Vertical Relevance’s Monolith to Microservices Foundation provides a proven framework on how to break the monolith and deliver improved agility to increase the pace of innovation and drive value to your customers and your business. By following the approaches laid out in this Foundation, customers will manage risk and lay out a consistent iterative approach to decompose the monolith into cloud native microservices, following a well-defined process.
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.
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.
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.
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.
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.