Integrating the on-premise data present in the database into a data warehouse has become an essential part of every business workflow. By doing so, organizations tend to take a more data-driven approach and are able to decide what steps to take for better business performance.
Amazon RDS Oracle is a popular relational database service that companies use to store data. Moving this data into a data warehousing environment like Snowflake can enable you to generate valuable insights that benefit your business. This article will discuss some popular methods to move your data from AWS RDS Oracle to Snowflake.
Why Integrate AWS RDS Oracle to Snowflake?
Integrating data from AWS RDS Oracle to Snowflake can help you apply advanced data analytics and machine learning to your data. It can provide a cost-effective advantage in performing ACID compliance, multi-partitioning, and other data management features. You can use Snowflake to scale your data beyond the limits of Amazon RDS Oracle.
With features like Snowgrid, you can connect your data with teams and business units across multiple regions. The Snowflake marketplace allows you to perform multiple workloads over a single platform, including application development, cybersecurity, data engineering, and more.
An Overview of AWS RDS for Oracle
Amazon Relational Database Service (RDS) Oracle is a cloud relational database service enabling you to create, manage, and scale your database.
Amazon RDS for Oracle provides an hourly pricing feature, enabling you to pay according to your requirements without any upfront fee. It allows you to use time efficiently by performing database administration tasks like backups, monitoring, and software patchups.
The Amazon RDS Multi-AZ feature provides high availability and durability for up to 99.95% of the time. To learn more about it, read Amazon RDS for Oracle.
An Overview of Snowflake
Snowflake is a fully managed data warehouse platform providing a single interface for data lakes, warehouses, application development, and real-time data sharing. It offers maintenance, supervision, and upgrade features in a cloud environment.
With its Elastic Multi-Cluster Compute feature, Snowflake can manage any workload with a single scalable engine. You can leverage Snowpark’s powers to create AI models, develop pipelines, and securely deploy applications on the cloud. To know more about Snowflake, refer to Snowflake Architecture and Concepts.
Methods to Load Data from AWS RDS Oracle to Snowflake
Here are two of the most widely used methods to convert AWS RDS Oracle to Snowflake.
Method 1: Migrate AWS RDS Oracle to Snowflake Using Hevo Data
Hevo Data is a cost-effective, real-time ELT platform that enables you to integrate and migrate data with its easy-to-use user interface. It provides a no-code solution and automated data pipelines, where you can integrate data from 150+ source connectors.
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Here are some critical features provided by Hevo Data:
- Data Transformation: Hevo provides Python-based and drag-and-drop data transformation techniques, enabling you to clean and prepare your data for analysis.
- Automated Schema Mapping: Hevo formats the incoming data by replicating it into a format more suitable for the destination schema. It also provides you with a choice of Full and incremental Mappings according to your specific requirements.
- Incremental Data Loading: Hevo ensures efficient bandwidth utilization on both the source and the destination by allowing you to transfer the modified data.
Follow the steps below to save AWS RDS Oracle to Snowflake using Hevo Data.
Configuring AWS RDS Oracle as the Source
Before proceeding with the steps involved in setting up AWS RDS Oracle as a source, you must ensure that all the prerequisites are satisfied.
Prerequisites
After satisfying all the prerequisite conditions, follow the steps below to set up AWS RDS Oracle as a source for your data pipeline.
- Select PIPELINES from the Navigation Bar.
- In the Pipelines List View, click on + CREATE.
- Select Amazon RDS Oracle on the Select Source Type page.
- Mention the mandatory fields on the Configure your Amazon RDS Oracle Source page.
AWS RDS Oracle to Snowflake: Configuring AWS RDS Oracle as a Source
Following these steps, you can easily configure AWS RDS Oracle as your source. Refer to the Hevo Data RDS Oracle Documentation to learn more about the steps involved.
Configuring Snowflake as the Destination
In this section, you will configure Snowflake as your destination. But before proceeding, make sure the prerequisites are satisfied.
Prerequisites
- You must have an active Snowflake account.
- You must have ACCOUNTADMIN and SECURITYADMIN in Snowflake to create a new role in Hevo.
- To create a warehouse, you must have ACCOUNTADMIN and SYSADMIN roles in Snowflake.
- Hevo must have USAGE permissions on the warehouse and USAGE and CREATE SCHEMA permissions on databases.
- Hevo must also have the USAGE, MONITOR, CREATE TABLE, CREATE EXTERNAL TABLE, and MODIFY permissions on the current and future schemas.
- You have a Team Collaborator or any administrator role apart from the Billing Administrator role in Hevo.
- You can also create a new Snowflake warehouse with permissions for Hevo to access your data by referring to the Create and Configure your Snowflake Warehouse section.
- You need to Obtain your Snowflake Account URL.
After following the prerequisites, you can follow the steps below to set up Snowflake as your data pipeline destination.
- In the Navigation Bar, click DESTINATIONS and select + CREATE from the Destinations List View.
- Select Snowflake on the Add Destination page.
- Specify the necessary details in the Configure your Snowflake Warehouse page.
AWS RDS Oracle to Snowflake: Configure Snowflake as a Destination
- Click TEST CONNECTION and select SAVE & CONTINUE.
Following these steps, you can easily sync AWS RDS Oracle to Snowflake. To know more about the steps involved, refer to Hevo Data Snowflake Documentation.
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Method 2: Convert AWS RDS Oracle to Snowflake Table Using Amazon S3
With this method, you can move your data from Amazon RDS to S3 and load it into Snowflake using the COPY command. You can follow another method: move your data from AWS RDS Oracle to S3 and then transfer it to Snowflake. Read Amazon S3 to Snowflake ETL to migrate data from Amazon S3 to Snowflake using Hevo Data.
Step 1: Migrating Data from AWS RDS Oracle to S3
In this section, you must move your data from the AWS RDS Oracle instance to Amazon S3.
Following the abovementioned steps, you can quickly move your data from AWS RDS to S3. For more information about the steps involved, read Amazon RDS Oracle and S3 integration documentation.
Step 2: Data Transfer from Amazon S3 to Snowflake
Following the steps mentioned in this section, you can quickly move your data from S3 to Snowflake.
- You need to create an external (S3) stage that specifies the location of the stored data files to access and load the data into a table. To do so, follow the steps mentioned in creating an S3 stage.
- You can utilize the COPY INTO <table> command to load the data from the staged file you created in the previous step.
You can refer to the Bulk Loading Guide from Amazon S3 to learn more about the steps involved.
Limitations of Using Amazon S3 to Transfer Data from Amazon RDS Oracle to Snowflake
Although both the methods mentioned above efficiently transfer data, there are certain limitations associated with using Amazon S3 to transfer data from Amazon RDS Oracle to Snowflake.
- Lack of Automation: This method lacks the automation essential for seamless data integration. You are required to perform manual tasks to transfer large datasets, which can consume valuable time.
- Lack of Real-Time Data Integration: Manually transferring data from source to destination lacks the essential real-time data integration feature. You must update the Snowflake tables constantly to ensure changes made to the source appear at the destination.
Use Cases of Integrating AWS RDS Oracle to Snowflake
- By loading data from AWS RDS Oracle to Snowflake, you can perform advanced statistical and analytical analysis on the data to produce valuable insights.
- Migrating data from AWS RDS Oracle to Snowflake can help you use features like schema evolution, table snapshots, and hidden partitioning.
- Integrating AWS RDS Oracle to Snowflake allows you to analyze your data cost-effectively. Snowflake’s cost management feature will enable you to optimize performance while minimizing costs.
Conclusion
In this article, you went through two of the most popular methods on how to insert AWS RDS Oracle data into Snowflake table. Although both methods are efficient in moving data from AWS RDS Oracle to Snowflake, there are certain limitations associated with the second method.
To overcome these limitations, you can use Hevo Data. It automates your data pipelining procedure, reducing the required manual effort. You can connect data from 150+ data source connectors through its easy user interface.
Interested in moving your data from some other database to Snowflake? Here are some of the top picks for you:
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. Also checkout our unbeatable pricing to choose the best plan for your organization.
Share your experience of AWS RDS Oracle to Snowflake integration in the comments section below!
Frequently Asked Questions (FAQs)
Q. What are the critical comparisons between Snowflake and AWS RDS?
Although Snowflake and AWS RDS have differences, there are specific points through which they are comparable. Here are some of the specific points:
- Snowflake can allow you to scale the data far beyond the limits of RDS.
- You can configure Amazon RDS with high specifications, including 64+ cores, 256+ gigabytes of memory, and fast local SSD storage.
- Amazon RDS can manage cumulative data within larger data warehouses, enhancing response time and availability.
- Properly using parallelism, partitioning, and a dimensional model can allow RDS to support a 10-terabyte data warehouse.
Suraj is a technical content writer specializing in AI and ML technologies, who enjoys creating machine learning models and writing about them.