AWS Database Blog

Sharding with Amazon Relational Database Service

Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. Amazon Relational Database Service (Amazon RDS) is a managed relational database service that provides great features to make sharding easy to use in the cloud. In this post, I describe how to use Amazon RDS to implement a sharded database architecture […]

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Boosting application performance and reducing costs with Amazon ElastiCache for Redis

Contributed by Senior Software Development Engineer, Shawn Wang, Software Development Engineer, Maddy Olson, and Senior Manager, Software Engineering, Itay Maoz. Amazon ElastiCache for Redis helps customers achieve extreme performance with very low latencies at cloud scale and minimal management costs. Redis’s high performance, simplicity, and support for diverse data structures have made it the most […]

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How to design Amazon DynamoDB global secondary indexes

Back in college, I created entity-relationship diagrams to model the system requirements of a relational database. The process involved finding all of the entities of the software system and defining relationships among them. I then modeled the relationships and entities into database tables before deciding which queries the database had to support. This method of […]

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Everything you ever wanted to know about the Amazon DynamoDB console but were afraid to ask: A detailed walkthrough

Since its release in 2012, Amazon DynamoDB has become a fully managed, multi-region, multimaster database service designed to deliver fast and predictable performance at any scale. DynamoDB is a NoSQL database that provides three options for performing operations: a web-based console, the AWS Command Line Interface (CLI), and a set of SDKs for a number […]

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Managing PostgreSQL users and roles

PostgreSQL is one of the most popular open-source relational database systems. With more than 30 years of development work, PostgreSQL has proven to be a highly reliable and robust database that can handle a large number of complicated data workloads. PostgreSQL is considered to be the primary open-source database choice when migrating from commercial databases […]

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New Amazon DocumentDB features for aggregations, arrays, and indexing

Amazon DocumentDB (with MongoDB compatibility) is a fast, scalable, highly available, and fully managed document database service that supports MongoDB workloads. You can use the same MongoDB application code, drivers, and tools as you do today to run, manage, and scale workloads on Amazon DocumentDB. This way, you can enjoy improved performance, scalability, and availability without having to […]

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Amazon DynamoDB auto scaling: Performance and cost optimization at any scale

Scaling up database capacity can be a tedious and risky business. Even veteran developers and database administrators who understand the nuanced behavior of their database and application perform this work cautiously. Despite the current era of sharded NoSQL clusters, increasing capacity can take hours, days, or weeks. As anyone who has undertaken such a task […]

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Creating a proof of concept using Amazon Aurora

As customers move to the cloud, they’re looking for the best tools to run their applications. When considering relational databases, Amazon Aurora is a frequent choice. This is no surprise, given that Amazon Aurora is MySQL and PostgreSQL wire-compatible and that it can provide greater throughput than either. Aurora provides up to five times the […]

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Introducing Gremlin query hints for Amazon Neptune

Amazon Neptune is a fast, reliable, fully managed graph database, optimized for storing and querying highly connected data. It is ideal for online applications that rely on navigating and leveraging connections in their data. Amazon Neptune supports W3C RDF graphs that can be queried using the SPARQL query language. It also supports Apache TinkerPop property […]

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Design patterns for high-volume, time-series data in Amazon DynamoDB

Time-series data shows a pattern of change over time. For example, you might have a fleet of Internet of Things (IoT) devices that record environmental data through their sensors, as shown in the following example graph. This data could include temperature, pressure, humidity, and other environmental variables. Because each IoT device tracks these values over […]

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