AWS Database Blog

This is a guest post from Shahria Hossain, Software Engineer, and Mikael Graindorge, Sales Operations Leader at Thermo Fisher Scientific. The continuous expansion of data volume is a growing challenge for businesses to produce strategic solutions for their customers. Thanks to innovative approaches, these challenges have become simpler to solve with the rise of new […]

Neptune is a fully managed graph database service that makes it easy to build and run applications using highly connected datasets. Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Neptune supports both the Property Graph and the Resource Description Framework (RDF) standard.

[Updated August 2021] The Gremlin Client for Amazon Neptune is now available from Maven Central. Some APIs have changed since this article was published. Please review the demo code in the GitHub repository for the latest examples of how to use the APIs. Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and […]

January 2024: This post was reviewed and updated for accuracy. Amazon Aurora PostgreSQL-Compatible Edition combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. Aurora provides this by scaling storage across three Availability Zones in the same Region, and supports up to 15 read replica instances for scaling […]

“I’m hungry. Where should I go to eat?” It’s one of the most common questions we ask ourselves every day, and when you’re going out to spend money somewhere, you don’t want to simply pick a random place and try it—you want some sort of assurance that the restaurant you choose matches what you’re looking […]

In this post, we look at the new visualization features recently added to the Amazon Neptune workbench and released on August 12, 2020. These additional capabilities allow you to produce an interactive graph diagram representing the results of your Gremlin and SPARQL queries. We look at some Gremlin-specific features and then do the same for SPARQL. Finally, we look at some of the more advanced ways you can modify the visualizations. As a sidenote, this entire post was produced using the workbench.

Your organization may require you to connect to databases using secure SSL connections so all traffic communicating with the database is encrypted. In this post, we provide guidance on how to connect to an Amazon Aurora PostgreSQL database from a C++ application using the libpq library. We show you how to enforce SSL connections to your Aurora PostgreSQL database and connect to this from a C++ application using a secured SSL connection. You can also apply the same principles to an Amazon RDS for PostgreSQL database.

This is a guest post by David Ting, VP of Engineering at Nylas. In their own words, Nylas is a pioneer and leading provider of universal communications APIs that allow developers to quickly connect their applications to every email, calendar, or contacts provider in the world. Over 26,000 developers around the globe use the Nylas […]

Amazon RDS for SQL Server now supports Microsoft SQL Server 2019 for Express, Web, Standard, and Enterprise Editions. You can use SQL Server 2019 features such as Accelerated Database Recovery, Intelligent Query Processing, Intelligent Performance, Monitoring improvements, and Resumable Online Index creations. The purpose of this post is to: Summarize the new features in SQL […]

In this post, I show you how to use SPARQL 1.1 Federated Query in Neptune to get data about soccer teams in the UK from an external dataset, DBpedia (a well-known public dataset of Wikipedia data). Using the DBpedia publicly accessible SPARQL endpoint, I link the data from DBpedia to data that I add to the Neptune cluster.