Chapter 4: Integrating with DynamoDB

In this section

• 4.1 Overview of AWS DynamoDB
  • DynamoDB Data Model: A Comprehensive Guide for Java and Clojure Developers
    Explore the intricacies of AWS DynamoDB's data model, including tables, items, attributes, and primary keys, and learn how to leverage its schemaless nature for flexible data solutions.
  • DynamoDB Benefits: Scalability, Cost Efficiency, and AWS Integration
    Explore the benefits of using AWS DynamoDB, including scalability, cost efficiency, and seamless integration with AWS services, for building robust and scalable applications.
• 4.2 Provisioning DynamoDB Tables and Capacity Planning
  • Creating Tables with Provisioned and On-Demand Capacity Modes in DynamoDB
    Explore the intricacies of creating DynamoDB tables with provisioned and on-demand capacity modes, including step-by-step instructions and best practices for Java and Clojure developers.
  • Managing Read and Write Capacity Units (RCUs and WCUs) in DynamoDB
    Learn how to effectively manage Read and Write Capacity Units (RCUs and WCUs) in AWS DynamoDB to optimize performance and cost.
• 4.3 Accessing DynamoDB from Clojure Using Amazonica
  • Amazonica Library: Simplifying AWS Integrations with Clojure
    Explore the Amazonica library, a powerful AWS SDK wrapper for Clojure, designed to simplify AWS service integrations with idiomatic functions. Learn how to add Amazonica to your project using Leiningen and leverage its capabilities for seamless cloud interactions.
  • AWS Credentials and Client Configuration for Clojure
    Learn how to configure AWS credentials and clients in Clojure using Amazonica, with a focus on security best practices and practical code examples.
• 4.4 Performing CRUD Operations and Batch Processing
  • Creating and Updating Items in DynamoDB with Clojure
    Master the art of creating and updating items in DynamoDB using Clojure. Learn to use put-item and update-item operations effectively, with condition expressions to manage data integrity.
  • Reading Items in DynamoDB with Clojure: Mastering Data Retrieval
    Explore advanced techniques for reading items from AWS DynamoDB using Clojure, including get-item operations, querying with partition keys, and optimizing data retrieval with filters and projections.
  • Deleting Items in DynamoDB with Clojure: Ensuring Safe and Consistent Data Management
    Explore the intricacies of deleting items in DynamoDB using Clojure, focusing on condition expressions, consistency models, and best practices to prevent accidental data loss.
  • Batch Operations in DynamoDB with Clojure: Enhancing Performance and Throughput
    Explore batch operations in AWS DynamoDB using Clojure. Learn about batch-write-item and batch-get-item, handling unprocessed items, and optimizing performance.
• 4.5 Leveraging DynamoDB Streams for Real-Time Applications
  • Understanding DynamoDB Streams: Real-Time Data Processing for Scalable Applications
    Explore the intricacies of DynamoDB Streams, their architecture, use cases, and integration with Clojure for real-time data processing and synchronization.
  • AWS Lambda Stream Processing with Clojure: Integrating DynamoDB Streams
    Learn how to set up AWS Lambda functions to process DynamoDB Streams using Clojure, including deployment strategies and event handling.
• 4.6 Case Study: Scaling an E-Commerce Backend
  • Designing the Data Model for Products and Orders in E-Commerce with DynamoDB
    Explore the intricacies of designing a scalable data model for e-commerce systems using DynamoDB, focusing on products and orders. Learn about entity modeling, primary keys, indexes, and handling complex relationships.
  • Implementing Shopping Cart Functionality in Clojure with DynamoDB
    Learn how to design and implement a scalable shopping cart functionality using Clojure and DynamoDB, focusing on atomic updates, session management, and handling anonymous users.
  • Handling High Traffic and Scaling: Strategies for Clojure and NoSQL
    Explore strategies for managing high traffic and scaling NoSQL databases with Clojure, including capacity monitoring, auto-scaling, and optimizing data access patterns.