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  • 1. LHOL: Hands-on Labs for Amazon DynamoDB
    • 1.1. Getting Started
      • 1.1.1. Prerequisites and Start
      • 1.1.2. Create the DynamoDB Tables
      • 1.1.3. Load Sample Data
      • 1.1.4. Cleanup
    • 1.2. Explore DynamoDB with the CLI
      • 1.2.1. Read Sample Data
      • 1.2.2. Reading Item Collections using Query
      • 1.2.3. Working with Table Scans
      • 1.2.4. Inserting/Updating Data
      • 1.2.5. Deleting Data
      • 1.2.6. Transactions
      • 1.2.7. Global Secondary Indexes
    • 1.3. Explore the DynamoDB Console
      • 1.3.1. Viewing Table Data
      • 1.3.2. Reading Item Collections using Query
      • 1.3.3. Working with Table Scans
      • 1.3.4. Modifying Data
      • 1.3.5. Global Secondary Indexes
    • 1.4. Backups
      • 1.4.1. AWS Backup Recap
      • 1.4.2. Point-In-Time Recovery Backup
      • 1.4.3. On-Demand Backup
      • 1.4.4. Scheduled Backup
      • 1.4.5. Restrict backup deletion
      • 1.4.6. Cleaning Up The Resources
    • 1.5. LMIG: Relational Modeling & Migration
      • 1.5.1. Exercise Overview
      • 1.5.2. Configure MySQL Environment
      • 1.5.3. Create DMS Resources
      • 1.5.4. Explore Source Model
      • 1.5.5. Explore Target Model
      • 1.5.6. Load DynamoDB Table
      • 1.5.7. Access DynamoDB Table
  • 2. LBED: Generative AI with DynamoDB zero-ETL to OpenSearch integration and Amazon Bedrock
    • 2.1. Getting Started
      • Obtain & Review Code
    • 2.2. Service Configuration
      • 2.2.1. Configure OpenSearch Service Permissions
      • 2.2.2. Enable Amazon Bedrock Models
      • 2.2.3. Load DynamoDB Data
    • 2.3. Integrations
      • 2.3.1. Configure Integrations
      • 2.3.2. Create the zero-ETL Pipeline
    • 2.4. Query and Conclusion
  • 3. LADV: Advanced Design Patterns for Amazon DynamoDB
    • 3.1. Start here: Getting Started
      • 3.1.1. Getting Started
      • 3.1.2. Step 1 - Open the AWS Systems Manager Console
      • 3.1.3. Step 2 - Check the Python and AWS CLI installation
      • 3.1.4. Step 3 - Check boto3 installation
      • 3.1.5. Step 4 - Check the content of the workshop folder
      • 3.1.6. Step 5 - Check the files format and content
      • 3.1.7. Step 6 - Preload the items for the table Scan exercise
    • 3.2. Exercise 1: DynamoDB Capacity Units and Partitioning
      • 3.2.1. Step 1 - Create the DynamoDB table
      • 3.2.2. Step 2 - Load sample data into the table
      • 3.2.3. Step 3 - Load a larger file to compare the execution times
      • 3.2.4. Step 4 - View the CloudWatch metrics on your table
      • 3.2.5. Step 5 - Increase the capacity of the table
      • 3.2.6. Step 6 - After increasing the table’s capacity, load more data
      • 3.2.7. Step 7 - Create a new table with a low-capacity global secondary index
    • 3.3. Exercise 2: Sequential and Parallel Table Scans
      • 3.3.1. Step 1 - Execute a sequential Scan
      • 3.3.2. Step 2 - Execute a parallel Scan
    • 3.4. Exercise 3: Global Secondary Index Write Sharding
      • 3.4.1. Step 1 - Creating the GSI
      • 3.4.2. Step 2 - Querying the GSI with shards
    • 3.5. Exercise 4: Global Secondary Index Key Overloading
      • 3.5.1. Step 1 - Create the employees table for global secondary index key overloading
      • 3.5.3. Step 3 - Query the employees table using the global secondary index with overloaded attributes
      • 3.5.2. Step 2 - Load data into the new table
    • 3.6. Exercise 5: Sparse Global Secondary Indexes
      • 3.6.1. Step 1 - Add a new global secondary index to the employees table
      • 3.6.2. Step 2 - Scan the employees table to find managers without using the sparse global secondary index
      • 3.6.3. Step 3 - Scan the employees table to find managers by using the sparse global secondary index
    • 3.7. Exercise 6: Composite Keys
      • 3.7.1. Step 1 - Create a new global secondary index for City-Department
      • 3.7.2. Step 2 - Query all the employees from a state
      • 3.7.3. Step 3 - Query all the employees of a city
      • 3.7.4. Step 4 - Querying all the employees of a city and a specific department
    • 3.8. Exercise 7: Adjacency Lists
      • 3.8.1. Step 1 - Create and load the the InvoiceandBilling table
      • 3.8.2. Step 2 - Review the InvoiceAndBills table on the DynamoDB console
      • 3.8.3. Step 3 - Query the table's invoice details
      • 3.8.4. Step 4 - Query the Customer details and Bill details using the Index
    • 3.9. Exercise 8: Amazon DynamoDB Streams and AWS Lambda
      • 3.9.1. Step 1 - Create the replica table
      • 3.9.2. Step 2 - Review the AWS IAM policy for the IAM role
      • 3.9.3. Step 3 - Create the Lambda function
      • 3.9.4. Step 4 - Enable DynamoDB Streams
      • 3.9.5. Step 5 - Map the source stream to the Lambda function
      • 3.9.6. Step 6 - Populate the logfile table and verify replication to logfile_replica
  • 4. LCDC: Change Data Capture for Amazon DynamoDB
    • 4.1. Getting Started
      • 4.1.1. Start with Cloud9
      • 4.1.2. Start with EC2 Instance
    • 4.2. Scenario Overview
      • 4.2.1. Create The DynamoDB Tables
      • 4.2.2. Load Sample Data
    • 4.3. Change Data Capture using DynamoDB Streams
      • 4.3.1. Enable DynamoDB Streams
      • 4.3.2. Create Dead Letter Queue
      • 4.3.3. Create Lambda Function
      • 4.3.4. Update IAM Role
      • 4.3.5. Simulate Order Updates
    • 4.4. Change Data Capture using Kinesis Data Streams
      • 4.4.1. Enable Kinesis Data Streams
      • 4.4.2. Create Dead Letter Queue
      • 4.4.3. Create Lambda Function
      • 4.4.4. Configure Lambda Function
      • 4.4.5. Simulate Order Updates
    • 4.5. Summary and Clean Up
  • 5. LMR: Build and Deploy a Global Serverless Application with Amazon DynamoDB
    • 5.1. Getting Started
    • 5.2. Module 1: Deploy the backend resources
    • 5.3. Module 2: Explore Global Tables
    • 5.4. Module 3: Interact with the Globalflix Interface
    • 5.5. Global Tables Discussion Topics
    • 5.6. Summary and Clean up
  • 6. LEDA: Build a Serverless Event Driven Architecture with DynamoDB
    • 6.1. Getting Started
    • 6.2. Overview
      • Optional - Pipeline Deep Dive
    • 6.3. Lab 1: Connect the pipeline
      • 6.3.1. Step 1: Connect StateLambda
      • 6.3.2. Step 2: Check MapLambda trigger
      • 6.3.3. Step 3: Connect ReduceLambda
    • 6.4. Lab 2: Ensure fault tolerance and exactly once processing
      • 6.4.1. Step 1: Prevent duplicates at StateLambda function
      • 6.4.2. Step 2: Ensure idempotency of ReduceLambda function
    • 6.5 Summary: Conclusions
      • Solutions
  • 7. LGME: Modeling Game Player Data with Amazon DynamoDB
    • 7.1. Getting Started
    • 7.2. Plan your data model
      • 7.2.1. Best Practices
      • 7.2.2. Build your entity-relationship diagram
      • 7.2.3. Review Access Patterns
    • 7.3. Core usage: user profiles and games
      • 7.3.1. Design the primary key
      • 1.1.1. Retrieve Item collections
      • 7.3.2. Create the table
      • 7.3.3. Bulk-load data
    • 7.4. 4. Find open games
      • 7.4.1. Model a sparse GSI
      • 7.4.2. Create a sparse GSI
      • 7.4.3. Query the sparse GSI
      • 7.4.4. Scan the sparse GSI
    • 7.5. Join and close games
      • 7.5.1. Add users to a game
      • 7.5.2. Start a game
    • 7.6. View past games
      • 7.6.1. Add an inverted index
      • 7.6.2. Retrieve games for a user
    • 7.7. Summary & Cleanup
  • 8. LDC: Design Challenges
    • 8.1. Retail Cart Scenario
      • Retail Cart References
    • 8.2. Bank Payments Scenario
      • Retail Cart References
    • 8.3. Links: NoSQL Design: Reference Materials

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Last Updated
31-08-2024


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Amazon DynamoDB Immersion Day > LDC: Design Challenges > Links: NoSQL Design: Reference Materials
  • DynamoDB Design: Reference Materials

Links: 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