AWS Database Blog
Category: Advanced (300)
Multiple database support on Amazon RDS for Db2 DB instance
Many organizations run IBM Db2 databases across multiple physical servers or virtual machines. This approach leads to resource investments in infrastructure, management, and licensing. Additionally, advancements in hardware technology, increased CPU capacities, and database engine enhancements result in underutilized servers if not rightsized at the outset. To optimize resource utilization, organizations can explore the following […]
Build resilient Oracle Database workloads on Amazon EC2
In this post, we dive into the various architecture patterns and options available for both compute and storage layers while configuring your self-managed Oracle databases on Amazon EC2 to comply with your HA and DR requirements.
Long-term backup options for Amazon RDS and Amazon Aurora
In this post, we show you several long-term data backup strategies and how to effectively implement them in the AWS environment, with a focus on Amazon Relational Database Service (Amazon RDS) and Amazon Aurora.
Automate Amazon RDS credential rotation with AWS Secrets Manager for primary instances with read replicas
When using Secrets Manager to manage your master user passwords, you cannot create new read replicas for your database instance. This applies to all DB engines except Amazon RDS for SQL Server, potentially impacting your organization’s ability to efficiently scale its read operations while maintaining secure credential practices. In this post, we present a solution that automates the process of rotating passwords for a primary instance with read replicas while maintaining secure credential management practices. This approach allows you to take advantage of the benefits of both read scaling and automated credential rotation.
How Mindbody improved query latency and optimized costs using Amazon Aurora PostgreSQL Optimized Reads
In this post, we highlight the scaling and performance challenges Mindbody was facing due to an increase in their data growth. We also present the root cause analysis and recommendations for adopting to Aurora Optimized Reads, outlining the steps taken to address these issues. Finally, we discuss the benefits Mindbody realized from implementing these changes, including enhanced query performance, significant cost savings, and improved price predictability.
Multi-tenant vector search with Amazon Aurora PostgreSQL and Amazon Bedrock Knowledge Bases
In this post, we discuss the fully managed approach using Amazon Bedrock Knowledge Bases to simplify the integration of the data source with your generative AI application using Aurora. Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.
Self-managed multi-tenant vector search with Amazon Aurora PostgreSQL
In this post, we explore the process of building a multi-tenant generative AI application using Aurora PostgreSQL-Compatible for vector storage. In Part 1 (this post), we present a self-managed approach to building the vector search with Aurora. In Part 2, we present a fully managed approach using Amazon Bedrock Knowledge Bases to simplify the integration of the data sources, the Aurora vector store, and your generative AI application.
Manage users and privileges in Amazon RDS Custom for Oracle with Multitenant option
Oracle Multitenant feature is available in Oracle database from 12cR1 (12.1.0.1) and later. This enables customers to use multiple PDBs in a single Oracle database, facilitating better manageability and consolidation of environments. In Oracle Multitenant architecture, there are various user management approaches available that can be used to create and manage user accounts in the container database (CDB) and PDBs. In this post we discuss the options for managing users and how they can be set up and used for different scenarios.
Timestamp writes for write hedging in Amazon DynamoDB
In this post we demonstrate how to enforce client-side timestamp-based write sequence order on Amazon DynamoDB. The goal is to ensure items with lower timestamps don’t overwrite items with higher timestamps, even if the requests are received out of order by the database.
Create a 360-degree master data management patient view solution using Amazon Neptune and generative AI
In this post, we explore how you can achieve a patient 360-degree view using Amazon Neptune and generative AI, and use it to strengthen your organization’s research and breakthroughs. By consolidating information from multiple sources such as electronic health records (EHRs), lab reports, prescriptions, and medical histories into a single location, healthcare providers can gain a better understanding of a patient’s health.