Amazon Snowflake Integration takes almost zero management and brings together all of your data. From all of your users permits data sharing, and you only pay for what you use. Companies require a data platform to enable today’s data analytics. One that provides rapid deployment, on-demand scalability, and attractive performance for a fraction of the price of traditional options.

In this article, you will get to read about what Snowflake is and what are the key features of the Snowflake. You will also get to know about the AWS i.e. Amazon Web services and their key features for business management. The importance of Amazon Snowflake Integration has also been discussed. You will find out the different steps to set up Amazon Snowflake Integration. Lastly, you will get to know about the benefits of Amazon Snowflake Integration.

Introduction to Snowflake

Amazon(AWS) Snowflake Integration - Snowflake Architecture
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Snowflake is a Software as a service platform where it provides a Data Warehouse in which you can store and analyze the data. Snowflake is built in such a way that there is no need for hardware and software to install, configure and manage. All the things which are carried out by hardware and software i.e Management, Maintenance, and Tuning are done by Snowflake itself. Snowflake also allows you to organize, discover and execute diverse data. It offers you the best in class performance, near unlimited scale and concurrency so you can mobilize your data securely and with ease. 

Snowflake is designed architecturally by three main components which make Snowflake a leading Cloud Data Management and a true Software as a service offering.

The three main components of Snowflake are as follows:

  • Cloud Service Layer: Cloud services perform the function of the collection of services and coordinate the activities of Snowflake. It ties the services of Snowflake such as Infrastructure Management, Data Optimization, Metadata Management, and Security and thus it helps process all of the user’s requests, let it be login requests or question patch requests. 
  • Query Processing Layer: It processes the queries from a virtual warehouse in Snowflake. Each virtual warehouse of Snowflake is a Massively Parallel Processing (MPP) computer cluster (as you can see in fig). 
  • Database Storage Layer: After uploading of data into Snowflake, it analyses the data according to Columnar, Optimized, and Compressed format. This layer is important for Cloud Storage.
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Key Features of Snowflake

There are many features of Snowflake, but some of the most important key features of Snowflake are mentioned here:

  • Security: In Snowflake, you can select the geographical location for the storage of data. Also, the credentials feature is available to secure the data. The communication between the client and you, is also protected using TLS
  • Standard and Extended SQL: Snowflake contains most DDL and DML in SQL. In addition, it also has the most advanced DML in Snowflake which helps in analytical extension. 
  • Tools and Interfaces: Virtual warehouse and management of data are done using GUI and Command-Line. 
  • Connectivity: Snowflake offers a free trial and a wide ecosystem that is supported by third partners and technology. 
  • Data Sharing: Using Snowflake you can upload and download unlimited data and secure it in Snowflake.

Introduction to AWS

Amazon(AWS) Snowflake Integration - AWS logo
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Amazon Web Services (AWS) was founded in 2006 as an extension of Amazon’s internal infrastructure for handling its online retail operations. AWS was one of the first firms to offer a Pay-as-you-go Cloud Computing model, which expands to meet users’ needs for Computation, Storage, and Throughput.

AWS provides a variety of tools and solutions for businesses and software developers that may be used in data centres across the globe. AWS services are available to government agencies, educational institutions, charities, and private businesses.

Amazon Web services (AWS) is a platform that offers more than 200 services for business management and growth such as Analytics, Compute, Mobile, Cloud storage, Security, Databases, Networking, IoT, Developer and Management tools, Enterprise Application, and so on. In the changing business requirements, AWS offers a highly reliable and scalable infrastructure to power your business.

Key Features of AWS

Some key features of AWS can be listed as follows:

  • Identity and Access Management: Identity and Access Management allows you to provide credentials to your employer so they access AWS from different platforms and provide suggestions to your business.
  • Virtual Private Clouds: VPC offers you control for Inbound and Outbound Network traffic. Using VPC, you can directly connect On-premise servers. 
  • Security Groups and Network ACL’s: This feature allows you to secure data using Firewall Rules and according to the prototype. It also prevents your server from DDOS attacks. 
  • Direct Connect: To provide the interface between your On-premise network and AWS cloud, this feature plays an important role. 
  • Cloud Trail: With AWS cloud trail features, you will have a history of API calls made against your business. 
  • Trusted advisor: You will have better suggestions to increase money and security gaps from trusted advisors of AWS. They inspect the AWS environment and provide solutions that are helpful in business management and growth.

Understanding the Importance of Amazon Snowflake Integration

In many ways, Amazon Snowflake Integration is helpful for your business:

  • Store Data: You can store all Structural and Related data with queries and dot notation. 
  • Pay Per Use and Time: With the Snowflake Built-up Cloud system, you have to pay for what you use and for the time you use. It saves your money. 
  • Compute Clusters: Data from your employees is stored in virtual warehouses in the same data management system. 
  • Flexibility: With Amazon Snowflake Integration you can share one to one, one to many file shares.
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  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources that can help you scale your data infrastructure as required.
  • Connectors: Hevo supports 100+ data sources and integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake, Firebolt Data Warehouses; Amazon S3 Data Lakes; and MySQL, MongoDB, TokuDB, DynamoDB, PostgreSQL databases to name a few.  
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Steps to Set Up Amazon Snowflake Integration

Here are the major steps involved in the Amazon Snowflake Integration

Step 1: Create a Custom Connector

To put Amazon Snowflake Integration into action, you must first design a custom connector.

Amazon(AWS) Snowflake Integration - New Custom Connector
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  • Click on connectors > create a custom connector
  • Enter the S3 location where you uploaded the Snowflake JDBC connector JAR file for the Connector S3 URL.
  • Enter a name in the Name field.
  • Select JDBC as the connector type.
  • Enter the Snowflake JDBC driver class name in the Class name field
  • Enter the following URL (using your own account) for the JDBC URL base:
jdbc:snowflake://<snowflake account info>/?user=${Username}&password=${Password}&warehouse=${warehouse}
  • Enter ‘&’ as the URL parameter delimiter, then choose Create Connector.
Amazon(AWS) Snowflake Integration - New Custom
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Step 2: Create a Connection

Complete the following steps to make a JDBC connection to Snowflake:

Amazon(AWS) Snowflake Integration - Connection Setup
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  • Select the connector on the Connectors page.
  • Select the option to create a connection.
  • Enter a name for the connection such as follows:
  • To identify the link, Write a suitable description in the Description field.
  • Choose default for JDBC URL format.
  • You have the choice of entering a user name and password or storing your encrypted credentials in Secrets Manager.
  • Select Use a Secret for Data source credentials.
  • Provide the following Additional URL parameters to perform a SQL statement in Snowflake:
    • Warehouse: The virtual Snowflake Warehouse in which the query will be conducted. Substitute a valid value for the warehouse.
    • Db: The name of the Snowflake database.
    • The Snowflake database schema is referred to as schema.
  • Check that the JDBC URL is correct.
Amazon(AWS) Snowflake Integration - AWS URL Check
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Step 3: Create a Job

You may now use this connection to define the job.

Amazon(AWS) Snowflake Integration - Select Connector
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  • Select your connection on the Connectors page.
  • Choose the option to Create a Job.
  • Enter a name in the Name field (for this post, you can enter an Untitled Job).
  • Enter a meaningful job description in the Description field.
  • Choose an IAM Role that has access to the destination S3 location where the job is writing to and the source location where the Snowflake JDBC JAR file is loaded, as well as the ability to run the AWS Glue task (use the AWS Glue service role).
  • For Type, Glue version, Language, Worker type, Number of workers, Number of retries, and Job timeout, use the default values.
  • Choose Disable for the Job bookmark.
Amazon(AWS) Snowflake Integration - Settings
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  • Save the position.
  • Go to the Data Source properties-connector tab to define the table or query to read from Snowflake.
  • Select Save.
Amazon(AWS) Snowflake Integration - Save Data
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  • Select the Add icon in the Visual tab to establish a new S3 node for the destination.
  • Pay special attention to the Target node option on the Node properties tab.
Amazon(AWS) Snowflake Integration - Target Node
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  • Define the S3 bucket location where AWS Glue will write the results on the Data target properties tab.
Amazon(AWS) Snowflake Integration - Data Results
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  • To map the Snowflake column name to the destination column, add an Apply Mapping transformation.
  • Save your settings.
  • On the Script tab, look at the script generated by AWS Glue for verification.
Amazon(AWS) Snowflake Integration - Code executed
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  • Run the job and validate that the table data is successfully stored in the specified S3 bucket location.
Amazon(AWS) Snowflake Integration - Data Available
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  • The data from Snowflake would now be visible in your S3 bucket, as shown in the accompanying screenshot.

This concludes the Amazon Snowflake Integration.

Benefits of Amazon Snowflake Integration

  • Amazon Snowflake Integration offers you seamless and secure data sharing in Multi-Cloud systems. 
  • You or employees can add queries and it will be helpful in the overall growth of the business. 
  • You can store and download a large amount of data from a Multi-Cloud system and analyze it within minutes in table, column, or chart form. 
  • It helps you reach your business needs by providing advice according to the AWS environment.


In this age of Big Data, Amazon Snowflake Integration can help Collect, Store, and Analyze increasing volumes of data in order to monitor essential business functions, allocate monetary and human capital resources appropriately, and make educated strategic business decisions.

Enterprises have turned to Cloud Computing and Data Services such as Amazon Web Services (AWS) and SaaS-based Data Warehousing such as Amazon Snowflake Integration to store Data Efficiently, Minimize cost and Human capital-output and deliver business insights in real-time across business units, subsidiaries, and partners with often hundreds to thousands of concurrent users.

Amazon Snowflake Integration offers a wide range of benefits but the integration of Snowflake on AWS is not an easy task to perform. However, this is where Hevo saves the day for you!

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Tell us about your experience of setting up the Amazon Snowflake Integration! Share your thoughts with us in the comments section below.

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