AWS Partner Network (APN) Blog
Category: Amazon Aurora
Unlock Mainframe Data with Precisely Connect and Amazon Aurora
To curb spend and unlock scale, Precisely collaborates with AWS to provide the means to synchronize mainframe data in the cloud at speed and power modern applications. Learn how Precisely Connect integrates data seamlessly from legacy systems into next-generation cloud and data platforms. Using Precisely Connect, data from sequential files, VSAM datasets, or databases such as IMS and Db2 can be transferred to Amazon RDS.
Read/Write Capability Enhancements in Amazon Aurora with Apache ShardingSphere-Proxy
Learn how to use ShardingSphere-Proxy to build database clusters, covering aspects such as sharding, read/write splitting, and dynamic configuration. Apache ShardingSphere is an ecosystem of open-source distributed database solutions, including JDBC and Proxy products, which can be deployed either independently or in combination. The commercial edition provides additional data security and data sharding features from AWS Partner SphereEx.
Migrating 50,000 MySQL Databases to Amazon Aurora with Futuralis
Large-scale database migrations are difficult, have multiple phases, and are time-consuming. Customers want a simpler way to achieve large-scale migration with little to no downtime. Learn how Futuralis helped their customer migrate more than 50,000 MySQL databases hosted on an Amazon EC2 server to Amazon Aurora relational database using AWS and native MySQL tools. The customer had MySQL version 5.5 hosted on EC2 with more than 50,000 databases with 1.5 million tables and hundreds of millions of records.
Gaining Operational Insights of the Australian Census with AWS
In early August, millions of people took part in the 2021 Census across Australia, providing a comprehensive picture of the country’s economic, social, and cultural makeup. The Australian Bureau of Statistics (ABS) ran the 2021 Census on AWS using an operational insights platform built in partnership with AWS Professional Services, Shine Solutions, ARQ Group, and the ABS. Learn how this tool provided near real-time insights into a very complex logistical activity.
Using the Heimdall Proxy to Split Reads and Writes for Amazon Aurora and Amazon RDS
Horizontally scaling a SQL database involves separating the write-master from read-only servers. This allows the write server to perform dedicated write operations rather than processing redundant read queries. However, writing to one node and reading from another can result in inconsistent data due to synchronization delays. Heimdall Data offers a database proxy to help developers and architects achieve optimal scale from their Amazon RDS and Amazon Aurora environment without any application changes.
SaaS Storage Partitioning with Amazon Aurora Serverless
With the introduction of Amazon Aurora Serverless (currently in preview), SaaS providers are now equipped with a model to bring the scale and cost efficiency of serverless computing directly to storage partitioning models of SaaS solutions. We take a closer look at how Aurora Serverless works and how it influences your approach to storage partitioning in SaaS environments. The goal here is to highlight the implications of the serverless storage model, identifying key areas that will be of particular interest to SaaS developers.
Re-Hosting Mainframe Applications to AWS with NTT DATA Services
NTT DATA Services provides a mainframe re-host solution that minimizes application code change while benefiting from the agility AWS offers. NTT DATA’s re-hosting reference architecture, migration best practices, and extensive technology feature set streamline mainframe migrations to AWS. NTT DATA is an APN Advanced Consulting Partner that helps clients navigate and simplify the modern complexities of business and technology, delivering the insights, solutions, and outcomes that matter most to their objectives.
How to Migrate Mainframe Batch to Cloud Microservices with AWS Blu Age
While modernizing customer mainframes, the team at AWS Blu Age discovered that Batch can be a complex aspect of a mainframe migration to AWS. It’s critical to design your AWS architecture to account for the key Batch stringent performance requirements such as intensive I/Os, large datasets, and short durations. Let’s explore how to migrate mainframe Batch to AWS microservices using AWS Blu Age automated transformation technology.
APN Partner Webinar Series – AWS Database Services
Want to dive deep and learn more about AWS Database offerings? This webinar series will provide you an exclusive deep dive into Amazon Aurora, Amazon Redshift, and Amazon DynamoDB. These webinars feature technical sessions led by AWS solutions architects and engineers, live demonstrations, customer examples, and Q&A with AWS experts. Check out these upcoming webinars and […]
Key Metrics for Amazon Aurora
This is a guest post by John Matson of Datadog. An expanded version of this post is available on the Datadog blog. Datadog is an Advanced APN Technology Partner, and is a Certified AWS MSP Technology Partner. Amazon Aurora is a MySQL-compatible database offered on Amazon RDS (Relational Database Service). In addition to a number […]