<p>The post describes a process for transferring data between MongoDB Atlas and
AWS S3 using AWS Glue’s visual ETL capabilities. These capabilities allow developers to create ETL pipelines without the knowledge of Spark or SQL by leveraging AWS Glue Studio. This post highlights the benefits of using AWS Glue for data transformation and integration with other AWS services. AWS S3—being highly scalable, durable, and cost-effective object storage—can be used as data lakes, a data warehousing solution, machine learning, media streaming, backup and recovery, and web hosting.
Read the blog: <a href="https://www.mongodb.com/developer/products/atlas/aws-glue-visual-etl/" target="_blank" rel="noopener nofollow">https://www.mongodb.com/developer/products/atlas/aws-glue-visual-etl/</a></p>

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Learn how to use AWS Glue's visual ETL capabilities to transfer data between MongoDB Atlas and AWS S3. AWS Glue Studio allows developers to create ETL pipelines without needing knowledge of Spark or SQL, facilitating seamless data transformation and integration with other AWS services. AWS S3 is utilized for scalable, durable, and cost-effective data storage, making it suitable for data lakes, warehousing, machine learning, media streaming, backup, and web hosting.

AWS Glue Visual ETL for Your Data in MongoDB Atlas