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268333295
AWS Data Engineering Cookbook: Practical Recipes for Scalable Data Pipelines and Analytics on AWS
Item #:
268333295
ETB 7535
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AWS Data Engineering Cookbook delivers 80 practical, hands-on recipes designed to help you build real-world cloud-native data engineering systems using AWS services.
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- AWS Data Engineering Cookbook: Practical Recipes for Scalable Data Pipelines and Analytics on AWS80 Hands-On Recipes + Source Code RepositoryBuilding scalable data pipelines and analytics platforms on AWS can quickly become complex. Modern data engineering requires far more than storing data—it involves building secure data lakes, orchestrating ETL workflows, processing streaming data, optimizing analytics performance, automating monitoring, and supporting AI-ready architectures across cloud environments.The challenge is not simply learning AWS services individually. The challenge is understanding how to combine them into reliable, scalable, secure, and cost-efficient data engineering solutions.This book solves that problem.AWS Data Engineering Cookbook delivers 80 practical, hands-on recipes designed to help you build real-world cloud-native data engineering systems using AWS services. Each recipe walks you step-by-step from problem to implementation using production-style architectures, AWS CLI commands, automation workflows, and operational best practices.A dedicated GitHub source code repository is included with scripts, ETL jobs, reusable templates, infrastructure configurations, and implementation examples aligned with the recipes throughout the book.You will learn how to build and optimize:Modern Amazon S3 data lakes and lakehouse architecturesETL and metadata pipelines using AWS GlueInteractive analytics workflows using Amazon AthenaEnterprise data warehouses with Amazon RedshiftReal-time streaming pipelines using Amazon KinesisServerless data engineering workflows with Lambda and EventBridgeBig data processing architectures using Amazon EMR and SparkGovernance, encryption, auditing, and compliance solutionsAI-ready analytics and machine learning data platformsHow This Book Is OrganizedThe book progresses from AWS data engineering foundations, governance, security, and cost optimization into modern S3-based data lakes, ETL and metadata management with AWS Glue, serverless analytics with Athena, enterprise warehousing with Redshift, real-time streaming architectures, serverless ETL pipelines, big data processing with EMR and Spark, governance and compliance automation, and modern AI-ready lakehouse architectures.By the end of the book, you will have a practical understanding of how to build scalable cloud-native data engineering platforms on AWS.Who This Book Is ForThis book is designed for:Data engineers building ETL, ELT, and analytics pipelinesCloud, DevOps, SysOps, and CloudOps engineersSolution architects designing scalable data platformsBig data engineers working with Spark and streaming systemsAnalytics engineers building reporting and BI environmentsAI and ML practitioners preparing data pipelines for machine learning workflowsAWS certification learners seeking hands-on implementation experienceBuild Real AWS Data Engineering SolutionsThis is a practical, implementation-focused guide—not a theory-only reference.You will gain hands-on experience building, automating, securing, monitoring, and optimizing modern AWS data engineering platforms using real-world architectures and operational workflows.Start building today and turn AWS data engineering concepts into production-ready cloud solutions.
| Publisher | Independently published |
| Publication date | May 24, 2026 |
| Language | English |
| Print length | 570 pages |
| ISBN-13 | 979-8198450660 |
| Item Weight | 3.52 pounds (1.6 kg) |
| Dimensions | 8.5 x 1.29 x 11 inches (21.6 x 3.3 x 27.9 cm) |
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AWS Data Engineering Cookbook: Practical Recipes for Scalable Data Pipelines and Analytics on AWS
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Features & Benefits
- Includes 80 hands-on recipes for cloud-native data engineering.
- Covers modern architectures like S3 data lakes and lakehouse structures.
- Teaches ETL workflows and interactive analytics with AWS Glue and Athena.
- Features real-time streaming with Kinesis and serverless workflows using Lambda.
- Provides a dedicated GitHub repository for practical coding resources.
- Ideal for data engineers, architects, and AWS learners seeking applied knowledge.
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