Category: Amazon Aurora

Data masking is an important technique in cybersecurity, allowing organizations to safeguard personally identifiable information (PII) and other confidential data, while maintaining its utility for development, testing, and analytics purposes. Data masking involves replacing original sensitive data with false, yet realistic information. This process helps ensure that the masked version preserves the format and characteristics […]

In this post, we explore several approaches for automating the generation of vector embedding in Amazon Aurora PostgreSQL-Compatible Edition when data is inserted or modified in the database. Each approach offers different trade-offs in terms of complexity, latency, reliability, and scalability, allowing you to choose the best fit for your specific application needs.

AWS DMS accommodates a broad range of source and target data repositories, such as relational databases, data warehouses, and NoSQL databases. Proper preparation and design are vital for a successful migration process, especially when it comes to optimizing performance and addressing potential delay issues. In this blog post, we offer guidance about recognizing potential root causes of complete load and CDC delays early in the process and provide suggestions for optimally clustering tables to achieve the best performance for an AWS DMS task.

In this post, we walk through a solution that automates the notification of Amazon RDS and Aurora recommendations through email using AWS Lambda, Amazon EventBridge and Amazon Simple Email Service (Amazon SES).

In this post, we dive deeper into database optimization for your Amazon Relational Database Service (Amazon RDS), exploring how you can use AWS Compute Optimizer recommendations to make cost-aware resource configuration decisions for your MySQL and PostgreSQL databases.

In this post, we explore how you can use Vercel’s v0 generative UI to build applications with a modern UI for AWS purpose-built databases such as Amazon Aurora, Amazon DynamoDB, Amazon Neptune, and Amazon ElastiCache.

In 2023, AWS introduced the AWS advanced JDBC wrapper, enhancing the capabilities of existing JDBC drivers with additional functionality. This wrapper enables support of AWS and Amazon Aurora functions on top of an existing PostgreSQL, MySQL, or MariaDB JDBC driver of your choice. This wrapper supports a variety of plugins, including the Aurora connection tracker plugin, the limitless connection plugin, and the read-write splitting plugin. In this post, we discuss the benefits, use cases, and implementation details for two popular AWS Advanced JDBC Wrapper Driver plugins: the Aurora Initial Connection Strategy and Failover v2 plugins.

You can access an Amazon Aurora DSQL cluster by using a public endpoint and AWS PrivateLink endpoints. In this post, we demonstrate how to control access to your Aurora DSQL cluster by using public endpoints and private VPC endpoints through PrivateLink, both from inside and outside AWS.

Wiz, a leading cloud security company, identifies and removes risks across major cloud platforms. Our agent-less scanner processes tens of billions of daily cloud resource metadata entries. This demands high-performance, low-latency processing, making our Amazon Aurora PostgreSQL-Compatible Edition database, serving hundreds of microservices at scale, a critical component of our architecture. In this post, we share how we upgraded our Aurora PostgreSQL database from version 14 to 16 with near-zero downtime using Amazon Aurora Blue/Green Deployments.

In this post, we show you how Clari optimized their database performance and reduced costs by 50% by switching to Amazon Aurora I/O-Optimized.