Introducing Curated Solutions for Databases on AWS

Solutions on AWS help you simplify cloud adoption and accelerate time-to-value by delivering proven, ready-to-deploy architectures tailored for industry, cross-industry, and technology use cases. The AWS Solutions Library features more than 1,300 Solutions built by AWS and AWS Partners. For instance, if you want to build a chatbot using generative artificial intelligence (AI) technologies or a containerized web portal for your organization, navigate to the Solutions Library, where you can find deployable packages tailored to those specific technologies. One type of Solution you’ll find in the library is referred to as ‘Guidance,’ which combines prescriptive technical assets, optional code, implementation guides, and content aligned with the AWS Well-Architected Framework. Guidance can be used as-is, as a starting point, or as a reference, giving you the flexibility to deploy immediately while also allowing for customization to help meet your business needs.

We have recently added a number of Guidance topics to Solutions for Databases. In this post, we provide a quick reference to the newest Guidance and how it can be used to address your business needs.

Migration and modernization

You can optimize your modern data architectures for scale, performance, and cost by building with AWS purpose-built data services such as Amazon DynamoDB, Amazon Aurora, and Amazon ElastiCache. With Guidance for Processing Real-Time Data Using Amazon DynamoDB, you can build near real-time data aggregations for DynamoDB tables. For example, if you are running an online bookstore, you can use this Guidance to track the real-time sales of a specific item. Guidance for Incremental Data Exports on AWS helps you build a serverless framework to achieve full and incremental data exports from DynamoDB to feed the changes to downstream systems, including data warehouse systems or a data lake. This will provide your business with accurate and up-to-date information for decisions.

Generative AI, machine learning, and vector databases

By using generative AI, high-performing foundation models (FMs), and large language models (LLMs), you can enhance user experiences through innovative approaches across diverse sectors. Guidance for High-Speed RAG Chatbots on AWS streamlines customer self-service processes and reduces operational costs by automating responses to customer service queries through generative AI-powered chatbots and virtual assistants. Guidance for Sentiment Analysis on AWS analyzes unstructured customer feedback from surveys, website comments, and call transcripts to identify key topics, detect sentiment, and surface emerging trends. Guidance for E-commerce Products Similarity Search on AWS helps you to implement personalized experiences for online shopping use cases. For example, if you’re running a fashion shopping website, you can use this Guidance to analyze user preferences and data and generate personalized apparel patterns and designs for your customers. Guidance for Ultra-Low Latency, Machine Learning Feature Stores on AWS provides a way to achieve low latency and cost-effective deployment of an online feature store. This Guidance covers a sample scenario based on a real-time loan approval application that makes online predictions using a feature store deployed on ElastiCache.

Workload resiliency

Your enterprise, regardless of scale, may be seeking comprehensive solutions to facilitate data backup, expedited failover, and accelerated recovery. You can use Guidance for Active-Active Replication on Amazon RDS for MySQL to implement a multi-primary mode topology to help achieve continuous availability for your applications that are backed by MySQL databases. Guidance for Disaster Recovery Using Amazon Aurora shows how to deploy a comprehensive disaster recovery (DR) framework to help meet your enterprise DR needs. This Guidance addresses two design patterns based on your recovery time objective (RTO) and recovery point objective (RPO) requirements. The first pattern uses AWS Backup to address cost-effective DR scenarios, such as backups, in the same or different AWS accounts and in the same or different AWS Regions, to help protect against situations such as ransomware threats. The second pattern uses Amazon Aurora Global Database to provide low-latency global reads and faster recovery in case of a disaster.

Guidance for Multi-Region Application Scaling Using Amazon Aurora and Guidance for Resilient Data Applications Using Amazon DynamoDB demonstrate how you can scale your web or mobile applications using the read local, write global approach to build resilient applications. Using AWS database features such as Aurora Global Database and Amazon DynamoDB global tables, you can help protect your mission-critical workloads against impairments to Regional services. Both of these Guidance help you achieve application resiliency by dynamically routing traffic away from affected Regions and using global data replication.

Your organization may frequently confront the challenge of striking a balance between application performance and cost optimization in order to deliver an optimal user experience. If your workload involves sudden traffic spikes from marketing events or new product launches, Guidance for Handling Data during Traffic Spikes on AWS can help you handle this scenario using a combination of Aurora Serverless v2 and Aurora Auto-scaling configurations. Guidance for Optimizing Cost of Amazon RDS for MySQL can help you optimize the cost of your database infrastructure, especially if your workload has a large volume of data and requires micro-second response times for your queries.

How do I get started?

Visit Solutions for Databases, review the architecture diagrams and their corresponding AWS Well-Architected framework, and get started with deploying the sample code. We invite you to explore our tutorials and training sessions to enhance your skills and knowledge. You can also try AWS database services by signing up for an AWS Free Tier.

About the author

Gowri Balasubramanian is a Senior Manager on the Specialist Solutions Architect team at Amazon Web Services. He focuses on accelerating customer adoption of AWS databases and developing prescriptive Guidance mechanisms to help customers in their cloud journey. He helps develop Solutions Architects’ database skills and guides them to deliver the best database solutions for their customers.