Amazon Aurora with PostgreSQL compatibility is now available with machine learning capabilities, an option to export data into Amazon S3, and compatibility with updated PostgreSQL versions.

You can use Aurora to add machine learning (ML) based predictions to your applications, using a simple, optimized, and secure integration with Amazon SageMaker and Amazon Comprehend. Aurora machine learning is based on the familiar SQL programming language, so you don’t need to build custom integrations, move data around, learn separate tools, or have prior machine learning experience. This functionality is also available for Aurora with MySQL 5.7 compatibility.
Aurora machine learning supports any ML model available in SageMaker, or you can run sentiment analysis using Comprehend. It’s available for PostgreSQL 10 and 11, with no additional charge beyond the price of the AWS services that you are using. For more information, read the launch blog, the Aurora ML feature page, and the Aurora documentation.

Azure Cosmos DB is a superset of the existing DocumentDB service, and Microsoft is transitioning all existing DocumentDB customers to Azure Cosmos DB, free of charge.

The system is designed to scale horizontally, whilst maintaining impressive performance and reliability. This is backed by a confident and generous service-level agreement.

Microsoft says that it can deliver single-digit millisecond latency at the 99th percentile, which is huge. It also says that 99.99 percent of all requests will “complete successfully,” and is also promising a 99.99 percent uptime availability.

This new effort from Redmond is extremely versatile, and can handle pretty much every type of data you’re likely to throw at it, including key-value, document, columnar, and graph types, in a variety of environments, including AI and IoT.

Azure Cosmos DB also plays nice with several NoSQL APIs including MongoDB, Table Storage, DocumentDB SQL, Gremlin, and Azure Tables. The Gremlin and Table Storage are currently in preview mode.

Another strength of Azure Cosmos DB is the ease and speed upon which data can be replicated in different Azure regions, allowing developers to quickly respond to regional surges of traffic. This elasticity doesn’t come at the expense of application downtime.

Posted On: Mar 19, 2018

Starting today, Amazon Kinesis Data Analytics supports real-time hotspot detection, which allows you to automatically detect regions of high density in your data, like a high concentration of vehicles on a highway indicating traffic bottlenecks, surging rideshare requests in a certain area indicating a popular event, or higher sales of products within a category indicating feature similarity. Detecting hotspots like these can help you gain actionable insights quickly and react to changing customer and business needs promptly.
To get started, simply call the Kinesis Data Analytics hotspot function from your Kinesis application. The hotspot function automatically builds and trains an appropriate machine learning model to identify subsections of your data streams that need attention. It identifies and reports one or more bounding boxes of these subsections in real-time.

Kinesis Data Analytics hotspot detection is unsupervised, meaning that the function does not require you to label the data for model training. In addition, Kinesis Data Analytics automatically updates the model behind the scenes to adapt to changes in the data stream. For more information including sample code and hotspot visualization, see Detecting Hotspots on a Stream in the Kinesis Data Analytics developer guide.

Kinesis Data Analytics is the easiest way to process data streams in real time with standard SQL without having to learn new programming languages or processing frameworks. Kinesis Data Analytics is available in the US East (N. Virginia), US West (Oregon), and EU (Ireland) regions. To get started, visit the Kinesis Data Analytics management console.