AWS Database Blog

Category: Artificial Intelligence

Diagram shows a Lambda function consuming the DynamoDB streams and interacting with Amazon Comprehend and with Kinesis Firehose.

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. […]

As we discussed earlier, the class column differentiates between bots and humans: class=1 is bot acceleration, class=0 is human acceleration.

Accelerating your application modernization with Amazon Aurora Machine Learning

Organizations that store and process data in relational databases are making the shift to the cloud. As part of this shift, they often wish to modernize their application architectures and add new cloud-based capabilities. Chief among these are machine learning (ML)-based predictions such as product recommendations and fraud detection. The rich customer data available in […]

The following diagram is a Neptune Workbench visualization of the relationship between a document, a corporate acquisition event, and the organizations (with their roles) involved in that event.

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 […]

Getting started with Amazon DocumentDB (with MongoDB compatibility); Part 4 – using Amazon SageMaker notebooks

In this post, we demonstrate how to use Amazon SageMaker notebooks to connect to Amazon DocumentDB for a simple, powerful, and flexible development experience. We walk through the steps using the AWS Management Console, but also include an AWS CloudFormation template to add an Amazon SageMaker notebook to your existing Amazon DocumentDB environment.

Building and querying the AWS COVID-19 knowledge graph

This blog post details how to recreate the AWS COVID-19 knowledge graph (CKG) using AWS CloudFormation and Amazon Neptune, and query the graph using Jupyter notebooks hosted on Amazon SageMaker in your AWS account. The CKG aids in the exploration and analysis of the COVID-19 Open Research Dataset (CORD-19), hosted in the AWS COVID-19 data […]

Exploring scientific research on COVID-19 with Amazon Neptune, Amazon Comprehend Medical, and the Tom Sawyer Graph Database Browser

COVID-19 is a global crisis that has affected us all. A massive research effort is underway to gain knowledge on every facet of the virus, including symptoms, treatments, and risk factors. To aid in the relief effort, AWS has created the public COVID-19 data lake, which contains various datasets you can use to help in the […]