Category: Amazon Aurora

By Milan Thanawala – Global Head for Database, Partner GTM – AWSBy Ryan Gross – Head of Data & Applications – CaylentBy Mark Olson – Portfolio CTO – Caylent Introduction Organizations are increasingly adopting generative AI applications, which require efficient storage and retrieval of high-dimensional vector representations of data. Traditional databases are not well-suited for […]

Wawanesa migrated their on-premises Guidewire platform to Guidewire Cloud Platform (GWCP) to enhance digital user experience and leverage cloud agility. They replicated GWCP data to AWS using Database Migration Service, maintaining existing processes while futureproofing operations. This article covers migration challenges, lessons, and outcomes.

Learn how to connect the ThoughtSpot analytics platform to an Amazon Aurora MySQL database for real-time operational analytics. This post outlines the steps to create an Aurora MySQL instance, populate it with sample data, create a connection from ThoughtSpot, and build generative AI-powered analytics using natural language search. By integrating ThoughtSpot and Aurora MySQL, businesses can get instant insights from their transactional database.

Customers who move to managed databases innovate faster, spending more time building new applications and less time managing infrastructure. Learn how Ippon assisted CallRail’s successful migration from self-managed PostgreSQL to Amazon RDS for PostgreSQL. This migration alleviated the strain caused by substantial workloads on the database and unblocked cost savings by eliminating manual database maintenance and management.

Organizations need to be able to carefully define, monitor, and manage who has access to specific pieces of data according to their policies and external regulations. Effective data governance ensures data is consistent and trustworthy without being misused. Learn how Privacera enables data access governance on Amazon Aurora, including tag-based access control policies, and how Privacera can be used to mask columns or filter by row.

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.

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.

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.

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.

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.