As organizations mature in their cloud journey, they are bound to have many workloads and resources across different AWS regions and accounts. This raises a tough challenge for the security teams to gain visibility into where the organization has the highest risks of security incidents. To avoid financial and reputational repercussions, security engineers and executives need a high-level, real-time view of their security posture within the cloud. This solution addresses the crucial question that keeps organizations’ security executives up at night – “is our IT infrastructure secure and are we meeting compliance requirements?”
Hosting workloads in the cloud can simplify hardware procurement and maintenance, but it doesn’t protect against failures in applications and infrastructure. Many site reliability practices focus on designing highly available architectures, creating resiliency tests, and automating failover for specific components, but these precautions do not replace the need for people and processes to respond effectively during a system failure. In this solution, we discussed the significance of ensuring operational resiliency through gameday execution. We demonstrated how to set up gamedays and how they can supplement your efforts to ensure operational resilience.
With the ever-growing adoption of the cloud and hybrid cloud, businesses are struggling to “connect the dots” when it comes to customer experience – regardless of whether the customer is in-house or external. By implementing instrumentation and distributed tracing as discussed throughout this solution, enterprises will be able to leverage their single pane of glass to improve performance at the margins and quickly identify and remediate application issues as they arise.
AWS Lambda is introducing a new feature called SnapStart for Java, a capability that delivers up to 10x faster startup performance for latency-sensitive Java functions
A Data Mesh is an emerging technology and practice used to manage large amounts of data distributed across multiple accounts and platforms. It is a decentralized approach to data management, in which data remains within the business domain (producers), while also making data available to qualified users in different locations (consumers), without moving data from producer accounts. It is a step forward in the adoption of modern data architecture and aims to improve business outcomes. A Data Mesh is a modern architecture made to ingest, transform, access, and manage analytical data at scale.
Vertical Relevance's Experiment Broker provides the infrastructure to implement automated resiliency experiments via code to achieve standardized resiliency testing at scale. The Experiment broker is a resiliency module that orchestrate experiments with the use of state machines, the input is driven by a code pipeline that kicks off the state machine but also can be executed manually. Coupled with a deep review and design of targeted resiliency tests, it can help ensure your AWS cloud application will meet business requirements in all circumstances.
NEW YORK, NY – October 27, 2022 - Today, AWS announced the launch of the AWS Control Tower delivery and AWS Control Tower ready program. Vertical Relevance (VR), a financial services-focused consulting firm and Amazon Web Services (AWS) Advanced Tier Services Partner, announced it has achieved the AWS Service Delivery designation for Control Tower. The achievement signifies Vertical Relevance’s extensive and AWS-recognized understanding of best practices and validated success in delivering Control Tower implementations to its customers
Vertical Relevance, a financial services-focused consulting firm, announced today that it has achieved an Amazon Web Service (AWS) Service Delivery designation for Amazon Elastic Kubernetes Service (Amazon EKS), recognizing that Vertical Relevance has proven success in helping customers architect, deploy, and operate containerized workloads.
Organizations are rapidly adopting modern development practices – agile development, continuous integration and continuous deployment (CI/CD), DevOps, multiple programming languages – and cloud-native technologies such as microservices, Docker containers, Kubernetes, and serverless functions. As a result, they're bringing more services to market faster than ever. In this solution, learn how to implement a monitoring system to lower costs, mitigate risk, and provide an optimal end user experience.
Monitoring is the act of observing a system’s performance over time. Monitoring tools collect and analyze system data and translate it into actionable insights. Fundamentally, monitoring technologies, such as application performance monitoring (APM), can tell you if a system is up or down or if there is a problem with application performance. Monitoring data aggregation and correlation can also help you to make larger inferences about the system. Load time, for example, can tell developers something about the user experience of a website or an app. Vertical Relevance highly recommends that the following foundational best practices be implemented when creating a monitoring solution.