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Whether you're a novice or an experienced professional, you'll learn by doing with real-world examples and scenarios.

Discover important topics, including serverless designs, data migration with AWS DMS, containerization with Docker, ECS, and EKS, and more. Learn how to use Lambda and the Snow Family to handle big data workloads efficiently, ensure security, and automate procedures on Amazon Web Services.

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Whether you're a novice or an experienced professional, you'll learn by doing with real-world examples and scenarios.

Discover important topics, including serverless designs, data migration with AWS DMS, containerization with Docker, ECS, and EKS, and more. Learn how to use Lambda and the Snow Family to handle big data workloads efficiently, ensure security, and automate procedures on Amazon Web Services.

This course will allow you to not only gain industry-leading skills but also prepare for AWS certification exams, putting you in line for top data engineering opportunities. You will leave with the knowledge and confidence to tackle even the most difficult cloud challenges.

Enroll today! Learn about AWS Databases & Compute for Data Engineers and become the cloud specialist that employers are looking for!

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What's inside

Syllabus

Mastering AWS Database Services
Explore key AWS database services like DynamoDB, RDS, and DocumentDB, learning to work with APIs, indexes, and securing data. Gain hands-on experience with DynamoDB Accelerator (DAX) and learn best practices for scaling and securing databases.
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Career center

Learners who complete AWS Databases & Compute for Data Engineers will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in AWS Databases & Compute for Data Engineers.
Is widely considered the most comprehensive guide to data modeling with Amazon DynamoDB. It's highly recommended for both newcomers and experienced users looking to master advanced patterns like single-table design. The book includes detailed walkthroughs and real-world examples, making it an invaluable reference for anyone building applications with DynamoDB. It's particularly useful for understanding the nuances of NoSQL data modeling compared to relational databases.
Co-authored by AWS Serverless Hero Yan Cui, Peter Sbarski, and AWS Lambda director Ajay Nair, the second edition is the flagship reference on event-driven FaaS choreography, Step Functions orchestration, cold-start mitigation, and serverless security.
Considered a modern classic, this book delves into the fundamental trade-offs and concepts behind building robust, scalable, and maintainable data systems. While not exclusively about data engineering, its in-depth coverage of distributed systems, databases, and data processing patterns is essential for any data professional looking to deepen their understanding. It is highly valuable for undergraduate and graduate students, as well as working professionals.
Published by O'Reilly in 2024, guiding enterprise engineering teams through domain-driven serverless design, EventBridge event meshes, idempotency, FinOps cost modeling, and observability at production scale.
Provides a comprehensive overview of cloud computing, covering concepts, technologies, and architectures. It is written by leading experts in the field.
Is an excellent starting point for anyone new to AWS and looking to gain a broad understanding of the cloud. It covers the fundamental concepts of AWS, including its value proposition, core services, security, architecture, pricing, and billing. This foundational text, ideal for those preparing for the Cloud Practitioner certification and provides necessary background knowledge for further certifications. It is commonly used as a study guide for the CLF-C02 exam.
Authored by AWS Senior Solutions Architects, this guide provides a thorough exploration of DynamoDB's features and capabilities. It's an excellent resource for understanding how DynamoDB works, optimizing performance, and integrating with other AWS services. The book is suitable for those transitioning from relational databases and covers essential data modeling techniques and advanced topics.
Draws on years of production experience running MindMup on AWS Lambda and API Gateway to teach pragmatic request routing, asynchronous background worker queues, local testing, and zero-downtime deployments.
This guide is for experienced professionals seeking the Solutions Architect Professional certification. It covers complex architectural scenarios, migration strategies, cost optimization, and security best practices at an enterprise level. must-read for those aiming for the highest level of solutions architecture expertise on AWS.
Moving beyond the fundamentals, this study guide is crucial for those aiming for the Solutions Architect Associate certification. It delves into designing resilient, high-performing, secure, and cost-optimized architectures on AWS. key resource for deepening understanding of how AWS services can be used together in practical scenarios and is widely used for SAA-C03 exam preparation.
Equips senior architects to design antifragile serverless systems using bounded autonomous services, CQRS event-stream processors, multi-regional replication, micro-frontends, and production observability while avoiding distributed anti-patterns.
Focuses on distributed saga transactions, circuit breakers, dead-letter queue recovery, event-driven state machines, and multi-region resilience patterns across decoupled serverless microservices.
Is tailored for developers seeking to validate their skills in developing and maintaining applications on AWS. It covers key AWS services from a developer's perspective, focusing on deployment, security, and debugging. This valuable resource for those preparing for the Developer Associate certification and provides practical knowledge for building cloud-native applications.
Covers the latest AWS certification exam blueprint and provides comprehensive coverage of all exam topics. It is an excellent resource for anyone preparing for the AWS Certified DevOps Engineer - Professional exam.
Another comprehensive guide to Apache Spark, co-authored by one of its creators. definitive resource for learning Spark for big data processing, covering its various components and APIs. It's a must-read for anyone serious about using Spark in their data engineering work, suitable for undergraduate, graduate, and professional levels.
Explains the AWS Lambda execution environment lifecycle, Firecracker microVM isolation, warm vs. cold invocations, IAM least-privilege execution roles, and asynchronous event source mappings from the ground up.
For those specializing in data, this study guide focuses on the AWS Data Analytics Specialty certification. It covers collecting, storing, processing, analyzing, and visualizing data on AWS. is valuable for data professionals looking to validate their expertise in AWS data services.
Provides a comprehensive guide to navigating the complexities of data migration, offering practical strategies for transitioning data to new platforms and architectures. It presents a structured approach covering planning, assessment, data profiling, quality checks, design, and post-migration validation. This book is highly relevant for gaining a solid understanding of the data migration process.
Apache Spark widely used engine for big data processing. provides a comprehensive introduction to Spark, covering its core concepts and APIs. It's essential for anyone working with big data pipelines and aligns with courses mentioning Spark. The 3rd edition, published in 2020, covers recent features and improvements.
Establishes the foundational principles of log-based message brokers, idempotent consumers, eventual consistency, and stream processing that govern every event-driven serverless architecture.
Security is paramount in the cloud. dives deep into AWS security services and best practices, essential for anyone designing or managing workloads on AWS. It's particularly relevant for those pursuing security-focused roles or certifications.
Aligning with the Google Cloud focused course titles, this book provides guidance on performing data engineering tasks on GCP. It's a valuable resource for those preparing for the Google Cloud Professional Data Engineer exam or working with GCP data services. It covers the relevant tools and services offered by Google Cloud.
Demonstrates how immutable event streams, materialized state views, and asynchronous choreography decouple stateless serverless compute functions from stateful downstream consumers.
Offers a practical, step-by-step guide to data migration. It covers the entire migration lifecycle, from planning and design to execution and validation. It's a valuable resource for understanding the practical aspects and common challenges of data migration projects.
Focuses specifically on applying design patterns for building efficient and scalable applications with DynamoDB. It's a valuable resource for developers looking to move beyond the basics and implement best practices for data modeling and access patterns in DynamoDB. It helps solidify understanding through practical examples of design patterns.
Provides a practical, hands-on approach to learning AWS. It covers core services through real-world examples and use cases, making it excellent for gaining a deeper understanding of how AWS works in practice. While not strictly a certification guide, it's a great complementary resource for solidifying knowledge.
Focuses specifically on the process of migrating databases. It covers various aspects of database migration, including planning, execution, and validation. It practical guide for database administrators and developers involved in database migration projects.
Compares Function-as-a-Service (FaaS), Backend-as-a-Service (BaaS), and container deployment models, showing architects how to define clean service boundaries and avoid distributed monoliths.
Python fundamental language in data engineering. focuses on using Python for various data engineering tasks, including building data pipelines and working with large datasets. It is particularly useful for those whose background is in Python and want to apply their skills to data engineering, aligning with courses mentioning Python for Data Engineering. Published in 2020, it's a relatively recent resource.
Broadens serverless architecture beyond hyperscaler FaaS by teaching edge-first system design with V8-isolate Cloudflare Workers, Durable Objects, D1 SQL databases, R2 object storage, and asynchronous Queues with near-zero cold starts.
Dbt (data build tool) popular tool in the modern data stack for transforming data in the warehouse. focuses on using dbt for data engineering workflows, aligning with courses mentioning dbt. It's particularly relevant for professionals and graduate students working with cloud data warehouses and ELT processes.
Understanding how databases work internally is crucial for data engineers. provides a detailed look into the design and implementation of databases and distributed data systems. It's a highly technical book, best suited for graduate students and experienced professionals who want to deepen their understanding of the foundational technologies they work with.
Offers a structured framework for data migration, drawing on real-world experience. It covers key components of a migration project, including scope, data quality, integration, and testing. It provides a practical approach to navigating the complexities of data migration.
Provides a theoretical foundation for cloud computing. It covers topics such as cloud architectures, cloud resource management, and cloud security.
While focused on data warehousing, this book classic in the field of ETL (Extract, Transform, Load), which fundamental process in many data migration scenarios. It provides in-depth knowledge of data extraction, transformation, and loading techniques that are directly applicable to data migration projects. valuable reference for understanding the technical underpinnings of data movement.
Bridges Kubernetes clusters and serverless workflows through Knative Serving and Eventing, showing architects how to implement scale-to-zero containers, CloudEvents routing, traffic revisions, and objective criteria for avoiding FaaS lock-in.
This practical guide focuses specifically on Docker, the leading containerization platform. It's a great resource for developers who want to get hands-on experience with building and deploying containerized applications.
Practical guide to cloud computing. It covers a wide range of topics, including cloud computing basics, cloud architectures, and cloud security.
Provides a comprehensive overview of Kubernetes Operators, a powerful mechanism for automating the management of complex Kubernetes applications. It's a great choice for anyone looking to gain a deep understanding of the topic.
Cuts through vendor marketing hype to evaluate the organizational, economic, and architectural realities of migrating legacy enterprise workloads to managed serverless platforms.
Explores common cloud design patterns and how to implement them on AWS. It helps in building well-architected and efficient solutions. It's a good resource for deepening architectural design skills.
Is specifically tailored to data migration within the SAP ecosystem, particularly for SAP S/4HANA and cloud solutions. Given the prevalence of SAP in enterprise environments, this book is highly relevant for those dealing with SAP data migration projects. It covers specific tools and methodologies used in SAP migrations.
Articulates the economic and architectural shift from server provisioning to managed cloud leverage, guiding engineering leaders through total cost of ownership modeling, build-versus-buy trade-offs, and production serverless migration strategy.
Covers the fundamental principles and best practices for building scalable data systems in the big data era. It provides a broader perspective on designing data architectures that can handle large volumes of data and high traffic, valuable for undergraduates, graduates, and professionals involved in system design.
Catalogs sixty-five timeless messaging patterns—including Content-Based Router, Splitter, Aggregator, and Dead Letter Channel—that map directly onto modern serverless workflows in AWS Step Functions and EventBridge.
Delves into designing and building serverless applications on AWS using services like Lambda, API Gateway, and DynamoDB. It's relevant for developers and architects interested in this contemporary architectural approach.
Guides cloud engineers through end-to-end AWS serverless application construction, combining AWS Lambda, API Gateway, DynamoDB, SNS/SQS asynchronous messaging, caching layers, and CI/CD deployment workflows for cost-efficient production systems.

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