If you come from a traditional SQL background, making the transition to Amazon’s DynamoDB can feel a bit like stepping into a parallel universe. While SQL databases rely on a structured schema with tables, columns, and relationships, DynamoDB employs a more flexible schema approach, which is a significant departure from what you might be used to. This blog post aims to bridge the gap and help you understand DynamoDB schema design from an SQL perspective.

Key Concepts


  • SQL: Tables in SQL are structured with a fixed schema, meaning that each row has the same set of columns.
  • DynamoDB: Tables in DynamoDB are more flexible. Each item (analogous to a row in SQL) can possess different attributes (columns in SQL).

Primary Key:

  • SQL: You can have single or multiple columns as primary keys. The primary key uniquely identifies each record in the table.
  • DynamoDB: There are two types of primary keys: the Partition Key and the Composite Key (Partition Key + Sort Key). The Partition Key is used for partitioning data across nodes, while the Sort Key is used for sorting data within a partition.


  • SQL: Indexes in SQL are used to speed up queries and are typically applied to columns.
  • DynamoDB: DynamoDB provides two types of indexes: Global Secondary Indexes (GSI) and Local Secondary Indexes (LSI). GSIs allow querying on any attribute, whereas LSIs allow more flexible querying within a single partition.

Schema Design Strategies

Normalization vs. Denormalization:

  • SQL: Typically, SQL databases are designed with normalization in mind to minimize data redundancy.
  • DynamoDB: It often makes sense to denormalize data in DynamoDB. Redundancy can be favorable as it allows for faster reads and writes by reducing the need for joins or multiple queries.

Data Access Patterns:

  • SQL: In SQL, you can afford to design your schema first and think about queries later, thanks to its flexible querying capabilities.
  • DynamoDB: In DynamoDB, you need to think about your data access patterns right from the beginning. Your schema design should be tailored to the types of queries you’ll be running.


  • SQL: SQL databases provide powerful JOIN operations to combine rows from two or more tables based on a related column.
  • DynamoDB: DynamoDB doesn’t support joins. Instead, you can store related data together or make multiple round trips to fetch related data.

Practical Example

Let’s say you have an e-commerce platform.

SQL Approach:

  1. You might create separate tables for Users, Products, and Orders.
  2. Users table may have columns like UserID (Primary Key), Name, Email.
  3. Orders table could have OrderID (Primary Key), UserID (Foreign Key), ProductID (Foreign Key), and OrderDate.
  4. To get all orders for a particular user, you’d execute a JOIN query between Users and Orders.

DynamoDB Approach:

  1. You might create a single table for your e-commerce platform.
  2. The primary key could be a composite key with UserID as the Partition Key and OrderID as the Sort Key.
  3. You could store the user’s information and all their orders in the same item, thereby denormalizing data.
  4. To get all orders for a particular user, you’d query the table based on the UserID partition key.


DynamoDB requires a different mindset compared to traditional SQL databases. Embrace denormalization, think about access patterns upfront, and understand the limitations (like the lack of joins). By effectively understanding these differences.

Be sure to check out more such insightful blogs in my Master Dynamodb: Demystifying AWS's Powerful NoSQL Database Service, One Byte at a Time series, for a deeper dive into DynamoDB's best practices and advanced features. Stay tuned and keep learning!