• Workshop: Hands-on Labs for Amazon DynamoDB
    • 1. Getting Started
      • Prerequisites
      • Set Up
      • Launch Cloud9 IDE
      • Create the DynamoDB Tables
      • Load Sample Data
    • 2. Explore DynamoDB with the CLI
      • Read Sample Data
      • Reading Item Collections using Query
      • Working with Table Scans
      • Inserting/Updating Data
      • Deleting Data
      • Transactions
      • Global Secondary Indexes
    • 3. Explore the DynamoDB Console
      • Viewing Table Data
      • Reading Item Collections using Query
      • Working with Table Scans
      • Modifying Data
      • Global Secondary Indexes
    • 4. Backups
      • AWS Backup Recap
      • Point-In-Time Recovery Backup
      • On-Demand Backup
      • Scheduled Backup
      • Restrict Backup Deletion
      • Cleaning Up The Resources
    • 5. Relational Modeling & Migration
      • Exercise Overview
      • Create Key Pair
      • Configure MySQL Environment
      • Explore Source Model
      • Explore Target Model
      • Load DynamoDB Table
      • Access DynamoDB Table
    • Cleanup
  • Workshop: Modeling Game Player Data with Amazon DynamoDB
    • 1. Getting Started
      • Setup AWS Cloud9 IDE
      • Obtain & Review Code
    • 2. Plan your data model
      • Best Practices
      • ER diagram (ERD)
      • Review Access Patterns
    • 3. Core usage: user profiles and games
      • Design the primary key
      • Create the table
      • Bulk-load data
      • Retrieve Item collections
    • 4. Find open games
      • Model a sparse GSI
      • Create a sparse GSI
      • Query the sparse GSI
      • Scan the sparse GSI
    • 5. Join and close games
      • Add users to a game
      • Start a game
    • 6. View past games
      • Add an inverted index
      • Retrieve games for a user
    • Summary & Cleanup
  • Workshop: Advanced Design Patterns for Amazon DynamoDB
    • Start here: Setup
      • Step 1 - Open the AWS Systems Manager Console
      • Step 2 - Check the Python and AWS CLI installation
      • Step 3 - Check boto3 installation
      • Step 4 - Check the content of the workshop folder
      • Step 5 - Check the files format and content
      • Step 6 - Preload the items for the table Scan exercise
    • Exercise 1: DynamoDB Capacity Units and Partitioning
      • Step 1 - Create the DynamoDB table
      • Step 2 - Load sample data into the table
      • Step 3 - Load a larger file to compare the execution times
      • Step 4 - View the CloudWatch metrics on your table
      • Step 5 - Increase the capacity of the table
      • Step 6 - After increasing the table’s capacity, load more data
      • Step 7 - Create a new table with a low-capacity global secondary index
    • Exercise 2: Sequential and Parallel Table Scans
      • Step 1 - Execute a sequential Scan
      • Step 2 - Execute a parallel Scan
    • Exercise 3: Global Secondary Index Write Sharding
      • Step 1 - Creating the GSI
      • Step 2 - Querying the GSI with shards
    • Exercise 4: Global Secondary Index Key Overloading
      • Step 1 - Create the employees table for global secondary index key overloading
      • Step 2 - Load data into the new table
      • Step 3 - Query the employees table using the global secondary index with overloaded attributes
    • Exercise 5: Sparse Global Secondary Indexes
      • Step 1 - Add a new global secondary index to the employees table
      • Step 2 - Scan the employees table to find managers without using the sparse global secondary index
      • Step 3 - Scan the employees table to find managers by using the sparse global secondary index
    • Exercise 6: Composite Keys
      • Step 1 - Create a new global secondary index for City-Department
      • Step 2 - Query all the employees from a state
      • Step 3 - Query all the employees of a city
      • Step 4 - Querying all the employees of a city and a specific department
    • Exercise 7: Adjacency Lists
      • Step 1 - Create and load the the InvoiceandBilling table
      • Step 2 - Review the InvoiceAndBills table on the DynamoDB console
      • Step 3 - Query the table's invoice details
      • Step 4 - Query the Customer details and Bill details using the Index
    • Exercise 8: Amazon DynamoDB Streams and AWS Lambda
      • Step 1 - Create the replica table
      • Step 2 - Review the AWS IAM policy for the IAM role
      • Step 3 - Create the Lambda function
      • Step 4 - Enable DynamoDB Streams
      • Step 5 - Map the source stream to the Lambda function
      • Step 6 - Populate the logfile table and verify replication to logfile_replica
  • Workshop: Build a Serverless Event Driven Architecture with DynamoDB
    • Start here: Setup
    • Introduction: Overview
      • AWS Event: Game Rules
      • Optional - Pipeline Deep Dive
    • Lab 1: Connect the pipeline
      • Step 1: Connect StateLambda
      • Step 2: Check MapLambda trigger
      • Step 3: Connect ReduceLambda
    • Lab 2: Ensure fault tolerance and exactly once processing
      • Step 1: Prevent duplicates at StateLambda function
      • Step 2: Ensure idempotency of ReduceLambda function
      • Optional: Add a simple Python frontend to view the data live
    • Summary: Summary & Conclusions
      • Solutions
  • Scenarios: Design Challenges
    • Retail Cart Scenario
      • Retail Cart References
    • Bank Payments Scenario
      • Bank Payments References
  • Links: NoSQL Design: Reference Materials

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Amazon DynamoDB Labs > NoSQL Design: Reference Materials

NoSQL Design: Reference Materials

DynamoDB Design: Reference Materials

DynamoDB Data Model Design:

  • Working with Queries in DynamoDB
  • Advanced Design Patterns for DynamoDB
  • DynamoDB Best Practices for Designing and Architecting with DynamoDB
  • Best Practices for Using Sort Keys to Organize Data
  • Using Global Secondary Indexes in DynamoDB
  • Best Practices for Managing Many-to-Many Relationships

Understanding Distributed Systems and DynamoDB:

  • Amazon DynamoDB Under the Hood: How We Built a Hyper-Scale Database
  • Amazon DynamoDB: How It Works

DynamoDB Related Tools:

  • NoSQL Workbench for Amazon DynamoDB
  • EMR-DynamoDB-Connector: Access data stored in Amazon DynamoDB with Apache Hadoop, Apache Hive, and Apache Spark

Online Training Courses:

  • A Cloud Guru: Amazon DynamoDB Deep Dive
  • A Cloud Guru: Amazon DynamoDB Data Modeling
  • edX: Amazon DynamoDB: Building NoSQL Database-Driven Applications