Warehouses and Lakes
Warehouses and Lakes

How to load data from Redshift to Snowflake destination

Learn how to use Airbyte to synchronize your Redshift data into Snowflake destination within minutes.

TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

  1. set up Redshift as a source connector (using Auth, or usually an API key)
  2. set up Snowflake destination as a destination connector
  3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Redshift

A fully managed data warehouse service in the Amazon Web Services (AWS) cloud, Amazon Redshift is designed for storage and analysis of large-scale datasets. Redshift allows businesses to scale from a few hundred gigabytes to more than a petabyte (a million gigabytes), and utilizes ML techniques to analyze queries, offering businesses new insights from their data. Users can query and combine exabytes of data using standard SQL, and easily save their query results to their S3 data lake.

What is Snowflake destination

A cloud data platform, Snowflake Data Cloud provides a warehouse-as-a-service built specifically for the cloud. The Snowflake platform is designed to empower many types of data workloads, and offers secure, immediate, governed access to a comprehensive network of data. Snowflake’s innovative technology goes above the capabilities of the ordinary database, supplying users all the functionality of database storage, query processing, and cloud services in one package.

Prerequisites

  1. A Redshift account to transfer your customer data automatically from.
  2. A Snowflake destination account.
  3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Redshift and Snowflake destination, for seamless data migration.

When using Airbyte to move data from Redshift to Snowflake destination, it extracts data from Redshift using the source connector, converts it into a format Snowflake destination can ingest using the provided schema, and then loads it into Snowflake destination via the destination connector. This allows businesses to leverage their Redshift data for advanced analytics and insights within Snowflake destination, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Redshift as a source connector

1. Open the Airbyte UI and navigate to the "Sources" tab.

2. Click on the "Create a new connection" button and select "Redshift" as the source.

3. Enter a name for the connection and click "Next".

4. Enter the necessary credentials for your Redshift database, including the host, port, database name, username, and password.

5. Test the connection to ensure that the credentials are correct and the connection is successful.

6. Select the tables or views that you want to replicate from Redshift to Airbyte.

7. Choose the replication method, either full or incremental, and set any necessary parameters.

8. Click "Create connection" to save the configuration and start the replication process.

9. Monitor the replication progress and troubleshoot any errors that may occur. 10. Once the replication is complete, you can use the data in Airbyte for further analysis or integration with other tools.

Step 2: Set up Snowflake destination as a destination connector

1. First, navigate to the Airbyte website and log in to your account.

2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.

3. Scroll down until you find the Snowflake Data Cloud destination connector and click on it.

4. You will be prompted to enter your Snowflake account information, including your account name, username, and password.

5. After entering your account information, click on the "Test" button to ensure that the connection is successful.

6. If the test is successful, click on the "Save" button to save your Snowflake Data Cloud destination connector settings.

7. You can now use the Snowflake Data Cloud destination connector to transfer data from your Airbyte sources to your Snowflake account.

8. To set up a data transfer, navigate to the "Sources" tab on the left-hand side of the screen and select the source you want to transfer data from.

9. Click on the "Create New Connection" button and select the Snowflake Data Cloud destination connector as your destination.

10. Follow the prompts to set up your data transfer, including selecting the tables or data sources you want to transfer and setting up any necessary transformations or mappings.

11. Once you have set up your data transfer, click on the "Run" button to start the transfer process.

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Step 3: Set up a connection to sync your Redshift data to Snowflake destination

Once you've successfully connected Redshift as a data source and Snowflake destination as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Redshift from the dropdown list of your configured sources.
  3. Select your destination: Choose Snowflake destination from the dropdown list of your configured destinations.
  4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
  5. Select the data to sync: Choose the specific Redshift objects you want to import data from towards Snowflake destination. You can sync all data or select specific tables and fields.
  6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Redshift to Snowflake destination according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Snowflake destination data warehouse is always up-to-date with your Redshift data.

Use Cases to transfer your Redshift data to Snowflake destination

Integrating data from Redshift to Snowflake destination provides several benefits. Here are a few use cases:

  1. Advanced Analytics: Snowflake destination’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Redshift data, extracting insights that wouldn't be possible within Redshift alone.
  2. Data Consolidation: If you're using multiple other sources along with Redshift, syncing to Snowflake destination allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
  3. Historical Data Analysis: Redshift has limits on historical data. Syncing data to Snowflake destination allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: Snowflake destination provides robust data security features. Syncing Redshift data to Snowflake destination ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: Snowflake destination can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Redshift data.
  6. Data Science and Machine Learning: By having Redshift data in Snowflake destination, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While Redshift provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Snowflake destination, providing more advanced business intelligence options. If you have a Redshift table that needs to be converted to a Snowflake destination table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Redshift account as an Airbyte data source connector.
  2. Configure Snowflake destination as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Redshift to Snowflake destination after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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For decades, data warehousing solutions have been the backbone of enterprise reporting and business intelligence. But, in recent years, cloud-based data warehouses like Amazon Redshift and Snowflake have become extremely popular. So, why would someone want to migrate from one cloud-based data warehouse to another?

The answer is simple: More scale and flexibility. With Snowflake, users can quickly scale-out data and compute resources independently by automatically adding nodes. Using the VARIANT data type, Snowflake also supports storing richer data such as objects, arrays, and JSON data. Debugging Redshift is not always straightforward as well, as Redshift users know. Sometimes it goes beyond feature differences that could trigger a desire to migrate. Maybe your team just knows how to work with Snowflake better than Redshift, or perhaps your organization wants to standardize on one particular technology.

This recipe will explain the steps you need to take to migrate from Redshift to Snowflake to maximize your business value using Airbyte.

Pre-requisites

1. You'll need to get Airbyte to move your data. To deploy Airbyte, follow the simple instructions in our documentation here.

2. Both Redshift and Snowflake are SaaS services, and you'll need to have an account on these platforms to get started.

3. When you create a data warehouse cluster with Redshift, it adds sample data by default within it. Our data is stored in the ‘redshift-cluster’ that we created within the ‘dev’ database.  The clusters created can be seen on the dashboard under ‘Amazon Redshift > Clusters’. To create a new cluster, click on the icon highlighted below and follow along.

4a) For the Destination, you will need to create an empty database and warehouse within Snowflake to host your data. To do so, click on the Databases icon as shown in the navigation bar, hit ‘Create...’.



Provide
a name for your database. In the example below, we have named our database ‘Snowflake_Destination’.


4b) After setting up the database, click on the warehouse icon and ‘Create’ a warehouse named ‘COMPUTE_WH’. In our example, we have used an X-Small compute instance. However, you can scale up the compute instance using bigger instance types or adding more instances. This can be achieved with a few simple clicks and will be demonstrated in a later section to illustrate the business value of the migration. Now you have all the prerequisites to start the migration.  



Step 1: Set up the Redshift Source in Airbyte

Open Airbyte by navigating to http://localhost:8000 in your web browser. 1b) Proceed to set up the source by filling out the details as follows -


In the ‘Set up the source’’ airbyte screen, we have named the source 'Redshift_Source' in this example, but you can change it to something else if you like. We picked the source type as ‘Redshift’. The remaining information can be obtained from the redshift-cluster dashboard, as illustrated in the highlighted parts of the screenshot below.  




Apart from this, a few more settings need to be adjusted in your Redshift console. For example, suppose you are running Airbyte locally on your machine and connecting to Redshift. In that case, you will need to enable your cluster to be publicly accessible over the internet so that Airbyte can connect to it. You can do so by navigating to the ‘Actions’ dropdown menu on the Redshift console, click ‘Modify publicly accessible setting’ and change it to be ‘Enabled’ (if not already).




Next, go to the VPC service offered by AWS. You can do so by searching for it in the search bar, as shown below.


Then navigate to the ‘Security Groups’ tab on the left to land on the page that looks like the one below to create a custom inbound and outbound rule. For example, you will need to create a custom TCP rule over port 5439 that allows incoming connections from your local IP address.  After the rule is saved, it will be displayed on the dashboard as highlighted in blue.  



The custom inbound rule needs to have the specifications as outlined in the image below. Note that the same step needs to be done for the outbound rules as well, and then your Redshift source will be ready for connection with Airbyte.




Step 2: Set up the Snowflake destination in Airbyte

After the Source is configured, proceed to the ‘Set up the destination’ page in Airbyte to configure the destination.

Similar to our previous step, the destination name is customizable. In this example, we have used “Snowflake_Destination”. Then, we picked the destination type as Snowflake. Refer to the image below for details on the other required fields.

The host value can be retrieved from your Snowflake dashboard.

Note: For this example, we are using the SYSADMIN role in Snowflake, but it is highly recommended to create a custom role in Snowflake with reduced privileges for use with Airbyte. By default, Airbyte uses Snowflake's default schema for writing data, but you can put data in another schema if you wish (like REDSHIFT_SCHEMA in our case). Having a separate schema is helpful when you want to separate the data you are migrating from the existing data. Lastly, the username and password should be as per the credentials setup during the Snowflake account creation.


Hit the “Setup the destination” button, and if everything goes well, you should see a message telling you that all the connection tests have passed.

Step 3: Set up the connection from Redshift to Snowflake

Once the destination is set up, you will be directed to the ‘set up the connection’ screen in Airbyte. In this step, you will notice that Airbyte has already detected the tables and schemas to migrate. By default, all tables are selected for migration. However, if you only want a subset of the data to be migrated, you can un-select the tables you wish to skip. In this step, you can also specify details such as sync frequency between source and destination. There are several interval options, as shown in the figure — from 5 minutes to every hour. The granularity of the sync operation can also be set by selecting the correct sync mode for your use case. Read sync mode in the Airbyte docs for more details.


After we have specified all our customizations, we can click on ‘Set up Connection’ to kick-off data migration from Redshift to Snowflake. At a glance, you'll be able to see the last sync status of your connection and when the previous sync happened.

That's how easy it is to move your data from Redshift to Snowflake using Airbyte. However, if you want to validate that the migration has happened, look at the destination database in Snowflake, and you will notice that additional data tables are present.



Step 4: Evaluating the results

After successfully migrating the data, let's evaluate Snowflake's key features which inspired the migration. In Snowflake, computing power and storage are decoupled, making Snowflake's storage capacity not dependent on the cluster size. Furthermore, Snowflake enables you to scale your data with three simple clicks, compared to Redshift's cumbersome process.


As shown in the figure below, Airbyte loads JSON data into a Snowflake table using the VARIANT data type. Using Snowflake's powerful JSON querying tools, you can work with JSON data that is stored in a table along with non-JSON data. On the other hand, Redshift has limited support for semistructured data types, requiring multiple complex sub-table joins to produce a reporting view.


Wrapping up.

To summarize, here is what we’ve done during this recipe:

1. Configured a Redshift Airbyte source

2. Configured a Snowflake Airbyte destination

3. Created an Airbyte connection that automatically migrates data from Redshift to Snowflake

4. Explored the easy to use scaling feature and support for JSON data in Snowflake using the VARIANT data type

We know that development and operations teams working on fast-moving projects with tight timelines need quick answers to their questions from developers who are actively developing Airbyte. They also want to share their learnings with experienced community members who have “been there and done that.”

Join the conversation at Airbyte’s community Slack Channel to share your ideas with over 1000 data engineers and help make everyone’s project a success.

With Airbyte, the integration possibilities are endless, and we can't wait to see what you're going to build!


What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
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Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
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Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
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Frequently Asked Questions

What data can you extract from Redshift?

Amazon Redshift provides access to a wide range of data related to the Redshift cluster, including:  

1. Cluster metadata: Information about the cluster, such as its configuration, status, and performance metrics.  

2. Query execution data: Details about queries executed on the cluster, including query text, execution time, and resource usage.  

3. Cluster events: Notifications about events that occur on the cluster, such as node failures or cluster scaling.  

4. Cluster snapshots: Point-in-time backups of the cluster, including metadata and data files.  

5. Cluster security: Information about the cluster's security configuration, including user accounts, permissions, and encryption settings.  

6. Cluster logs: Detailed logs of cluster activity, including system events, query execution, and error messages.  

7. Cluster performance metrics: Metrics related to the cluster's performance, such as CPU usage, disk I/O, and network traffic.  

Overall, Redshift's API provides a comprehensive set of data that can be used to monitor and optimize the performance of Redshift clusters, as well as to troubleshoot issues and manage security.

What data can you transfer to Snowflake destination?

You can transfer a wide variety of data to Snowflake destination. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from Redshift to Snowflake destination?

The most prominent ETL tools to transfer data from Redshift to Snowflake destination include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from Redshift and various sources (APIs, databases, and more), transforming it efficiently, and loading it into Snowflake destination and other databases, data warehouses and data lakes, enhancing data management capabilities.