If you're looking to modernize your applications, containers are a powerful tool that you can't afford to ignore. Containers are portable, lightweight, and allow for easy deployment, scaling, and management of applications. They also provide more robust operability and engineering agility. However, as you adopt containerized architectures, you may face challenges in managing the numerous unique services that come with them. That's where container orchestration comes in. In this solution, we explain how specific tools work together to provide a streamlined and efficient way to manage containers at scale, enabling organizations to improve their engineering agility and operational efficiency.
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.
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.
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 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.
In this use case learn how a leading financial services company obtained a carefully planned, scalable, and maintainable testing framework that dramatically reduced testing time for their mission-critical application and enabled them to constantly test the applications releasability.
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.