Amazon Redshift, part of Amazon Web Services, is typically the final destination of many data integration projects. Data-driven companies move data from various locations to Amazon's user-friendly, high-performance cloud data warehouse so they can run that data through business intelligence (BI) tools such as Looker and Zoho. This process helps these companies gain unparalleled real-time insights into their organizational workloads so decision-makers can improve workflows.

However, there might come a time when you use Redshift as a source rather than a destination. You might need to move data from Redshift to a second location such as a data warehouse in these scenarios. 

Transferring Redshift data to Snowflake is more common than you think and data-driven organizations do it for several reasons. The challenge is finding the correct methods and tools to facilitate the data integration process.

Below, learn why organizations transfer Amazon Redshift data to Snowflake, how they do it and why is the easiest way to move data between these two virtual warehouses. 

Table of Contents is the ETL solution for moving Redshift data to Snowflake with no code. Start your seven-day free trial.

Read more: Securely Integrate Amazon Redshift With Snowflake

Why Connect Amazon Redshift to Snowflake?

There are several reasons why you might want to transfer data from Redshift to Snowflake:

  • You experience scalability or performance issues on Redshift. 
  • You want to analyze data with one of the business intelligence tools that connect to Snowflake.
  • You want to use Snowflake in conjunction with Redshift for a wider variety of BI insights, better data storage, or because Redshift's built around SQL. 

Or you may have an issue with data interchange. Snowflake offers better support for JSON than Redshift, allowing users to query this file format with its native functions.

Another reason for moving data between these data warehousing solutions is price. Redshift combines compute and storage capacities and users have to pay for both functions, even if they only need one of them. Snowflake, on the other hand, lets users purchase the features they need. Depending on your data requirements, Snowflake could be more cost-effective than Redshift.

Whatever the reason for Redshift to Snowflake data integration, there are several ways you can achieve your goal.

Manually Load Data From Amazon Redshift to Snowflake

You can move data from Redshift to Snowflake yourself, but it's complicated and demands high-level data engineering knowledge. You will need to use AWS Glue DataBrew — a visual data preparation tool that normalizes and cleans data for analytics — and Amazon's fully managed integration service AppFlow, also part of AWS services. 

There are several guides online that help you transfer data sets to Snowflake via Glue DataBrew. All tell you to set up Amazon S3 buckets, an Identity and Access Management (IAM) role, a Glue DataBrew project, and AppFlow flow. 

Moving data to Snowflake in this way can cause various problems if you don't have any experience. Get it wrong and you could affect impact query performance or experience problems with concurrency, parallel processing, replication, partitioning, and scalability. 

Read more: AWS Redshift vs. Other Data Warehouses

Use an ETL Tool for Snowflake Data Integration

Extract, Transform, Load (ETL) is a data integration process that transfers data into a destination system. In this case, that system is Snowflake. You can extract data from Amazon Redshift, transfer it to the correct format and load it to Snowflake yourself by building manual data pipelines that ensure the smooth flow of data between these warehouses. However, this method, like using AWS Glue DataBrew, requires high-level data engineering skills. It also uses excessive compute resources and bandwidth. If you make a mistake when creating a pipeline between Redshift and Snowflake, you risk data loss, schema changes, and ruining query performance. 

That's why successful enterprises use ETL tools, which automate the ETL process. These platforms extract, transform and load Redshift data to Snowflake with limited human intervention, making them a good choice for companies without programming knowledge or data engineering teams. You still have control over your data when you use an automated tool. Plus, the best ETL platforms improve data governance by safeguarding sensitive information with encryption, access controls, and other security features. 

ETL tools transfer data from Amazon Redshift to Snowflake with native connectors. Typically, these connectors require no code or, at the very least, low code. That means you don't need to know Java, Scala, Python, or any other programming language to move big data from one warehouse to another. 

Choosing the Right ETL Tool 

There are various ETL platforms on the market. Some of the most popular ones include, Talend, Informatica, and Fivetran. You can choose an open-source platform that won't cost you any money to use. However, these tools aren't always suitable for transferring large data loads from Redshift to Snowflake, making them useless for those with high-level data integration requirements. 

Snowflake recommends you choose software that's "easy to use" and "maintain," "connects to all required data sources to fetch all relevant data," and "works seamlessly with other components of your data platform, including data warehouses and data lakes."

Amazon has its own ETL tool called AWS Glue, but it doesn't transfer data to Snowflake. So you'll need to choose one from a third-party company that specializes in data migration. 

Read more: Allowing Access to My Snowflake Account

How Helps

Using is the best way to move data from Amazon Redshift to Snowflake because its native Redshift-Snowflake connector needs no code or programming. So you can transfer data from Redshift to Snowflake in minutes without worrying about data types, data sharing, metadata, Redshift clusters, or any of the other complicated stuff. Another benefit of using is that you only pay for each connector you use and not data volume, potentially saving you money. Other platforms, such as Fivetran, have a consumption-based pricing model rather than an on-demand one, which won't suit enterprises wanting to transfer large amounts of data to Snowflake. also comes with other no-code/low-code connectors, allowing you to move Redshift data to other warehouses such as Azure and BigQuery. Then there's its Salesforce-to-Salesforce connector, which collects Salesforce data, transforms it, and sends it back to Salesforce. 

You also get a REST API for creating your own data pipelines, excellent customer service (including telephone support), and an easy-to-use drag-and-drop user interface. also complies with data security standards and data governance legislation such as GDPR, HIPAA, and CCPA. migrates data from Amazon Redshift to Snowflake with its no-code native connector. Start your seven-day free trial now and try it for yourself!