NoSQL vs relational database comparison in DynamoDB - Hands-On Comparison

NoSQL vs Relational Database Comparison with DynamoDB

📖 Scenario: You are working for a small online bookstore. You want to store information about books and their authors. You will use DynamoDB, a NoSQL database, to create and query this data.This project will help you understand how to set up data in a NoSQL database and compare it with relational database concepts.

🎯 Goal: Create a DynamoDB table to store books with their authors, add a configuration for a query, write a query to find books by a specific author, and complete the setup with a final attribute.

📋 What You'll Learn

Create a DynamoDB table called Books with BookID as the primary key and attributes Title and Author.

Add a variable called author_name to specify the author to query.

Write a query to find all books by the author stored in author_name.

Add a final attribute Genre to the table items.

💡 Why This Matters

🌍 Real World

Online stores and apps often use NoSQL databases like DynamoDB to store flexible data about products and users. This project shows how to organize and query such data.

💼 Career

Understanding how to create tables, add data, and query in DynamoDB is essential for roles like cloud developer, database administrator, and backend engineer working with AWS.

1

Create DynamoDB table with books data

Create a DynamoDB table called Books with BookID as the primary key. Add three items with these exact values: {'BookID': '1', 'Title': 'The Great Gatsby', 'Author': 'F. Scott Fitzgerald'}, {'BookID': '2', 'Title': '1984', 'Author': 'George Orwell'}, and {'BookID': '3', 'Title': 'To Kill a Mockingbird', 'Author': 'Harper Lee'}.

DynamoDB

# Create the Books table and add three items with BookID, Title, and Author
# Your code here

Hint

Use boto3.resource('dynamodb') to get the DynamoDB resource. Then create a table object with dynamodb.Table('Books'). Use put_item to add each book.

2

Add author_name variable for query

Create a variable called author_name and set it to the string 'George Orwell' to specify the author you want to find books for.

DynamoDB

import boto3

dynamodb = boto3.resource('dynamodb')

Books = dynamodb.Table('Books')

Books.put_item(Item={'BookID': '1', 'Title': 'The Great Gatsby', 'Author': 'F. Scott Fitzgerald'})
Books.put_item(Item={'BookID': '2', 'Title': '1984', 'Author': 'George Orwell'})
Books.put_item(Item={'BookID': '3', 'Title': 'To Kill a Mockingbird', 'Author': 'Harper Lee'})

# Create author_name variable with value 'George Orwell'
# Your code here

Hint

Just create a variable named author_name and assign the string 'George Orwell' to it.

3

Query books by author_name

Write a query using Books.scan() with a filter expression to find all books where the Author attribute equals the value in author_name. Use boto3.dynamodb.conditions.Attr for the filter expression and store the result in a variable called response.

DynamoDB

import boto3
from boto3.dynamodb.conditions import Attr

dynamodb = boto3.resource('dynamodb')

Books = dynamodb.Table('Books')

Books.put_item(Item={'BookID': '1', 'Title': 'The Great Gatsby', 'Author': 'F. Scott Fitzgerald'})
Books.put_item(Item={'BookID': '2', 'Title': '1984', 'Author': 'George Orwell'})
Books.put_item(Item={'BookID': '3', 'Title': 'To Kill a Mockingbird', 'Author': 'Harper Lee'})

author_name = 'George Orwell'

# Query Books table for items where Author equals author_name
# Your code here

Hint

Use Books.scan() with FilterExpression=Attr('Author').eq(author_name) to find matching books.

4

Add Genre attribute to books

Add the Genre attribute to each book item in the Books table with these exact values: 'Classic' for BookID '1', 'Dystopian' for BookID '2', and 'Historical Fiction' for BookID '3'. Use put_item to update the items.

DynamoDB

import boto3
from boto3.dynamodb.conditions import Attr

dynamodb = boto3.resource('dynamodb')

Books = dynamodb.Table('Books')

Books.put_item(Item={'BookID': '1', 'Title': 'The Great Gatsby', 'Author': 'F. Scott Fitzgerald'})
Books.put_item(Item={'BookID': '2', 'Title': '1984', 'Author': 'George Orwell'})
Books.put_item(Item={'BookID': '3', 'Title': 'To Kill a Mockingbird', 'Author': 'Harper Lee'})

author_name = 'George Orwell'

response = Books.scan(FilterExpression=Attr('Author').eq(author_name))

# Add Genre attribute to each book item
# Your code here

Hint

Update each put_item call to include the Genre attribute with the correct value.

Practice

1. Which of the following best describes a key difference between NoSQL databases like DynamoDB and relational databases?

easy

A. NoSQL databases cannot scale horizontally, but relational databases can.

B. NoSQL databases store data without a fixed schema, while relational databases require a fixed schema.

C. Relational databases store data as key-value pairs, while NoSQL uses tables.

D. NoSQL databases always use SQL language, while relational databases do not.

Solution

1. Step 1: Understand schema requirements

   NoSQL databases like DynamoDB allow flexible, schema-less data storage, meaning you don't have to define all columns upfront.

2. Step 2: Compare with relational databases

   Relational databases require a fixed schema with tables and columns defined before storing data.

3. Final Answer:

   NoSQL databases store data without a fixed schema, while relational databases require a fixed schema. -> Option B

4. Quick Check:

   Schema flexibility = D [OK]

Hint: Remember: NoSQL = flexible schema, relational = fixed schema [OK]

Common Mistakes:

• Thinking NoSQL always uses SQL language
• Confusing data storage formats between NoSQL and relational
• Assuming NoSQL cannot scale horizontally

2. Which of the following is the correct way to describe DynamoDB's data model?

easy

A. DynamoDB stores data in fixed tables with strict column types like SQL databases.

B. DynamoDB requires complex JOIN operations to combine tables.

C. DynamoDB stores data as flexible key-value pairs or documents without fixed columns.

D. DynamoDB uses only relational schemas with foreign keys.

Solution

1. Step 1: Identify DynamoDB data model

   DynamoDB is a NoSQL database that stores data as flexible key-value pairs or documents, not fixed tables.

2. Step 2: Eliminate incorrect options

   Options A, B, and D describe relational database features which DynamoDB does not require.

3. Final Answer:

   DynamoDB stores data as flexible key-value pairs or documents without fixed columns. -> Option C

4. Quick Check:

   DynamoDB data model = C [OK]

Hint: DynamoDB = flexible key-value or document store, not fixed tables [OK]

Common Mistakes:

• Thinking DynamoDB uses SQL JOINs
• Assuming DynamoDB has fixed columns like relational DB
• Confusing relational schema terms with NoSQL

3. Consider a DynamoDB table storing user profiles with flexible attributes. Which statement is true about querying this data compared to a relational database?

medium

A. DynamoDB queries are optimized for key-value lookups and simple filters, not complex joins.

B. You can perform complex JOIN queries across multiple tables easily in DynamoDB.

C. DynamoDB requires a fixed schema to run queries.

D. Relational databases cannot enforce data integrity rules like DynamoDB.

Solution

1. Step 1: Understand DynamoDB query capabilities

   DynamoDB is designed for fast key-value lookups and simple filtering, but does not support complex JOIN operations like relational databases.

2. Step 2: Compare with relational databases

   Relational databases support complex JOINs and enforce data integrity rules, unlike DynamoDB.

3. Final Answer:

   DynamoDB queries are optimized for key-value lookups and simple filters, not complex joins. -> Option A

4. Quick Check:

   Query complexity = B [OK]

Hint: DynamoDB = simple filters, no complex JOINs [OK]

Common Mistakes:

• Assuming DynamoDB supports SQL JOINs
• Thinking DynamoDB requires fixed schema for queries
• Believing relational DBs lack data integrity

4. You wrote a DynamoDB query to retrieve items by a non-key attribute but it returns no results. What is the most likely cause?

medium

A. The table schema is fixed and missing the attribute.

B. The query syntax is invalid because DynamoDB uses SQL JOINs.

C. DynamoDB requires all attributes to be indexed automatically.

D. DynamoDB only allows queries on primary key attributes, not on arbitrary columns.

Solution

1. Step 1: Recall DynamoDB query restrictions

   DynamoDB queries work only on primary key attributes or indexed attributes, not on arbitrary non-key attributes.

2. Step 2: Analyze other options

   The query syntax is invalid because DynamoDB uses SQL JOINs. is wrong because DynamoDB does not use SQL JOINs. DynamoDB requires all attributes to be indexed automatically. is incorrect as indexes must be created explicitly. The table schema is fixed and missing the attribute. is invalid because DynamoDB is schema-less.

3. Final Answer:

   DynamoDB only allows queries on primary key attributes, not on arbitrary columns. -> Option D

4. Quick Check:

   Query keys only = A [OK]

Hint: Query only on keys or indexes in DynamoDB [OK]

Common Mistakes:

• Trying to query non-key attributes without indexes
• Expecting SQL JOIN support in DynamoDB
• Assuming DynamoDB has fixed schema

5. You need to design a system that stores user data with varying attributes and must scale easily with traffic. Which database choice and design is best?

hard

A. Use DynamoDB with flexible schema and partition keys to scale horizontally.

B. Use a relational database with fixed tables and complex JOINs for all queries.

C. Use a relational database but avoid indexes to improve speed.

D. Use DynamoDB but enforce a strict fixed schema for all items.

Solution

1. Step 1: Analyze requirements for flexibility and scalability

   The system needs to handle varying user attributes and scale easily with traffic.

2. Step 2: Match database features to requirements

   DynamoDB offers flexible schema and horizontal scaling using partition keys, making it suitable. Relational DBs with fixed schema and complex JOINs are less flexible and harder to scale horizontally.

3. Final Answer:

   Use DynamoDB with flexible schema and partition keys to scale horizontally. -> Option A

4. Quick Check:

   Flexible schema + scalability = A [OK]

Hint: Flexible schema + partition keys = DynamoDB scaling [OK]

Common Mistakes:

• Choosing relational DB for flexible schema needs
• Avoiding indexes in relational DB reduces performance
• Forcing fixed schema in DynamoDB defeats flexibility