AWS Database Blog
Category: Amazon Comprehend
Detect PII data in Amazon Aurora with Amazon Comprehend
In this post, we demonstrate how to build a mechanism to automate the detection of sensitive data, in particular personally identifiable information (PII), in your relational database. PII is information connected to an individual and can be used to identify them. Handling PII data in a relational database, such as Amazon Aurora, requires planning and […]
Build a generative AI-powered agent assistance application using Amazon Aurora and Amazon SageMaker JumpStart
Generative AI is a form of artificial intelligence (AI) that is designed to generate content, including text, images, video, and music. In today’s business landscape, harnessing the potential of generative AI has become essential to remain competitive. Foundation models are a form of generative AI. They generate output from one or more inputs (prompts) in […]
Integrate your Amazon DynamoDB table with machine learning for sentiment analysis
Amazon DynamoDB is a non-relational database that delivers reliable performance at any scale. It’s a fully managed, multi-Region, multi-active database that provides consistent single-digit millisecond latency and offers built-in security, backup and restore, and in-memory caching. DynamoDB offers a serverless and event-driven architecture, which enables you to use other AWS services to extend DynamoDB capability. […]
Building a knowledge graph in Amazon Neptune using Amazon Comprehend Events
On 28-Oct-22, the AWS CloudFormation template and Jupyter notebook linked in this post were updated to 1/ add openCypher queries along with the existing Gremlin and SPARQL queries, 2/ updated to use Sagemaker newer Amazon Linux 2 instances, 3/ fixed a bug in the RDF generation code that improperly labeled a property as an RDF […]