Category: Intermediate (200)

In Part 1 of this post, we covered how Retrieval Augmented Generation (RAG) can be used to enhance responses in generative AI applications by combining domain-specific information with a foundation model (FM). However, we stayed focused on the semantic search aspect of the solution, assuming that our vector store was already built and fully populated. In this post, we explore how to generate vector embeddings on Wikipedia data stored in a SQL Server database hosted on Amazon RDS. We also use Amazon Bedrock to invoke the appropriate FM APIs and an Amazon SageMaker Jupyter Notebook to help us orchestrate the overall process.

In this series of posts, we dive deep into performance testing of MySQL environments being migrated from on-premises to AWS. In this post, we review two different approaches to testing migrated environments with traffic that is representative of real production traffic: capturing and replaying traffic using a playback application, and mirroring traffic as it comes in using a proxy. This means you’re validating your environment using realistic data access patterns.

Amazon RDS provides different instance types optimized to fit different relational database use cases. You can modify provisioned instances manually from the Amazon RDS console or using an API. When modifications need to be done on a recurring basis, such as scaling an instance up and down during predefined periods of time, you can automate the task using EventBridge Scheduler and Lambda. In this post, we present a solution using Amazon EventBridge Scheduler and AWS Lambda that allows you to schedule a programmatic modification of a DB instance with specific tags.

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.

Whether you’ve used DynamoDB for a day or a decade, this question has no practical relevance. As a serverless database, DynamoDB doesn’t have a version. DynamoDB has had no version upgrades, no maintenance windows, no patching, and no downtime due to maintenance since launching in January 2012. You access new DynamoDB features as they become […]

In this post, we discuss the challenges with traditional data analytics mechanisms, our approach to solve them, and how you can use Amazon Aurora PostgreSQL-Compatible Edition zero-ETL integration with Amazon Redshift, which is generally available as of October 15th, 2024.

This post is a follow-up to How Zendesk tripled performance by moving a legacy system onto Amazon Aurora and Amazon Redshift. In this post, we go over the techniques we used to plan and upgrade major versions of Aurora PostgreSQL databases for Zendesk Explore with minimal customer downtime. We also discuss the performance optimizations we performed, the cost savings we achieved, and how we accomplished all of this within a period of 6 months. AWS Technical Account Managers played a significant role in helping us achieve these goals in a short period of time. The upgrade was performed successfully and without customer downtime.

As of October 8th 2024, we’ve added support for Valkey 7.2 on Amazon ElastiCache and Amazon MemoryDB, our fully managed in-memory services. In this post, we discuss the AWS contributions to Valkey, AWS commitment to making Valkey more accessible for ElastiCache and MemoryDB customers, and how customers can start using it in their applications.

AWS announced that Amazon Aurora Serverless v2 supports database capacity of up to 256 Aurora Capacity Units (ACUs). Aurora Serverless v2 is an on-demand, auto scaling configuration for Amazon Aurora. It adjusts capacity in fine-grained increments to provide the right amount of database resources that the application needs. There is no database capacity for you […]

In this post, we show you an analysis tool which serves as a starting point to your database analysis journey by highlighting specific interdependencies between database objects: object dependencies, object constraints, and trigger references. Through a combination of SQL queries on the source Oracle database dictionary and Excel filters, the solution in this post can capture interdependent database objects for a target schema and generate a visual dependency diagram.