In today’s data-driven world, organizations are constantly seeking innovative solutions to ensure data availability, reliability, and scalability. While PostgreSQL on Amazon RDS is a preferable choice to set up and operate relational databases in the cloud, Amazon Aurora allows distributed storage for downstream applications. This allows you to share data with several applications for enhanced productivity. As a result, organizations move their data from PostgreSQL on Amazon RDS to Amazon Aurora.
The integration of these two services provides a powerful solution for addressing data management challenges. One significant data management challenge that organizations often experience is the need to efficiently handle increasing volumes of data. This can lead to an increase in execution and response time for applications. However, with Amazon Aurora’s scalability features, organizations can streamline the management of vast datasets.
In this article, we will explore two ways to build a PostgreSQL on Amazon RDS to Amazon Aurora data pipeline.
Methods to Connect PostgreSQL on Amazon RDS to Aurora
Method 1: Load Data from PostgreSQL on Amazon RDS to Aurora using Read Replicas
This method is one of the popular choices for loading data from PostgreSQL on Amazon RDS to an Amazon Aurora database.
Aurora offers compatibility with both MySQL and PostgreSQL database engines. In this article, we will use Aurora PostgreSQL as a destination to migrate the data.
The migration process involves using an Aurora read replica, which acts as an intermediary step to facilitate the transition.
Here’s the breakdown to replicate data from PostgreSQL on Amazon RDS to Amazon Aurora using Aurora read replica.
- Amazon RDS PostgreSQL Instance: An active PostgreSQL database instance running on Amazon RDS. Ensure that the PostgreSQL instance is properly configured and consists of the data you want to transfer.
- Amazon Aurora Cluster: Create an Amazon Aurora cluster where you’ll be transferring the data. Choose the appropriate Amazon Aurora PostgreSQL-compatible edition and ensure that the Aurora cluster is correctly configured and ready to receive the data.
You can create an Aurora read replica either by using the AWS Management Console, AWS CLI, or the RDS API. However, creating an Aurora read replica with AWS Console is subject to the compatibility of the Aurora PostgreSQL version within the AWS Region. The following steps cover the migration process using the AWS console.
Step 1: Create an Aurora Read Replica of PostgreSQL
- In the AWS Management Console, navigate to the Amazon RDS dashboard.
- Select Databases in the navigation pane.
- Choose the PostgreSQL RDS instance that you’ll use as the source for your Aurora read replica.
- Click on Actions > Create Aurora read replica.
- On the settings page, configure the Aurora read replica settings. This will include the target Aurora cluster, instance type, security groups, and other preferences.
- Start the creation process by clicking on Create read replica.
- Go back to the RDS dashboard and select the Parameter Groups. Create or modify a parameter group associated with your PostgreSQL RDS instance. Set the rds.logical_replication parameter to 1 to enable binary logging for replication. This ensures that changes made in the source PostgreSQL database are properly captured and replicated to the Aurora replica. As a result, the data in the replica remains up-to-date and consistent with the data source.
- The Aurora read replica creation process automatically starts replicating data from your PostgreSQL RDS instance to the Aurora replica.
- You can perform tests to ensure that the data in the Aurora read replica matches the source PostgreSQL database. Validate the data, check indexes, and run queries to confirm accuracy.
Step 2: Promote Aurora Read Replica
- When the replica is synchronized and up-to-date with the source database, you can stop the replication process. After that, you can promote the Aurora read replica to become its own standalone Aurora PostgreSQL DB cluster. This means it can independently complete both read and write operations, serving as a fully separate database.
- To promote the Aurora read replica, go to the Amazon RDS dashboard and navigate to the Aurora cluster containing the read replica.
- Select the read replica and click on Actions > Promote.
- This will promote the read replica to become a standalone Aurora PostgreSQL instance.
- To confirm the Aurora Replica cluster promotion, click on Events in the navigation pane. On the Events page, search the name of your cluster on the Source list. Each event has a source, type, time, and message. Here, you can see all the events that have occurred in your AWS region for your account.
- After a successful promotion, you’ll see the following message:
Promoted Read Replica cluster to a standalone database cluster.
You’ve successfully established PostgreSQL on Amazon RDS Aurora integration using Aurora read replica.
Using the above method is particularly well-suited for the following use cases:
- No Coding Required: With this method, you can seamlessly move data from Amazon RDS PostgreSQL to Aurora without writing code, reducing complexity and allowing users to efficiently manage migration tasks. This simplification accelerates the process and minimizes potential errors, making it an accessible solution for a wide range of users.
- Seamless Integration: Both RDS and Aurora are AWS services, ensuring seamless integration without the need for any third-party applications. This simplifies the configuration and management of read replica within the AWS environment.
However, there are a few limitations of using Aurora Read Replica for PostgreSQL on Amazon RDS to Aurora Data Migration:
- Version Compatibility: You can use Aurora read replica for migration when you’re moving your database within the same AWS region and account. However, it only works if the Aurora PostgreSQL version matches or is of a higher version than your current RDS PostgreSQL version.
- Schema Changes: You need to manually map any schema changes from the source RDS instance to the Aurora cluster. This can create additional overhead during the migration process.
- Technical Complexity: Having prior experience with read replicas is important, as using them for the migration process can introduce intricacy in your database architecture. This complexity requires careful planning and ongoing monitoring for a successful migration.
- Read-Only: Aurora read replicas are read-only, so you can’t perform data modifications on them. Any data changes during the migration must be made on the source RDS instance.
Method 2: Using a No-Code Tool to Build PostgreSQL on Amazon RDS to Aurora ETL Pipeline
A no-code tool overcomes the limitations of the above method. It offers several benefits like faster development, reduced technical complexities, and accessibility to a wide range of users. This allows you to streamline your workflows and quickly adapt to changing requirements without being reliant on any coding skills.
Hevo Data is one such robust cloud-based no-code data replication platform designed to simplify the process of migrating data from PostgreSQL on Amazon RDS to Aurora. It provides an all-in-one data extraction, transformation, and loading solution.
To replicate data from PostgreSQL on Amazon RDS to Aurora integration using the Hevo platform, follow the steps mentioned below:
Step 1: Connect and Configure Amazon RDS PostgreSQL as a Data Source
Before connecting Amazon RDS PostgreSQL as a data source, make sure you’ve taken the prerequisites into consideration.
Step 2: Connect and Configure Amazon Aurora
Before proceeding with the configuration of Amazon Aurora as your destination, please review the prerequisites.
That concludes the process! You’ve completed the data migration from PostgreSQL on Amazon RDS to Aurora using the Hevo Data platform, all achieved within two straightforward steps.
Here are some of the distinctive features offered by the Hevo platform:
- Pre-built Connectors: Hevo offers 150+ pre-built connectors for various data sources and destinations, streamlining the integration process and reducing setup time.
- Minimal Coding Required: With Hevo, you can simplify the PostgreSQL on Amazon RDS to Aurora data pipeline without the need for extensive coding. It provides a user-friendly interface to set up data pipelines, making it accessible for technical as well as non-technical users.
- Data Transformation: Hevo provides various data transformation features, including pre-load and post-load functionalities. You can use its intuitive drag-and-drop feature for straightforward data transformations. However, for more complex transformation scenarios, you can use a Python console.
- Scalability: Along with horizontal scalability, Hevo also offers auto-scaling capabilities, ensuring optimal performance and efficient resource utilization as data volume increases.
- Automated Schema Mapping: Hevo can automatically map schema between PostgreSQL on Amazon RDS and Aurora, simplifying the setup process and reducing the risk of manual errors.
- Monitoring and Alerts: With Hevo, you gain access to monitoring and alerting functionalities that promptly notify you about any concerns or bottlenecks within your data pipeline. This enables you to address the data pipeline issues and troubleshoot them effectively.
What can you Achieve with PostgreSQL on Amazon RDS to Aurora Integration?
Here are some of the analyses you can perform with PostgreSQL on Amazon RDS to Amazon Aurora integration.
- Sales Analysis: Identify sales trends, optimize pricing strategies, and evaluate product performance for revenue growth.
- Customer Insights: Create 360-degree customer profiles and tailor marketing efforts to increase user satisfaction.
- Team Performance Evaluation: Assess the sales and support team’s effectiveness. This could allow you to conduct training sessions and optimize resource allocation.
- Operational Efficiency: Optimize inventory management, supply chains, and production processes to reduce costs.
Now, you know two effective ways to seamlessly integrate PostgreSQL on Amazon RDS to Amazon Aurora. The first approach involves utilizing Amazon read replicas, offering a straightforward method for data transfer. However, it is important to consider potential drawbacks. These drawbacks might include schema change limitations, version compatibility, and the need for technical expertise. These can pose challenges during the data migration process.
On the other hand, the no-code Hevo data platform streamlines the migration process. It overcomes limitations through features like schema validation, real-time streaming, and multiple pre-built connectors. This platform empowers you to bypass manual interventions, ensuring data accuracy and reliability throughout the migration journey.
If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest up to 1 million records.
Schedule a demo to see if Hevo would be a good fit for you, today!