total of 80 unique quiz questions. This way you can easily measure your learning progress. - SIMULATE THE REAL EXAM: Get FREE access to the online exam simulator from Digital Cloud Training to evaluate your exam readiness. The full-length Practice Tests with 65 UNIQUE questions is timed (130 mins) and scored (passing score is 72%) mimicking the real exam environment so you get familiar with the AWS exam format. Every question includes deep-dive reference links and detailed explanations that explain why each answer is correct or incorrect. |
| aws data engineer training hyderabad: DATA ENGINEERING WITH AWS COOKBOOK , 2024 |
| aws data engineer training hyderabad: AWS for Solutions Architects - Second Edition Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, 2023-04 Become a master Solutions Architect with this comprehensive guide, featuring cloud design patterns and real-world solutions for building scalable, secure, and highly available systems Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features: Gain expertise in automating, networking, migrating, and adopting cloud technologies using AWS Use streaming analytics, big data, AI/ML, IoT, quantum computing, and blockchain to transform your business Upskill yourself as an AWS solutions architect and explore details of the new AWS certification Book Description: Are you excited to harness the power of AWS and unlock endless possibilities for your business? Look no further than the second edition of AWS for Solutions Architects! Packed with all-new content, this book is a must-have guide for anyone looking to build scalable cloud solutions and drive digital transformation using AWS. With a focus on solution-building and best practices, this updated edition offers in-depth information on the AWS Well-Architected Framework design pillars, cloud-native design patterns, and more. You'll learn about networking in AWS, including edge networking and building hybrid cloud connectivity, as well as big data and streaming data processing with EMR, Glue, Kinesis, and MSK. And with entirely new chapters on CloudOps and emerging technologies like machine learning, IoT, and blockchain, you'll gain valuable insights into how to design industry-standard AWS architectures that meet your organization's technological and business requirements. The book also includes new sections on storage in AWS, containers with ECS and EKS, and data lake patterns, including AWS Lake Formation, lakehouse architecture, and data mesh architecture. Whether you're an experienced solutions architect or just getting started with AWS, this book has everything you need to build cloud-native workloads and enterprise solutions with confidence. What You Will Learn: Optimize your Cloud Workload using the AWS Well-Architected Framework Learn methods to migrate your workload using the AWS Cloud Adoption Framework Apply cloud automation at various layers of application workload to increase efficiency Build a landing zone in AWS and hybrid cloud setups with deep networking techniques Select reference architectures for business scenarios, like data lakes, containers, and serverless apps Apply emerging technologies in your architecture, including AI/ML, IoT and blockchain Who this book is for: This book is for application and enterprise architects, developers, and operations engineers who want to become well versed with AWS architectural patterns, best practices, and advanced techniques to build scalable, secure, highly available, highly tolerant, and cost-effective solutions in the cloud. Existing AWS users are bound to learn the most, but it will also help those curious about how leveraging AWS can benefit their organization. Prior knowledge of any computing language is not needed, and there's little to no code. Prior experience in software architecture design will prove helpful. |
| aws data engineer training hyderabad: AWS Certified Cloud Practitioner iCertify Training, 2019-11-22 Aligned with the latest AWS Cloud Practitioner Certification Guide * 2019Latest Feedback: Chris Franco (Army Veteran)State of Washington, Executive ServicesI completed the AWS Certified Cloud Practitioner course from iCertify Training shortly after transitioning out of the Army and got AWS Cloud certified. It helped me quickly integrate into my organization and serve as an asset in our pursuit of providing the best service possible for our customers and citizens.The AWS certification was a great way to learn the tools and techniques that are being applied in the most competitive industries around the globe. It helped me to rapidly serve as an asset to my organization .Deborah Ecaruan, Manager Training from iCertify NYC - books, videos and practice tests provided me with many useful hints and tips to get the AWS Cloud practitioner certified Katie V, Healthcare - New YorkThe AWS(R) training Capsule consisting of all visual elaborates, presentations, vocabularies and practical exercises is very informative, useful and practical. It is very effective for a person new to Cloud. I was able to pass the certification on my first attempt because of the training.The fundamental-level course is intended for individuals who seek an overall understanding of the AWS Cloud, independent of specific technical roles. It provides a detailed overview of cloud concepts, AWS services, security, architecture, pricing, and support. This course also helps you prepare for the AWS Certified Cloud Practitioner exam.Intended AudienceThis course is intended for: SalesLegalMarketingBusiness analystsProject managersAWS Academy studentsOther IT-related professionalsCourse ObjectivesIn this course, you will learn how to: Define what the cloud is and how it worksDifferentiate between cloud computing and deployment modelsDescribe the AWS Cloud value propositionDescribe the basic global infrastructure of the cloudCompare the different methods of interacting with AWSDescribe and differentiate between AWS service domainsDescribe the Well-Architected FrameworkDescribe basic AWS Cloud architectural principlesExplain the Shared Responsibility modelDescribe security services with the AWS cloudDefine the billing, account management, and pricing models for the AWS platformPrerequisitesWe recommend that attendees of this course have the following prerequisites: General IT technical knowledgeGeneral IT business knowledgeCourse OutlineThis course covers the following concepts: Cloud Concepts IntroductionAWS Core ServicesAWS Enhanced ServicesAWS ArchitectingSecurityPricing and Suppo |
| aws data engineer training hyderabad: Databricks Certified Data Engineer Associate Study Guide Derar Alhussein, 2025-04 Data engineers proficient in Databricks are currently in high demand. As organizations gather more data than ever before, skilled data engineers on platforms like Databricks become critical to business success. The Databricks Data Engineer Associate certification is proof that you have a complete understanding of the Databricks platform and its capabilities, as well as the essential skills to effectively execute various data engineering tasks on the platform. In this comprehensive study guide, you will build a strong foundation in all topics covered on the certification exam, including the Databricks Lakehouse and its tools and benefits. You'll also learn to develop ETL pipelines in both batch and streaming modes. Moreover, you'll discover how to orchestrate data workflows and design dashboards while maintaining data governance. Finally, you'll dive into the finer points of exactly what's on the exam and learn to prepare for it with mock tests. Author Derar Alhussein teaches you not only the fundamental concepts but also provides hands-on exercises to reinforce your understanding. From setting up your Databricks workspace to deploying production pipelines, each chapter is carefully crafted to equip you with the skills needed to master the Databricks Platform. By the end of this book, you'll know everything you need to ace the Databricks Data Engineer Associate certification exam with flying colors, and start your career as a certified data engineer from Databricks! You'll learn how to: Use the Databricks Platform and Delta Lake effectively Perform advanced ETL tasks using Apache Spark SQL Design multi-hop architecture to process data incrementally Build production pipelines using Delta Live Tables and Databricks Jobs Implement data governance using Databricks SQL and Unity Catalog Derar Alhussein is a senior data engineer with a master's degree in data mining. He has over a decade of hands-on experience in software and data projects, including large-scale projects on Databricks. He currently holds eight certifications from Databricks, showcasing his proficiency in the field. Derar is also an experienced instructor, with a proven track record of success in training thousands of data engineers, helping them to develop their skills and obtain professional certifications. |
| aws data engineer training hyderabad: Ultimate Data Engineering with Databricks Mayank Malhotra, 2024-02-14 Navigating Databricks with Ease for Unparalleled Data Engineering Insights. KEY FEATURES ● Navigate Databricks with a seamless progression from fundamental principles to advanced engineering techniques. ● Gain hands-on experience with real-world examples, ensuring immediate relevance and practicality. ● Discover expert insights and best practices for refining your data engineering skills and achieving superior results with Databricks. DESCRIPTION Ultimate Data Engineering with Databricks is a comprehensive handbook meticulously designed for professionals aiming to enhance their data engineering skills through Databricks. Bridging the gap between foundational and advanced knowledge, this book employs a step-by-step approach with detailed explanations suitable for beginners and experienced practitioners alike. Focused on practical applications, the book employs real-world examples and scenarios to teach how to construct, optimize, and maintain robust data pipelines. Emphasizing immediate applicability, it equips readers to address real data challenges using Databricks effectively. The goal is not just understanding Databricks but mastering it to offer tangible solutions. Beyond technical skills, the book imparts best practices and expert tips derived from industry experience, aiding readers in avoiding common pitfalls and adopting strategies for optimal data engineering solutions. This book will help you develop the skills needed to make impactful contributions to organizations, enhancing your value as data engineering professionals in today's competitive job market. WHAT WILL YOU LEARN ● Acquire proficiency in Databricks fundamentals, enabling the construction of efficient data pipelines. ● Design and implement high-performance data solutions for scalability. ● Apply essential best practices for ensuring data integrity in pipelines. ● Explore advanced Databricks features for tackling complex data tasks. ● Learn to optimize data pipelines for streamlined workflows. WHO IS THIS BOOK FOR? This book caters to a diverse audience, including data engineers, data architects, BI analysts, data scientists and technology enthusiasts. Suitable for both professionals and students, the book appeals to those eager to master Databricks and stay at the forefront of data engineering trends. A basic understanding of data engineering concepts and familiarity with cloud computing will enhance the learning experience. TABLE OF CONTENTS 1. Fundamentals of Data Engineering 2. Mastering Delta Tables in Databricks 3. Data Ingestion and Extraction 4. Data Transformation and ETL Processes 5. Data Quality and Validation 6. Data Modeling and Storage 7. Data Orchestration and Workflow Management 8. Performance Tuning and Optimization 9. Scalability and Deployment Considerations 10. Data Security and Governance Last Words Index |
| aws data engineer training hyderabad: Amazon SageMaker Best Practices Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode, 2021-09 Overcome advanced challenges in building end-to-end ML solutions by leveraging the capabilities of Amazon SageMaker for developing and integrating ML models into production Key Features: Learn best practices for all phases of building machine learning solutions - from data preparation to monitoring models in production Automate end-to-end machine learning workflows with Amazon SageMaker and related AWS Design, architect, and operate machine learning workloads in the AWS Cloud Book Description: Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows. What You Will Learn: Perform data bias detection with AWS Data Wrangler and SageMaker Clarify Speed up data processing with SageMaker Feature Store Overcome labeling bias with SageMaker Ground Truth Improve training time with the monitoring and profiling capabilities of SageMaker Debugger Address the challenge of model deployment automation with CI/CD using the SageMaker model registry Explore SageMaker Neo for model optimization Implement data and model quality monitoring with Amazon Model Monitor Improve training time and reduce costs with SageMaker data and model parallelism Who this book is for: This book is for expert data scientists responsible for building machine learning applications using Amazon SageMaker. Working knowledge of Amazon SageMaker, machine learning, deep learning, and experience using Jupyter Notebooks and Python is expected. Basic knowledge of AWS related to data, security, and monitoring will help you make the most of the book. |
| aws data engineer training hyderabad: Azure Data Factory by Example Richard Swinbank, 2021 Data engineers who need to hit the ground running will use this book to build skills in Azure Data Factory v2 (ADF). The tutorial-first approach to ADF taken in this book gets you working from the first chapter, explaining key ideas naturally as you encounter them. From creating your first data factory to building complex, metadata-driven nested pipelines, the book guides you through essential concepts in Microsoft's cloud-based ETL/ELT platform. It introduces components indispensable for the movement and transformation of data in the cloud. Then it demonstrates the tools necessary to orchestrate, monitor, and manage those components. The hands-on introduction to ADF found in this book is equally well-suited to data engineers embracing their first ETL/ELT toolset as it is to seasoned veterans of Microsoft's SQL Server Integration Services (SSIS). The example-driven approach leads you through ADF pipeline construction from the ground up, introducing important ideas and making learning natural and engaging. SSIS users will find concepts with familiar parallels, while ADF-first readers will quickly master those concepts through the book's steady building up of knowledge in successive chapters. Summaries of key concepts at the end of each chapter provide a ready reference that you can return to again and again. You will: Create pipelines, activities, datasets, and linked services Build reusable components using variables, parameters, and expressions Move data into and around Azure services automatically Transform data natively using ADF data flows and Power Query data wrangling Master flow-of-control and triggers for tightly orchestrated pipeline execution Publish and monitor pipelines easily and with confidence. |
| aws data engineer training hyderabad: Data Lake Analytics on Microsoft Azure Harsh Chawla, Pankaj Khattar, 2020-11-15 Get a 360-degree view of how the journey of data analytics solutions has evolved from monolithic data stores and enterprise data warehouses to data lakes and modern data warehouses. You will This book includes comprehensive coverage of how: To architect data lake analytics solutions by choosing suitable technologies available on Microsoft Azure The advent of microservices applications covering ecommerce or modern solutions built on IoT and how real-time streaming data has completely disrupted this ecosystem These data analytics solutions have been transformed from solely understanding the trends from historical data to building predictions by infusing machine learning technologies into the solutions Data platform professionals who have been working on relational data stores, non-relational data stores, and big data technologies will find the content in this book useful. The book also can help you start your journey into the data engineer world as it provides an overview of advanced data analytics and touches on data science concepts and various artificial intelligence and machine learning technologies available on Microsoft Azure. What Will You Learn You will understand the: Concepts of data lake analytics, the modern data warehouse, and advanced data analytics Architecture patterns of the modern data warehouse and advanced data analytics solutions Phases—such as Data Ingestion, Store, Prep and Train, and Model and Serve—of data analytics solutions and technology choices available on Azure under each phase In-depth coverage of real-time and batch mode data analytics solutions architecture Various managed services available on Azure such as Synapse analytics, event hubs, Stream analytics, CosmosDB, and managed Hadoop services such as Databricks and HDInsight Who This Book Is For Data platform professionals, database architects, engineers, and solution architects |
| aws data engineer training hyderabad: Data Pipelines Pocket Reference James Densmore, 2021 Data pipelines are the foundation for success in data analytics and machine learning. Moving data from many diverse sources and processing it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as data pipeline design patterns, data ingestion implementation, data transformation, the orchestration of pipelines, and build versus buy decision making. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support machine learning and analytics needs Considerations for pipeline maintenance, testing, and alerting. |
AWS Management Console
Manage your AWS cloud resources easily through a web-based interface using the AWS Management Console.
Cloud Computing Services - Amazon Web Services (AWS)
Amazon Q is the generative AI-powered assistant from AWS that helps you streamline processes, enhance decision making, and boost productivity. Amazon Q has many new capabilities: Build …
What is AWS? - Cloud Computing with AWS - Amazon Web Services
For over 17 years, AWS has been delivering cloud services to millions of customers around the world running a wide variety of use cases. AWS has the most operational experience, at …
Free Cloud Computing Services - AWS Free Tier
Gain hands-on experience with the AWS platform, products, and services for free with the AWS Free Tier offerings. Browse 100 offerings for AWS free tier services.
Getting Started - Cloud Computing Tutorials for Building on AWS
Learn the fundamentals and start building on AWS now · Get to Know the AWS Cloud · Launch Your First Application · Visit the technical resource centers.
Welcome to AWS Documentation
Welcome to AWS Documentation
Sign in to the AWS Management Console - AWS Sign-In
Learn how to sign in to your AWS account and what credentials are required. Includes tutorials on how to sign in to the AWS Management Console as a root user and IAM users, and how to …
AWS Training and Certification
Begin learning by accessing 600+ free digital courses, curated by the experts at AWS. Unlock diverse lab experiences and more by becoming an AWS Skill Builder subscriber.
How to Create an AWS Account
Creating an account is the starting point to provide access to AWS services and resources. Follow these steps to set up your account.
Getting Started with AWS Cloud Essentials
Gain familiarity with core concepts of cloud computing and the AWS Cloud. Get the answers to common questions about cloud computing and explore best practices for building on AWS.
AWS Management Console
Manage your AWS cloud resources easily through a web-based interface using the AWS Management Console.
Cloud Computing Services - Amazon Web Services (AWS)
Amazon Q is the generative AI-powered assistant from AWS that helps you streamline processes, enhance decision making, and boost productivity. Amazon Q has many new capabilities: Build …
What is AWS? - Cloud Computing with AWS - Amazon Web …
For over 17 years, AWS has been delivering cloud services to millions of customers around the world running a wide variety of use cases. AWS has the most operational experience, at …
Free Cloud Computing Services - AWS Free Tier
Gain hands-on experience with the AWS platform, products, and services for free with the AWS Free Tier offerings. Browse 100 offerings for AWS free tier services.
Getting Started - Cloud Computing Tutorials for Building on AWS
Learn the fundamentals and start building on AWS now · Get to Know the AWS Cloud · Launch Your First Application · Visit the technical resource centers.
Welcome to AWS Documentation
Welcome to AWS Documentation
Sign in to the AWS Management Console - AWS Sign-In
Learn how to sign in to your AWS account and what credentials are required. Includes tutorials on how to sign in to the AWS Management Console as a root user and IAM users, and how to …
AWS Training and Certification
Begin learning by accessing 600+ free digital courses, curated by the experts at AWS. Unlock diverse lab experiences and more by becoming an AWS Skill Builder subscriber.
How to Create an AWS Account
Creating an account is the starting point to provide access to AWS services and resources. Follow these steps to set up your account.
Getting Started with AWS Cloud Essentials
Gain familiarity with core concepts of cloud computing and the AWS Cloud. Get the answers to common questions about cloud computing and explore best practices for building on AWS.
