AWS Database Blog

Category: Database

With partitioning, the ingestion finishes nearly 50 minutes faster

Designing high-performance time series data tables on Amazon RDS for PostgreSQL

Many organizations need to store time series data. Some organizations have applications designed to store and query large amounts of time series data such as collecting metrics from a fleet of internet of things (IoT) devices. Others may have a single table of time series data such as a transaction history table in an OLTP […]

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Amazon DocumentDB (with MongoDB compatibility) re:Invent 2020 recap

AWS re:Invent 2020 was a very different event than past re:Invents, given the travel shutdown imposed in response to COVID-19, but that didn’t stop the Amazon DocumentDB (with MongoDB capability) team from having a great time interacting with our customers at all of the AWS Database sessions and Ask-the-expert chat rooms! For us, the highlights […]

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The following diagram shows a simple setup where you have a single VPC.

Using Amazon RDS for SQL Server in a hybrid cloud environment

A common use case in an enterprise cloud database adoption strategy is to move your database workloads to the cloud first, while slowly moving the rest of your applications in batches. This post looks into the various possible scenarios and configurations you can use when accessing an Amazon RDS for SQL Server database instance from […]

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Screenshot of the RDS Console showing a blue banner on the top of the page with the message "Failover of aurora-globaldb in progress". The "status" column shows "Failing over" for the cluster line and "Modifying" for the databases lines.

Managed planned failovers with Amazon Aurora Global Database

Amazon Aurora is a relational database service that combines the speed and availability of high-end commercial databases with the simplicity and cost effectiveness of open-source databases. Aurora has a distributed architecture that replicates a shared storage volume across three Availability Zones to provide a high availability solution with no data loss and failover time measured […]

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Amazon DynamoDB session videos from AWS re:Invent 2020

This blog post includes links to the videos from the keynotes and Amazon DynamoDB sessions presented during AWS re:Invent 2020. This year’s conference was free and completely virtual, and featured 11 sessions about DynamoDB: eight at the advanced level and three at the expert level. To give you some idea of where to start, Rick Houlihan’s […]

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The architecture of the solution proposed is shown in the following diagram.

Orchestrating database refreshes for Amazon RDS and Amazon Aurora

The database refresh process consists of recreating of a target database using a consistent data copy of a source database, usually done for test and development purposes. Fully-managed database solutions such as Amazon Relational Database Service (Amazon RDS) or Amazon Aurora make it incredibly easy to do that. However, database administrators may need to run […]

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Amazon ElastiCache at AWS re:Invent 2020

We just wrapped up an exciting AWS re:Invent! This year, four sessions focused on Amazon ElastiCache, highlighting technical deep dives, new service announcements, best practices, and more. In case you missed one of these sessions, check out the session recordings, along with highlights from the speakers themselves, so you can learn more about the most […]

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As we discussed earlier, the class column differentiates between bots and humans: class=1 is bot acceleration, class=0 is human acceleration.

Accelerating your application modernization with Amazon Aurora Machine Learning

Organizations that store and process data in relational databases are making the shift to the cloud. As part of this shift, they often wish to modernize their application architectures and add new cloud-based capabilities. Chief among these are machine learning (ML)-based predictions such as product recommendations and fraud detection. The rich customer data available in […]

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Patterns for AWS IoT time series data ingestion with Amazon Timestream

Large-scale internet of things (IoT) applications generate data at fast rates, and many IoT implementations require data to be stored sequentially, based on date-time values generated either at sensor or at ingestion levels. Across IoT implementations in many business verticals, such as industrial, utilities, healthcare, oil and gas, logistics, consumer devices, and smart vehicles, time […]

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