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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
1.1.
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
21-01-2025
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Gia Hưng
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LADV: Advanced Design Patterns for Amazon DynamoDB
> Start here: Getting Started
Start here: Getting Started
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Exercise 1: DynamoDB Capacity Units and Partitioning