DynamoDB vs MongoDB vs Cassandra - Hands-On Comparison

Comparing DynamoDB, MongoDB, and Cassandra with Sample Queries

📖 Scenario: You are working as a junior database analyst for a company that wants to understand how three popular NoSQL databases work: DynamoDB, MongoDB, and Cassandra. You will create simple data structures and queries to see how each database handles data storage and retrieval.

🎯 Goal: Build simple data entries and queries in DynamoDB style to compare with MongoDB and Cassandra concepts. You will create a table, add a configuration for querying, write a query to retrieve data, and finalize the setup.

📋 What You'll Learn

Create a DynamoDB table structure with exact attribute names and values

Add a configuration variable for query filtering

Write a query expression to retrieve items based on the configuration

Complete the table setup with a key schema and provisioned throughput

💡 Why This Matters

🌍 Real World

Understanding how different NoSQL databases store and query data helps in choosing the right database for applications like user management, product catalogs, or real-time analytics.

💼 Career

Database developers and data engineers often need to work with multiple NoSQL databases and write queries or configurations that fit the application's needs.

1

Create a DynamoDB table with sample items

Create a variable called table_items as a list of dictionaries. Add exactly two items with these attributes: {'UserID': 'user1', 'Name': 'Alice', 'Age': 30} and {'UserID': 'user2', 'Name': 'Bob', 'Age': 25}.

DynamoDB

# Create a list called table_items with two dictionaries for users
# Your code here

Need a hint?

Use a list with two dictionaries exactly as shown, with keys 'UserID', 'Name', and 'Age'.

2

Add a filter configuration for querying

Create a variable called age_filter and set it to the integer 28. This will be used to filter users older than this age.

DynamoDB

table_items = [
    {'UserID': 'user1', 'Name': 'Alice', 'Age': 30},
    {'UserID': 'user2', 'Name': 'Bob', 'Age': 25}
]
# Create an integer variable age_filter set to 28
# Your code here

Need a hint?

Just assign the number 28 to the variable age_filter.

3

Write a query to select users older than the filter

Create a variable called filtered_users that uses a list comprehension to select items from table_items where the 'Age' is greater than age_filter.

DynamoDB

table_items = [
    {'UserID': 'user1', 'Name': 'Alice', 'Age': 30},
    {'UserID': 'user2', 'Name': 'Bob', 'Age': 25}
]
age_filter = 28
# Use list comprehension to create filtered_users with users older than age_filter
# Your code here

Need a hint?

Use a list comprehension filtering by item['Age'] > age_filter.

4

Complete the DynamoDB table setup with key schema and throughput

Create a dictionary called table_config with keys: 'TableName' set to 'Users', 'KeySchema' set to a list with one dictionary {'AttributeName': 'UserID', 'KeyType': 'HASH'}, and 'ProvisionedThroughput' set to {'ReadCapacityUnits': 5, 'WriteCapacityUnits': 5}.

DynamoDB

table_items = [
    {'UserID': 'user1', 'Name': 'Alice', 'Age': 30},
    {'UserID': 'user2', 'Name': 'Bob', 'Age': 25}
]
age_filter = 28
filtered_users = [item for item in table_items if item['Age'] > age_filter]
# Create table_config dictionary with TableName, KeySchema, and ProvisionedThroughput
# Your code here

Need a hint?

Follow the exact dictionary structure with keys and nested lists/dictionaries as shown.