AWS Database Blog
Category: Artificial Intelligence
Use Amazon ElastiCache for Redis as a near-real-time feature store
Customers often use Amazon ElastiCache for real-time transactional and analytical use cases. It provides high throughout and low latencies, while meeting a variety of business needs. Because it uses in-memory data structures, typical use cases include database and session caching, as well as leaderboards, gaming and financial trading platforms, social media, and sharing economy apps. […]
Supercharge your knowledge graph using Amazon Neptune, Amazon Comprehend, and Amazon Lex
Knowledge graph applications are one of the most popular graph use cases being built on Amazon Neptune today. Knowledge graphs consolidate and integrate an organization’s information into a single location by relating data stored from structured systems (e.g., e-commerce, sales records, CRM systems) and unstructured systems (e.g., text documents, email, news articles) together in a […]
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. […]
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 […]
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 […]