PostgreSQL has become the preferred open-source relational database for many enterprises and start-ups with its extensible design for developers. One of the reasons developers use PostgreSQL is it allows them to add database functionality by building extensions with their preferred programming languages. You can already install and use PostgreSQL extensions in Amazon Aurora PostgreSQL-Compatible Edition and Amazon Relational Database Service for PostgreSQL. We support more than 85 PostgreSQL extensions in Amazon Aurora and Amazon RDS, such as the pgAudit extension for logging your database activity. While many workloads use these extensions, we heard our customers asking for flexibility to build and run the extensions of their choosing for their PostgreSQL database instances. Today, we are announcing the general availability of Trusted…
Category: AWS
Reposts from Amazon Web Services (AWS).
Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data
You use map apps every day to find your favorite restaurant or travel the fastest route using geospatial data. There are two types of geospatial data: vector data that uses two-dimensional geometries such as a building location (points), roads (lines), or land boundary (polygons), and raster data such as satellite and aerial images. Last year, we introduced Amazon Location Service, which makes it easy for developers to add location functionality to their applications. With Amazon Location Service, you can visualize a map, search points of interest, optimize delivery routes, track assets, and use geofencing to detect entry and exit events in your defined geographical boundary. However, if you want to make predictions from geospatial data using machine learning (ML), there…
New – Redesigned UI for Amazon SageMaker Studio
Today, I’m excited to announce a new, redesigned user interface (UI) for Amazon SageMaker Studio. SageMaker Studio provides a single, web-based visual interface where you can perform all machine learning (ML) development steps with a comprehensive set of ML tools. For example, you can prepare data using SageMaker Data Wrangler, build ML models with fully managed Jupyter notebooks, and deploy models using SageMaker’s multi-model endpoints. Introducing the Redesigned UI for Amazon SageMaker Studio The redesigned UI makes it easier for you to discover and get started with the ML tools in SageMaker Studio. One highlight of the new UI includes a redesigned navigation menu with links to SageMaker capabilities that follow the typical ML development workflow from preparing data to…
Announcing Amazon DocumentDB Elastic Clusters
Amazon DocumentDB (with MongoDB compatibility) is a scalable, highly durable, and fully managed database service for operating mission-critical JSON workloads. It is one of AWS fast-growing services with customers including BBC, Dow Jones, and Samsung relying on Amazon DocumentDB to run their JSON workloads at scale. Today I am excited to announce the general availability of Amazon DocumentDB Elastic Clusters. Elastic Clusters enables you to elastically scale your document database to handle virtually any number of writes and reads, with petabytes of storage capacity. Elastic Clusters simplifies how customers interact with Amazon DocumentDB by automatically managing the underlying infrastructure and removing the need to create, remove, upgrade, or scale instances. A Few Concepts about Elastic Clusters Sharding – A popular…