 Название: Ultimate AWS Data Engineering: Design, Implement and Optimize Scalable Data Solutions on AWS with Practical Workflows and Visual Aids for Unmatched Impact
Автор: Rathish Mohan, Shekhar Agrawal, Srinivasa Sunil Chippada
Издательство: Orange Education Pvt Ltd, AVA
Год: 2025
Страниц: 460
Язык: английский
Формат: epub (true)
Размер: 45.1 MB
Unlock the Power of AWS Data Engineering and Build Smarter Pipelines for Data-Driven Success.
Key Features:
- Gain an in-depth understanding of essential AWS services such as S3, DynamoDB, Redshift, and Glue to build scalable data solutions.
- Learn to design efficient, fault-tolerant data pipelines while adhering to best practices in cost management and security.
Book Description:
In today’s data-driven era, mastering AWS data engineering is key to building scalable, secure pipelines that drive innovation and decision-making. Ultimate AWS Data Engineering is your comprehensive guide to mastering the art of building robust, cost-effective, and fault-tolerant data pipelines on AWS. Designed for data professionals and enthusiasts, this book begins with foundational concepts and progressively explores advanced techniques, equipping you with the skills to tackle real-world challenges.
Throughout the chapters, you’ll dive deep into the core principles of data replication, partitioning, and load balancing, while gaining hands-on experience with AWS services like S3, DynamoDB, Redshift, and Glue. Learn to design resilient data architectures, optimize performance, and ensure seamless data transformation—all while adhering to best practices in cost-efficiency and security.
Whether you aim to streamline your organization’s data flow, enhance your cloud expertise, or future-proof your career in data engineering, this comprehensive guide offers the practical knowledge and insights you need to succeed. By the end, you will be ready to craft impactful, data-driven solutions on AWS with confidence and expertise.
Data engineering is the practice of building, maintaining, and automating the infrastructure and processes used to collect, store, process, analyze, and interpret data. It involves designing and implementing data pipelines, which are the workflows that move data through various stages of the data lifecycle. Data engineers are responsible for a variety of tasks, including:
• Data Ingestion: Acquiring data from various sources, such as databases, APIs, and sensors.
• Data Storage: Choosing and managing storage solutions for different data types and needs.
• Data Processing: Cleaning, transforming, and enriching data to prepare it for analysis.
• Data Analysis: Using tools and techniques to extract insights and patterns from data.
• Data Reporting: Creating and maintaining reports, dashboards, and visualizations for stakeholders.
• Data Pipelines: Designing, building, and automating workflows to move data through various stages.
• Data Governance: Establishing policies and procedures to ensure data quality, security, and compliance.
By effectively managing the data lifecycle, data engineers play a critical role in enabling data-driven decision-making. They provide valuable insights that can inform strategic business decisions, optimize operations, and drive innovation across various industries.
What you will learn:
- Design scalable data pipelines using core AWS data engineering tools.
- Master data replication, partitioning, and sharding techniques on AWS.
- Build fault-tolerant architectures with AWS scalability and reliability.
Who is this book for?
This book is tailored for aspiring and experienced data engineers, cloud architects, and IT professionals aiming to master AWS data engineering. Whether you are new to the field or looking to enhance your expertise, this comprehensive guide equips you with the skills to design, implement, and optimize scalable data solutions on AWS.
Contents:
1. Unveiling the Secrets of Data Engineering
2. Architecting for Scalability: Data Replication Techniques
3. Partitioning and Sharding: Optimizing Data Management
4. Ensuring Consistency: Consensus Mechanisms and Models
5. Balancing the Load: Achieving Performance and Efficiency
6. Building Fault-Tolerant Architectures
7. Exploring the Realm of AWS Data Storage Services
8. Orchestrating Data Flow
9. Advanced Data Pipelines and Transformation
10. Data Warehousing Demystified
11. Visualizing the Unseen
12. AWS Machine Learning: Classic AI to Generative AI
13. Advanced Data Engineering with AWS
Index
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Ultimate AWS Data Engineering: Design, Implement and Optimize Scalable Data Solutions on AWS with Practical Workflows 
