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.
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.
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.
The Data Pipeline Foundations provide guidance on the fundamental components of a data pipeline such as ingestion and data transformations. For data ingestion, we heavily leaned on the concept of data consolidation to structure our ingestion paths. For transforming your data, be sure to utilize our step-by-step approach to optimize architecting your data for end-user consumption. By following the strategies provided, your organization can create a pipeline to meet your data goals.
The Vertical Relevance Automated Performance Testing Framework lowers the barrier to entry in performance tests by providing a starting point upon which a mature solution can be built to meet the needs of your organization. By following this guidance, you can gain confidence that your production systems are going to meet the current and future demands of your organization and customers
Regular and robust resiliency testing provides assurances your cloud application can weather whatever outages may occur. The Vertical Relevance Resiliency Automation Framework can help guarantee your workload can prevail through disruptions and failures and prevent the damaging consequences of an outage. Reach out to us to learn more about how we can help you meet your resiliency requirements.
While there are many different components involved with securing the cloud, a carefully architected IAM strategy is paramount. A solid IAM strategy allows engineers to develop quickly, provides key stakeholders with a comprehensive picture of the actions that can be performed by different IAM principals, and results in a more secure cloud environment overall. Security without reasonable user experience can lead to workarounds and dysfunction, and by implementing this solution, both key stakeholders and engineers can all be satisfied with the result.
Best Practices for Designing and Automating Scalable Networks on AWS. Vertical Relevance highly recommends that the following foundational best practices be implemented to create a sustainable AWS Network that will support an enterprise-level organization.
This AWS Network Foundation solution provides an opinionated, foundational set of network architectures, design considerations, automated solutions, and best practices that help our clients “quick start” their AWS journey with a strong foothold while ensuring the scalability and growth they are likely to require as their AWS footprint grows. Vertical Relevance highly recommends that the following foundational best practices be implemented to create a sustainable AWS Network that will support an enterprise-level organization.