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.
In this use case learn how a leading financial services company obtained a data platform that is capable of scaling to accommodate the various steps of the data lifecycle along with tracking of all the steps involved including cost allocation, parameter capturing, and the providing of metadata required for integration of the client’s third party services.
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.
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.
Vertical Relevance, a financial services-focused consulting firm and Amazon Web Services (AWS) Advanced Tier Services Partner, today announced it has achieved the AWS Service Delivery designation for Amazon Systems Manager.
In non-production AWS environments today, security and IAM are often deprioritized to increase velocity of development. Vertical Relevance’s Role Broker was created as an alternative to the costly, error-prone strategies that many organizations use to manage their IAM roles in non-production environments.
By engaging AWS and Vertical Relevance, the customer was able to automate their deployment of SCPs across their AWS Organization. After implementing a multi-stage pipeline that performed automated checks to ensure that SCPs were compliant with security policies and did not contradict service usage across their accounts, the customer could quickly and reliably make changes to SCPs. With these changes in place, the Security Team updated organization-wide security controls quicker and with more confidence, which drastically improved the overall security posture of the customer.