AWS Architecture Blog
Overview of Data Transfer Costs for Common Architectures
Data transfer charges are often overlooked while architecting a solution in AWS. Considering data transfer charges while making architectural decisions can help save costs. This blog post will help identify potential data transfer charges you may encounter while operating your workload on AWS. Service charges are out of scope for this blog, but should be […]
Top 5: Featured Architecture Content for June
The AWS Architecture Center provides new and notable reference architecture diagrams, vetted architecture solutions, AWS Well-Architected best practices, whitepapers, and more. This blog post features some of our top picks from the new and newly updated content we released this month. 1. Taco Bell: Aurora as The Heart of the Menu Middleware and Data Integration […]
How Financial Institutions can use AWS to Address Regulatory Reporting
Since the 2008 financial crisis, banking supervisory institutions such as the Basel Committee on Banking Supervision (BCBS) have strengthened regulations. There is now increased oversight over the financial services industry. For banks, making the necessary changes to comply with these rules is a challenging, multi-year effort. Basel IV, a massive update to existing rules, is […]
Integrate AWS Network Firewall with your ISV Firewall Rulesets
You may have requirements to leverage on-premises firewall technology in AWS by using your existing firewall implementation. As you move these workloads to AWS or launch new ones, you may replicate your existing on-premises firewall architecture. In this case, you can run partner appliances such as Palo Alto and Fortinet firewall appliances on Amazon EC2 […]
Field Notes: Automating Data Ingestion and Labeling for Autonomous Vehicle Development
This post was co-written by Amr Ragab, AWS Sr. Solutions Architect, EC2 Engineering and Anant Nawalgaria, former AWS Professional Services EMEA. One of the most common needs we have heard from customers in Autonomous Vehicle (AV) development, is to launch a hybrid deployment environment at scale. As vehicle fleets are deployed across the globe, they […]
Audit Your Supply Chain with Amazon Managed Blockchain
For manufacturing companies, visibility into complex supply chain processes is critical to establishing resilient supply chain management. Being able to trace events within a supply chain is key to verifying the origins of parts for regulatory requirements, tracing parts back to suppliers if issues arise, and for contacting buyers if there is a product/part recall. […]
Building a Showback Dashboard for Cost Visibility with Serverless Architectures
Enterprises with centralized IT organizations and multiple lines of businesses frequently use showback or chargeback mechanisms to hold their departments accountable for their technology usage and costs. Chargeback involves actually billing a department for the cost of their division’s usage. Showback focuses on visibility to make the department more cost conscientious and encourage operational efficiency. […]
Disaster Recovery (DR) Architecture on AWS, Part IV: Multi-site Active/Active
In my first blog post of this series, I introduced you to four strategies for disaster recovery (DR). My subsequent posts shared details on the backup and restore, pilot light, and warm standby active/passive strategies. In this post, you’ll learn how to implement an active/active strategy to run your workload and serve requests in two […]
Field Notes: Accelerating Data Science with RStudio and Shiny Server on AWS Fargate
This post was updated November 18, 2021. Data scientists continuously look for ways to accelerate time to value for analytics projects. RStudio Server is a popular Integrated Development Environment (IDE) for R, which is used to render analytics visualizations for faster decision making. These visualizations are traditionally hosted on legacy unix servers along with Shiny […]
Architecting Persona-centric Data Platform with On-premises Data Sources
Many organizations are moving their data from silos and aggregating it in one location. Collecting this data in a data lake enables you to perform analytics and machine learning on that data. You can store your data in purpose-built data stores, like a data warehouse, to get quick results for complex queries on structured data. […]









