AWS DYNAMODB TWO CASE STUDIES

1. AWS DYNAMODB TWO CASE STUDIES
Introduction
Amazon DynamoDB is a fully managed NoSQL database service that provides
fast and predictable performance with seamless scalability. It enables you to
store and retrieve any amount of data, and serve any level of request traffic. It
supports document and key-value store models, and offers built-in security,
backup and restore, and in-memory caching. Additionally, it integrates with
other AWS services such as Lambda, S3, and CloudWatch, to enable a variety
of use cases including web and mobile applications, gaming, ad tech, IoT, and
more.
DynamoDB supports both document and key-value data models, allowing you
to easily store and retrieve data using simple API operations. You can use the
document model to store complex data structures, such as JSON or XML
documents, and the key-value model to store simple data types, such as strings
and numbers.
One of the key features of DynamoDB is its ability to scale automatically. It
does this by partitioning your data across multiple servers, and automatically
adjusting the number of partitions based on the amount of data and the number
of requests. This allows DynamoDB to handle sudden increases in traffic
without any manual intervention.
DynamoDB also offers built-in security features, such as encryption at rest, and
the ability to control access to your data using IAM policies. Additionally, it
offers backup and restore capabilities, allowing you to create on-demand or
scheduled backups of your data.
DynamoDB also has in-memory caching feature which enables you to reduce
the read latency for frequently accessed data by caching it in memory.
Additionally, it integrates with other AWS services such as Lambda, S3, and

2. CloudWatch, to enable a variety of use cases including web and mobile
applications, gaming, ad tech, IoT, and more.
DynamoDB supports both on-demand and provisioned capacity modes. On-
demand mode allows you to pay for only what you use, with no upfront costs or
capacity planning. Provisioned mode allows you to specify the number of read
and write capacity units for a table, and pay a fixed price for that capacity.
The First case study
One example of using Amazon DynamoDB in a web application is creating a
user management system. In this scenario, DynamoDB would be used to store
information about each user, such as their name, email address, and password.
Here is an example of how the application might use DynamoDB:
1. The user signs up for the application by submitting a form that includes their
name, email address, and password.
2. The application sends this information to DynamoDB, which stores it in a
table called "Users". The primary key for this table would be the email address
of the user, and the other information (name and password) would be stored as
attributes.
3. When the user logs in to the application, they enter their email address and
password. The application sends a request to DynamoDB to retrieve the user's
information using their email address as the primary key.
4. If the email address and password match what's stored in DynamoDB, the
user is granted access to the application. If not, the login attempt is denied.
5. Once the user is logged in, the application can use DynamoDB to retrieve and
update the user's information as needed. For example, the user could update
their password, or the application could retrieve the user's name to display it on
the screen.

3. 6. The application can also use DynamoDB's scan and query operation to
retrieve all the users information and perform any sort of analysis or reporting
on that data.
The Second case study
Another example of using Amazon DynamoDB is in an IoT application. In this
scenario, let's imagine a smart home system where multiple IoT devices such as
smart thermostat, smart lock and smart lights are sending sensor data to a
central server every few minutes.
Here is an example of how the application might use DynamoDB:
1. Each IoT device sends sensor data to a Lambda function that acts as a
message broker. The Lambda function then stores the sensor data in a
DynamoDB table called "SensorData".
2. The primary key for the table would be a composite key consisting of the
device's unique ID and a timestamp. Each item in the table would have
attributes for the sensor data, such as temperature, humidity, and motion
detection.
3. The smart home application can query the DynamoDB table to retrieve
sensor data for a specific device, or for all devices, over a specific time period.
4. The data can be used to create real-time dashboards, or to trigger actions,
such as turning on the air conditioner when the temperature exceeds a certain
threshold.
5. The application can also use DynamoDB's Global Secondary Indexes to
create more specific queries, such as finding all devices that have reported a
temperature above a certain threshold in the last hour.
6. The application could also use DynamoDB's automatic scaling to handle the
high write and read throughput required by an IoT application.