Category: Amazon DocumentDB

In the post Reduce cost and improve performance by migrating to Amazon DocumentDB 5.0, we discussed various ways to reduce costs by migrating your workload to Amazon DocumentDB. In this post, we demonstrate the document compression feature in Amazon DocumentDB to reduce storage usage and I/O cost.

In this post, we explore the fundamentals of building containerized applications for Amazon DocumentDB using Quarkus with the Panache ORM library. We cover the processes of containerizing your code, building an image, and deploying it using Amazon ECS with AWS Fargate.

Amazon DocumentDB now supports authentication of database users using IAM – users and applications can authenticate to Amazon DocumentDB clusters using IAM users and roles. In this post, we discuss this new feature and provide you resources on how to enable IAM authentication in your Amazon DocumentDB cluster.

In this post, we discuss how to migrate a self-managed document database or Amazon DocumentDB database to Amazon DocumentDB using AWS DMS homogeneous migration.

A primary reason companies move their workloads to AWS is because of cost. With AWS, cloud migration and application modernization plans are based on your business needs and not agreements or licensing. You can acquire technology on an as-needed basis, only paying for the resources you use. You can build modern, scalable applications on AWS […]

In this post, we explore the key factors to consider when selecting a database for your generative AI applications. We focus on high-level considerations and service characteristics that are relevant to fully managed databases with vector search capabilities currently available on AWS. We examine how these databases differ in terms of their behavior and performance, and provide guidance on how to make an informed decision based on your specific requirements.

Parallel indexing in Amazon DocumentDB (with MongoDB compatibility) significantly reduces the time to create indexes. In this post, we show you how parallel indexing works, its benefits, and best practices for implementation.

In this post, we show you two ways to schedule the scaling of your Amazon DocumentDB instance-based clusters to address anticipated read traffic patterns. By aligning your Amazon DocumentDB cluster scaling operations with the anticipated read traffic patterns, you can achieve optimal performance during peak loads and save costs by reducing the need to overprovision your cluster.

Today, customers use document databases for many different types of applications. For example, gaming clients use them for handling users’ attribute information, while a stock application employs a document-oriented database to store chronological quote data. As the number of documents grows over time, you need more compute and storage than what is traditionally offered through […]

Amazon DocumentDB (with MongoDB compatibility) is a fully managed native JSON document database that makes it easy and cost-effective to operate critical document workloads at virtually any scale without managing infrastructure. You can use the same application code written using MongoDB API (versions 3.6, 4.0, and 5.0) compatible drivers, and tools to run, manage, and […]