Popularly over the past few years, application modernization solely meant cloud migration. Fortunately, however, there are many more options for companies to modernize their applications as the wave of digital transformation continues to accelerate. Below are just some of the more specialized features offered in the cloud that have evolved beyond legacy capabilities and are available for modernizing apps.
1. Cloud Computing for application modernization — The cloud's benefits are well promoted: access to the latest languages, agile technologies, collaborative platforms, and scalable environments. Most companies have some aspect of their operations within the cloud.
2. Containers to modernize application workloads in the cloud — Containers are a cloud virtualization approach to separating run-time environments for apps and workloads in the cloud. These virtual environments are beneficial to developers, equipping them with consistent environments, and sandbox-style isolation.
3. APIs to modernize application integration — Application programming interfaces allow one program to talk to and access data from another. APIs have been paramount in promoting integrations between third-party software that have enhanced customer experiences.
4. Microservices to modernize application implementation — Microservices architecture offers loosely connected services for applications increasing the ease of IT development, implementation, and updates. The separation between services allows for maintenance robustness, without compromising the entire IT ecosystem.
5. AI / ML Automation to modernize application performance — Artificial intelligence and machine learning use intelligent algorithms to automate manual IT processes and workflows, conduct complex data analysis (think Big Data analysis), and actively monitor and optimize application performance.
6. Data Lakes for modernizing application storage and processing — Data lakes store Big Data assets, a collection of structured, semi-structured, and unstructured data, ready for processing. These massive and scalable data collections support data analytics through SQL queries, Big Data analytics, and machine learning.