Category: Amazon DynamoDB

By combining Amazon DynamoDB with SingleStore, organizations can efficiently capture, process, and analyze DynamoDB data at scale. SingleStore has high-throughput data ingestion and near-real time analytical query capability for both relational and JSON data. This integration empowers businesses to derive actionable insights from their data in near real time, enabling faster decision-making and improved operational efficiency.

Learn how AWS and ThinkCol collaborate to build responsible AI solutions using guardrails and safeguards. With ThinkCol’s Guardrail Solution, businesses can now harness generative AI’s power while mitigating risks of toxicity, hallucination, and misuse. Additionally, organizations have the ability to maintain brand integrity and customer trust through a framework that includes pre-checking queries, generating accurate answers, and post-checking for relevance.

This post discusses an approach to creating a privacy-preserving chatbot using Amazon Bedrock and Skyflow Data Privacy Vault. We explore an architecture pattern for protecting sensitive information, such as personally identifiable information (PII) and intellectual property. The integration of Skyflow into a multi-tenant RAG model allows for an effective data privacy, compliance, and security framework to protect sensitive data throughout a generative AI application’s lifecycle.

This post presents a cost-effective, serverless multi-tenant SaaS architecture utilizing AWS managed services. Users can belong to multiple tenants via Amazon Cognito user groups, and the solution enables seamless tenant switching and allows tenants to define custom roles with Amazon DynamoDB transactions. By leveraging AWS services, the architecture provides security, isolation, custom permissions, and flexibility to scale without operational overhead.

Leveraging AWS Lambda response streaming functionality enables progressive data delivery from large datasets, enhancing web application performance. This post compares a traditional API implementation to a Lambda streaming API, demonstrating reduced time-to-first-byte latency and quicker, more dynamic client-side rendering. By incrementally sending data, response streaming eliminates lag from waiting on entire dataset transfers, vastly improving user experience for data-intensive applications.

Infrastructure as code is one of the most important concepts used with cloud solutions, and AWS CloudFormation enables IaC by deploying stacks and provisioning resources on AWS using JSON or YAML files called templates. APN Ambassador Francois Rouxel from Slalom will show you how to create a hundred VPCs in one AWS region without providing any parameters, and how to easily establish a peering connection between two of them within a single line of code.

For microservices that predominantly write data, Amazon DynamoDB provides an “always on” experience without the need for careful capacity planning, resharding, and database maintenance. These capabilities make DynamoDB a popular database service for various parts of game platforms like player data, game state, session history, and leaderboards. Learn how to pair Amazon DynamoDB with an analytics solution like Rockset to automatically index your data for fast search, aggregations, and joins at scale.

As you design, develop, and build SaaS solutions on AWS, you must think about how you want to partition the data that belongs to each of your customers (tenants). In this post, experts from AWS SaaS Factory focus on what it means to implement the pooled model with Amazon DynamoDB. We’ll outline basic strategies to partition and isolate data by tenant, and illustrate common techniques you can use to avoid the “hot” partition problem that’s often associated with partitioning tenant data in a pooled model.

This is the third post in our series documenting a project to fix issues with the AWS GameDay architecture by using tenets of the AWS Well-Architected Framework. In Part 2, we remediated the critical findings found in our initial review and here we’ll cover remediating the deficiencies found in our Disaster Recovery plan, as well as other optimizations we’ve made due to recent announcements. We will also discuss how to address another crucial component in our application development—testing.

This is the second post in our series documenting a project to fix issues with the GameDay architecture by using tenets of the AWS Well-Architected Framework. We cover the steps it took to fix the critical findings identified by the review team. As noted in Part 1 of the series, the review team delivered a list of critical findings we needed to prioritize, as well as a list of non-critical ideas that we should consider in our roadmap.