A Guide to Store Data from Amazon Aurora RDS on an Incremental Basis using AWS Glue

Introduction

In today’s data-driven world, the ability to efficiently and securely manage databases is paramount. Amazon Aurora, a high-performance, fully managed Relational Database Service (RDS) offered by AWS, has gained immense popularity for its speed, reliability, and scalability. However, integrating Aurora RDS with other AWS services and external data sources can sometimes be challenging. This is where AWS Glue Connectors come into play, serving as the bridge between Amazon Aurora RDS and various data sources, making data integration a breeze.

AWS Glue Connectors

AWS Glue Connectors are a pivotal component of the AWS Glue service, a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics. AWS Glue Connectors are pre-built, customizable components that allow you to create ETL jobs for different data sources and destinations. They serve as the connection point between your data and AWS services, facilitating data movement and transformation.

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Why AWS Glue Connectors?

AWS Glue Connectors simplify the integration of data sources with AWS services like Amazon Aurora RDS by providing a standardized way to access, ingest, and transform data. Instead of building custom scripts or connectors for each data source, you can leverage AWS Glue Connectors to streamline the process. This saves time and ensures consistency and reliability in your data integration workflows.

Incremental Data Loading

Incremental data loading is a technique that transfers only the new or modified data from a source to a destination without moving the entire dataset. This is essential for reducing the time and resources required for data migration and ensuring that the destination system always contains the most up-to-date information.

In the context of Amazon Aurora RDS, incremental data loading means capturing changes made to the database since the last data transfer and pushing these changes to a destination like Amazon S3 or Amazon Redshift.

Pre-requisite

Amazon Aurora RDS to be launched in a private subnet
NAT gateway

pre

Amazon S3 endpoint

pre2

Steps to create a connector for Amazon Aurora RDS

Go to AWS Glue
In the Data Catalog, click on the connector
Click on Create connector

step1

step1b

Create an AWS IAM role with the below permissions

step2

Select the newly created connector and click test connection
In the test connection, give the created AWS IAM role and click test connection

step3

Once it’s successful, our connection is ready to be used

AWS Glue Crawler to read metadata from Amazon Aurora RDS

Go to AWS Glue –> Data catalog –> crawler
Give the AWS Glue crawler a name
In source, select jdbc and give connector name and /database/table
Create an AWS glue database and add that
Click on Create Crawler
Run the Crawler
Once the Crawler is successful, a table will be created under the database.

Steps to Create a Job to transfer data from Amazon Aurora RDS to Amazon S3

Click on Create Job and select a blank canvas
Select the source as AWS data catalog. Select the database and table

step4

Select target as Amazon S3 bucket and specify your Amazon S3 bucket

step4b

In job details, enable the job bookmark so that data will be loaded on an incremental basis

step4c

Conclusion

Storing data from Amazon Aurora RDS on an incremental basis using AWS Glue is an efficient and reliable approach to keeping your data warehouse or data lake up to date. You can maintain a real-time or near-real-time data synchronization process by setting up the ETL job correctly, configuring the source and target, and handling incremental data effectively.

Remember that data integrity is paramount when implementing incremental loading. Regularly monitor your ETL jobs, handle errors, and ensure that the data in your destination system is consistent and accurate. AWS Glue simplifies the ETL process and lets you focus on deriving insights from your data rather than worrying about data transfer logistics.

In a world where data is valuable, the ability to store and update data incrementally with AWS Glue ensures that your organization stays ahead of the competition by making data-driven decisions based on the most recent and relevant information. So, if you’re using Amazon Aurora RDS, consider integrating AWS Glue into your data pipeline for incremental data loading and stay competitive in today’s data-centric business landscape.

Drop a query if you have any questions regarding Amazon Aurora RDS, AWS Glue and we will get back to you quickly.

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FAQs

1. What is AWS Glue, and how does it relate to Amazon Aurora RDS?

ANS: – AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to move data between data stores. You can use AWS Glue to connect to Amazon Aurora RDS, extract data, transform it, and load it into other data stores or data lakes.

2. Can AWS Glue handle schema changes in Amazon Aurora RDS when performing incremental extraction?

ANS: – AWS Glue can handle schema changes, but you may need to update your ETL job’s schema mapping when changes occur in the source data.

3. Can I schedule AWS Glue ETL jobs for incremental updates automatically?

ANS: – Yes, you can schedule AWS Glue ETL jobs to run at specific intervals or in response to events. This allows you to automate the incremental data extraction process.

WRITTEN BY Hridya Hari

Hridya Hari works as a Research Associate - Data and AIoT at CloudThat. She is a data science aspirant who is also passionate about cloud technologies. Her expertise also includes Exploratory Data Analysis.