AWS Database Blog

Scale your connections with Amazon DocumentDB using mongobetween

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 […]

Unit testing Apache TinkerPop transactions: From TinkerGraph to Amazon Neptune

In this post, I build upon the approach of the previous post and show how you can use TinkerGraph to unit test your transactional workloads. Additionally, I show how to use TinkerGraph in embedded mode. Embedded mode requires the use of Java, but it simplifies the test environment considerably as there is no need to run the server as a separate process.

Enhanced Full Load Performance in AWS DMS Serverless

With AWS Database Migration Service (AWS DMS), you can migrate your data from relational databases and data warehouses to AWS or a combination of a cloud and on-premises configurations. In June 2023, AWS DMS Serverless was released, which automatically provisions, scales, and manages migration resources to make database migrations straightforward and more cost-effective. It removes the necessity of handling infrastructure tasks like capacity estimation, provisioning, cost-optimization, and managing versions and patching. In this post, we provide an overview of this new feature and present benchmarking results for two use cases.

Right-sizing Amazon RDS for Db2 by replaying the Db2 LUW workload

Amazon Relational Database Service (Amazon RDS) for Db2 makes it easy to set up, operate, and scale Db2 deployments in the cloud. Db2 is an IBM relational database that supports large-scale transactional and analytical workloads. Amazon RDS for Db2 handles time-consuming database administrative tasks, such as hardware provisioning, software patching, and backup management, freeing you […]

Use AWS DMS to migrate data from IBM Db2 DPF to an AWS target

AWS has introduced a new feature in AWS Database Migration Service (AWS DMS) that simplifies the migration of data from IBM Db2 databases with the Database Partitioning Feature (DPF) databases to Amazon Simple Storage Service (Amazon S3), a highly scalable and durable object storage service. With this new capability, you can now migrate your data from IBM Db2 DPF databases to Amazon S3, paving the way for building robust data lakes in the cloud. This new feature streamlines the migration process, provides data integrity, and minimizes the risk of data loss or corruption, even when dealing with large volumes of data distributed across multiple partitions and databases of varying sizes. In this post, we delve into the intricacies of this new AWS DMS feature and demonstrate how to implement it. We explore best practices for orchestrating data flows and optimizing the migration process, achieving a smooth transition from on-premises IBM Db2 DPF databases to a cloud-based data lake on Amazon S3.

Create a fallback migration plan for your self-managed MySQL database to Amazon Aurora MySQL using native bi-directional binary log replication

In this post, we show you how to set up bi-directional replication between an on-premises MySQL instance and an Aurora MySQL instance. We cover how to configure and set up bi-directional replication and address important operational concepts such as monitoring, troubleshooting, and high availability. In certain use cases, native bi-directional binary log replication can either provide a simpler fallback plan for your migration or provide a way to migrate applications or schemas individually, rather than all at the same time.

Executive Conversations: Putting generative AI to work in omnichannel customer service with Prashant Singh, Chief Operating Officer at LeadSquared

Prashant Singh, Chief Operating Officer at LeadSquared, joins Pravin Mittal, Director of Engineering of Amazon Aurora, for a discussion on using generative artificial intelligence (AI) to scale their omnichannel customer service application while controlling costs. LeadSquared helps customers build truly connected, empowered, and self-reliant sales and service organizations, with the power of automation. This Executive […]

How LeadSquared accelerated chatbot deployments with generative AI using Amazon Bedrock and Amazon Aurora PostgreSQL

LeadSquared is a new-age software as a service (SaaS) customer relationship management (CRM) platform that provides end-to-end sales, marketing, and onboarding solutions. Tailored for sectors like BFSI (banking, financial services, and insurance), healthcare, education, real estate, and more, LeadSquared provides a personalized approach for businesses of every scale. LeadSquared Service CRM goes beyond basic ticketing, […]

Choose the right change data capture strategy for your Amazon DynamoDB applications

Change data capture (CDC) is the process of capturing changes to data from a database and publishing them to an event stream, making the changes available for other systems to consume. Amazon DynamoDB CDC offers a powerful mechanism for capturing, processing, and reacting to data changes in near real time. Whether you’re building event-driven applications, […]

Migrate logins, database roles, users, and object-level permissions from Azure SQL Database to Amazon RDS for SQL Server

In this post, we demonstrate how to migrate SQL logins, database roles, users, and object-level permissions from Azure SQL Database to Amazon Relational Database Service (Amazon RDS) for SQL Server using T-SQL. Within SQL Server, a SQL login acts as a security principal, allowing a user or application to connect to a SQL Server instance. […]