Question: What are the differences between DynamoDB and Snowflake?

Rafal Wilinski

Answered by Rafal Wilinski


DynamoDB and Snowflake are popular data storage solutions, but they serve very different purposes and have different strengths.

Amazon DynamoDB

Amazon DynamoDB is a NoSQL database service provided by Amazon Web Services (AWS). It's designed for high-performance, scalable, and low-latency applications. It provides flexible data modeling for semi-structured data and supports key-value and document structures.

Key Features:

  • Serverless: No servers to provision or manage.
  • Performance at scale: Single-digit millisecond response times, and can handle millions of requests per second.
  • Event-driven programming: Integration with AWS Lambda allows for triggering code in response to data modifications.
  • On-demand or provisioned capacity modes: Pay for the reads/writes that your application performs on your tables.

Here's an example of how to interact with DynamoDB using AWS SDK for JavaScript in Node.js:

var AWS = require("aws-sdk"); AWS.config.update({region: "us-west-2"}); var dynamodb = new AWS.DynamoDB.DocumentClient(); var params = { TableName : "Movies", Key: { "year": 2015, "title": "The Big New Movie" } }; dynamodb.get(params, function(err, data) { if (err) console.error("Unable to read item. Error JSON:", JSON.stringify(err, null, 2)); else console.log("GetItem succeeded:", JSON.stringify(data, null, 2)); });


Snowflake is a fully-managed, cloud-based, SQL data warehouse that focuses on great performance, zero-tuning, diversity of data sources, and security. Snowflake separates compute and storage resources, enabling users to scale up and down on-the-fly without downtime.

Key Features:

  • Support for Structured and Semi-Structured Data: In addition to native support for relational data, Snowflake has unique capabilities for querying semi-structured data such as JSON directly with SQL.
  • Fully Managed Service: Handles all aspects of data management from data protection and security to infrastructure capacity planning.
  • Seamless Data Sharing: Provides sharing of live, ready-to-query data across different business units and with any of your business partners.

A basic SQL command to query data in Snowflake might look like this:

SELECT column1, column2 FROM my_table WHERE condition;


While both DynamoDB and Snowflake can store and retrieve data, they are designed for different use cases.

Choose DynamoDB when you require:

  • High-speed interactions and real-time data access. DynamoDB shines with applications needing consistent, single-digit millisecond latency.
  • Seamless scalability and performance.
  • Tight integration with other AWS services.

Opt for Snowflake when you need:

  • A SQL-compliant, fully-managed, and zero-maintenance data warehouse solution.
  • Complex, large-scale data analytics.
  • Separation of storage and compute allowing independent scaling.
  • Built-in support for structured and semi-structured data (like JSON, AVRO, XML, ORC, Parquet).

Remember, the choice between DynamoDB and Snowflake should be based on your specific needs and the nature of your workload.

Login to the AWS Console less. Use Dynobase.

Start your 7-day free trial today

Product Features

Member Portal
© 2024 Dynobase
Better DynamoDB Experience.
Try Dynobase to accelerate your DynamoDB workflow. Start your 7-day free trial today.