How to Connect DynamoDB to Snowflake: Step-by-Step Explanation

Moving your DynamoDB data to Snowflake gives you big benefits: better data analysis, custom reports, easy connection with business intelligence tools, and faster performance for complex queries that help you make smart business decisions.

This guide walks you through two proven methods to sync your DynamoDB data with Snowflake. Whether you’re a small team looking for a quick setup or an enterprise needing advanced customization, we’ll show you exactly which method fits your situation. You’ll get complete step-by-step instructions, troubleshooting tips for common roadblocks, and practical examples to get your integration running smoothly.

By the end of this guide, you’ll have your NoSQL data flowing seamlessly into Snowflake, ready for powerful analysis. Let’s get started.  

What are the Methods to Connect DynamoDB to Snowflake

Method 1: Using Hevo Data to Load Data from DynamoDB to Snowflake

Step 1. Check Prerequisites

• Enable Streams on your DynamoDB tables and assign the required IAM permissions in AWS.
• Set up roles in Snowflake with rights to create warehouses, databases, schemas, and users.
• Grant Hevo access using an Admin or Collaborator role and connect it to both AWS and Snowflake.

Step 2. Connect Hevo to DynamoDB (Source Setup)

• In AWS, enable Streams for each DynamoDB table and select “New and Old images” for real-time sync.
• Get DynamoDB credentials (IAM Role or Access Keys, plus AWS region info).

• In the Hevo dashboard, go to Pipelines and click + CREATE PIPELINE.
• Select DynamoDB as the source and enter connection info (credentials and region).
• Choose data sync options such as loading historical data or only new data.

• Click TEST CONNECTION to validate settings.

Step 3. Connect Hevo to Snowflake (Destination Setup)

• In the Hevo dashboard, go to Destinations and click + CREATE DESTINATION.
• Select Snowflake and fill in the account URL, database user and password, warehouse, database, and schema details.
• Give Hevo the necessary USAGE, MODIFY, and CREATE permissions for schemas, warehouses, and databases.

• Click TEST CONNECTION to check and then SAVE & CONTINUE.

Step 4. Start Syncing Data

• Once both connections are ready, create and finalize your pipeline in Hevo.
• Data starts moving automatically from DynamoDB to Snowflake without manual work.
• Monitor pipeline status in Hevo and update settings if needed.

Method 2: Using Custom ETL Scripts to Load Data from DynamoDB to Snowflake

Step 1: Export Data from DynamoDB to Amazon S3

• Use AWS CLI or SDK (like Python boto3) to scan the DynamoDB table and write the data as JSON or CSV files to an S3 bucket.
• First, create an S3 bucket to stage data:

aws s3api create-bucket --bucket my-dynamodb-export-bucket --region us-east-1

• Example Python snippet to scan DynamoDB and save to S3:

Step 2: Prepare Snowflake Environment and Create Target Table

• In Snowflake, create the target table matching your DynamoDB schema.

Example SQL to create table:

CREATE OR REPLACE TABLE my_dynamodb_table (

    id STRING,

    attribute_1 STRING,

    attribute_2 NUMBER,

    ...

);

• Create a named external stage pointing to your S3 bucket (replace placeholders):

CREATE OR REPLACE STAGE my_s3_stage

URL='s3://my-dynamodb-export-bucket/'

STORAGE_INTEGRATION = my_storage_integration;

Step 3: Load Data from S3 into Snowflake Using COPY Command

• Use Snowflake’s COPY INTO command to load the staged JSON or CSV data into your target table.

Example:

COPY INTO my_dynamodb_table

FROM @my_s3_stage/dynamodb_data.json

FILE_FORMAT = (TYPE = 'JSON');

• You can schedule or automate this process with SQL scripts or Snowflake Snowpipe for continuous ingestion.

Step 4: Automate ETL with Scheduling or Lambda

For example, you can set a Lambda function to trigger the Python script and then run a Snowflake task or API call to start the COPY command.

Use AWS Lambda or a scheduler (e.g., AWS CloudWatch Events) to run the export and upload script periodically.

Optionally use Airflow or other orchestration tools to manage the entire extract-transform-load workflow.

Overview of DynamoDB and Snowflake

DynamoDB is a fully managed, NoSQL Database that stores data in the form of key-value pairs as well as documents. It is part of Amazon’s Data Warehousing suite of services called Amazon Web Services (AWS). DynamoDB is known for its super-fast data processing capabilities that boast the ability to process more than 20 million requests per second. In terms of backup management for Database tables, it has the option for On-Demand Backups, in addition to Periodic or Continuous Backups.

Snowflake is a fully managed, Cloud Data Warehousing solution available to customers in the form of Software-as-a-Service (SaaS) or Database-as-a-Service (DaaS). Snowflake follows the standard ANSI SQL protocol that supports fully Structured as well as Semi-Structured data like JSON, Parquet, XML, etc. It is highly scalable in terms of the number of users and computing power while offering pricing at per-second levels of resource usage. 

Significant Features of Hevo 

• Transformation: Hevo provides a drag-and-drop transformation feature, a user-friendly method of performing simple data transformations. Alternatively, you can use the Python interface for specific data transformations. 
• Fully Managed Service & Live Support: Hevo manages the entire ETL process, from data extraction to loading, ensuring flawless execution. Additionally, Hevo provides round-the-clock support for your data integration queries via email, call, or live chat.
• Pre-Built Connectors: Hevo offers 150+ pre-built connectors for various data sources, enabling you to establish an ETL pipeline quickly.
• Live Monitoring: Hevo provides live monitoring support, allowing you to check the data flow at any point in time. You can also receive instant notifications about your data transfer pipelines across devices.

Conclusion

In conclusion, this article offers a step-by-step description of creating Custom Data Pipelines to move data from DynamoDB to Snowflake. It highlights the challenges a Custom ETL solution brings along with it. In a real-life scenario, this would typically mean allocating a good number of human resources for both the development and maintenance of such Data Pipelines to ensure consistent, day-to-day operations. Knowing that it might be worth exploring and investing in a reliable cloud ETL service provider, Hevo offers comprehensive solutions to use cases such as this one and many more.

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