Data Engineering With Aws
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Data Engineering with AWS
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.
Advanced Data Engineering with AWS: Building Scalable and Reliable Data Pipelines 2025
Author: AUTHOR :1- GAYATRI TAVVA, AUTHOR :2 - DR PRIYANKA KAUSHIK
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
PREFACE The exponential growth of data has redefined the way organizations operate, compete, and innovate. In today’s digital era, businesses are no longer just consumers of data but active participants in building complex, scalable ecosystems that collect, process, store, and derive value from massive data streams. Amazon Web Services (AWS), as the world’s leading cloud platform, offers a robust suite of tools and services that empower enterprises to transform raw data into actionable insights with unprecedented speed and reliability. This book, Advanced Data Engineering on AWS: Building Scalable, Secure, and Intelligent Pipelines, is designed to guide readers through the essential foundations and evolving innovations in data engineering using AWS. It systematically covers the principles and practices needed to architect high-performance data pipelines that can handle modern business demands. The journey begins with establishing the Foundations of Data Engineering in the AWS Ecosystem, helping readers understand how AWS services interplay to create a seamless environment for data management. We then explore Designing Data Pipelines for Scalability and Reliability, focusing on the architectural patterns that ensure resilience and flexibility in an unpredictable data landscape. As data sources become increasingly diverse and dynamic, mastering Data Ingestion Techniques on AWS is critical. We delve into both batch and real-time ingestion strategies, enabling efficient collection of high-velocity data. Coupled with this is Data Storage Optimization using services like S3, Redshift, and Beyond, ensuring that storage solutions align with both performance and cost-efficiency goals. Understanding ETL and ELT on AWS is pivotal for preparing data for downstream analytics and machine learning tasks. Subsequently, Real-Time Data Processing on AWS highlights how to transform and analyze data streams to deliver timely, business-critical insights. Automation becomes key as we address Data Orchestration and Workflow Automation, enabling complex pipelines to run with minimal human intervention. Ensuring trust in data requires rigorous focus on Data Quality and Governance, laying a strong foundation for secure, compliant, and high-fidelity analytics. We further extend this security narrative in Security and Compliance in AWS Data Pipelines, offering a deep dive into encryption, access controls, and regulatory alignment. No modern pipeline is complete without observability; hence, Monitoring, Logging, and Performance Tuning explores techniques to gain actionable insights into pipeline behavior, prevent failures, and optimize operations proactively. In an increasingly globalized world, Advanced Architectures: Multi-Region and Hybrid Pipelines prepares readers for designing architectures that span geographic—es and cloud environments, ensuring data availability and fault tolerance. Finally, we look ahead to Future Trends: AI/ML-Driven Data Engineering on AWS, where artificial intelligence automates data engineering tasks, adaptive pipelines become reality, and next-generation solutions redefine how businesses leverage data at scale. This book aims to serve data engineers, architects, cloud practitioners, and technical leaders who seek to not only build scalable AWS-based systems but also future-proof their architectures in an evolving technology landscape. Through a blend of foundational principles, hands-on techniques, best practices, and forward-looking insights, this book is your comprehensive guide to mastering advanced data engineering on AWS. We invite you to embark on this journey to build the data systems that will power the intelligent enterprises of tomorrow. Authors Gayatri Tavva Dr Priyanka Kaushik
AWS Data Engineering for Modern Analytics
Author: Frank Reiniger
language: en
Publisher: Independently Published
Release Date: 2025-11-05
AWS Data Engineering for Modern Analytics What if your data pipelines didn't break at scale, no surprise bills, no late-night firefights, no silent failures? In a world where cloud-native analytics defines competitive advantage, simply collecting data isn't enough. Enterprises need platforms that are secure, auditable, cost-efficient, and engineered to survive real-world complexity. This book is your practical blueprint for building production-ready data systems on AWS. It strips away hype and focuses on the reality facing modern data teams: how to architect lakes on S3 with intent, how to run Glue and EMR without waste, how to orchestrate with Step Functions and CI/CD instead of ad-hoc scripts, and how to design pipelines that evolve safely as your business grows. At its heart, this guide solves the biggest challenge in cloud data engineering-moving from prototypes that "work" to platforms you can trust with mission-critical workloads. You will learn how to: Structure S3 data lakes with the right formats, partitions, and lifecycle rules Build incremental ETL pipelines with Glue that handle schema changes and retries Implement real-time streaming with Kinesis and Flink for event-driven analytics Design secure, governed environments with IAM, Lake Formation, and encryption Deliver ML-ready feature pipelines and integrate with SageMaker Observe pipeline health, enforce SLAs, and prevent silent data drift Deploy reliable infrastructure using Terraform/CloudFormation and automated CICD Through hands-on labs and real deployment patterns, you'll master the engineering fundamentals behind cost control, operational resilience, metadata design, multi-environment workflows, disaster recovery, and future-proof storage formats like Apache Iceberg. If you're a data engineer, architect, analytics leader, or cloud practitioner committed to building systems that don't crumble under real workloads, this book will elevate your execution and confidence. Build with precision. Ship with certainty. Own your data platform, not the other way around. Get your copy and start engineering AWS pipelines the right way, today.