AWS Database Blog

Category: Database

Monitoring best practices with Amazon ElastiCache for Redis using Amazon CloudWatch

Monitoring is an important part of maintaining the reliability, availability, and performance of your Amazon ElastiCache resources. This post shows you how to maintain a healthy Redis cluster and prevent disruption using Amazon CloudWatch and other external tools. We also discuss methods to anticipate and forecast scaling needs.

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Write a cascading delete in SPARQL

Customers often manage tree structures in their graph applications. Typical examples include categories of topics in a knowledge graph, relationships between people in an identity graph, or transaction networks in a financial application. Often, the structures are actually forests (collections of trees) with shared subtrees. In these applications, you frequently need to traverse a tree, […]

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Best practices for configuring performance parameters for Amazon RDS for SQL Server

This post discusses how to fine-tune some parameters in Amazon RDS for SQL Server to improve the performance of critical database systems. The recommended values are applicable to most environments; however, you can tune them further to fit your specific workloads. We recommend changing one or two parameters at a time and monitoring them to see the impact.

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Graph your AWS resources with Amazon Neptune

In this post, we walk through an example we released for Neptune with integration with Altimeter. Altimeter is an open-source project (MIT License) from Tableau Software, LLC that scans AWS resources and links these resources into a graph. You can store, query, and visualize the data in Neptune. You can query the graph to examine the AWS resources and their relationships in an account. For example, you can query for resources or pathways that expose a cluster with a public IP address to check for security and compliance.

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Complement Commercial Intelligence by Building a Knowledge Graph out of a Data Warehouse with Amazon Neptune

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 […]

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Cox Automotive scales digital personalization using an identity graph powered by Amazon Neptune

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.

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Load balance graph queries using the Amazon Neptune Gremlin Client

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Graph applications built using Neptune use read replicas to horizontally scale read throughput. These applications use the Neptune reader endpoint to distribute connections across the replicas in the cluster. […]

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Migrate to an Amazon Aurora PostgreSQL instance from another PostgreSQL source

Amazon Aurora with PostgreSQL compatibility 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 out read workloads and high availability within a single […]

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Using collaborative filtering on Yelp data to build a recommendation system in Amazon Neptune

“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 […]

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Visualize query results using the Amazon Neptune workbench

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.

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