AWS Database Blog

Category: Advanced (300)

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.

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.

Oracle Application Express for Amazon RDS for Oracle demystified

Oracle Application Express (APEX) allows you to quickly develop and deploy compelling applications that solve real problems and provide immediate value. In this post, we cover the steps for installing, configuring, and upgrading an APEX repository in Amazon RDS for Oracle and ORDS. We also show how to handle APEX when performing snapshot restore or point-in-time recovery (PITR).

Use Amazon Neptune Analytics to analyze relationships in your data faster, Part 2: Enhancing fraud detection with Parquet and CSV import and export

In this two-part series, we show how you can import and export using Parquet and CSV to quickly gather insights from your existing graph data. In Part 1, we introduced the import and export functionalities, and walked you through how to quickly get started with them. In this post, we show how you can use the new data mobility improvements in Neptune Analytics to enhance fraud detection.

Monitor server-side latency for Amazon ElastiCache for Valkey

Modern applications are built as a group of microservices, and the latency for one component can impact the performance of the entire system. Monitoring latency is critical for maintaining optimal performance, enhancing user experience, and maintaining system reliability. In this post, we explore ways to monitor latency, detect anomalies, and troubleshoot high-latency issues effectively for your self-designed (node-based) ElastiCache clusters.

Monitor server-side latency for Amazon MemoryDB for Valkey

Amazon MemoryDB is a Valkey– and Redis OSS-compatible, durable, in-memory database service that delivers ultra-fast performance. With MemoryDB, data is stored in memory with Multi-AZ durability, which enables you to achieve microsecond read and single-digit millisecond write latency and high throughput. MemoryDB is often used for building durable microservices and latency-sensitive database workloads such as […]