The intent to develop applications using Micro Services fails when we do not decide how the application clients interact with the micro services restful API’s. This is not a concern in building a monolithic style application, since there is just one set of endpoints that are typically replicated with a load balancer to distribute traffic and a circuit breaker to
In the land of Micro Services the question of analytics, complexity of algorithms, schema reporting gets well defined with a resilient data model. The culture and design principles should embrace failure and faults, similar to anti-fragile systems. As the Data system do not change as often as the surrounding application stack, it is important to a design the data base system
There is not much of complexity in terms of processes and communications between services in a Monolithic Application that deal with a single relational database. Most of the relational database use ACID transaction to process each request from the client. It means your database will have to process ‘insert, update and delete’ function quite often whenever there is a change or modification made.
Organization should be culturally aligned, as well as provide a subtle environment in adopting to a Micro Services architecture. Transitioning or Developing applications using Micro Services architecture is definitely not a cake walk. While the popularity of Micro Services is high, developers and testers really find it difficult in transitioning a monolithic style application to a micro services build architecture. This popularity is partly off the back of trends such
Testing Micro Services is an area that cannot be avoided or procrastinated to any point of time. Each services’ build before it reaches the deployment stage must be ensured that it passes the test criteria defined by the project team. While the Project team / Organization focuses on Designing and Developing Applications using Micro Services, it is also equally important to design Testing Strategies to test those Micro Services.
Combination of many Micro Services form a complex application. When it is necessary to test each service as a unit and component, it also becomes essential to test the service in an integrated environment and make sure the Application behaves as expected. More emphasis has to be given around User Acceptance testing and monitoring the application as a whole post production. The objective of applying test strategies
The aura around Cloud migration has settled down and the converts have become experts now. The business case for migrating applications to the Cloud has become a necessity with more enterprises embarked on this journey. There are a few technical challenges that still exist especially around data security and privacy but they are not show stoppers
Cloud is an increasingly credible and powerful infrastructure alternative for critical business applications. It’s a great way to avoid capital expenses and maintenance costs while gaining scalability on demand. Deploying SAP on the cloud can transform SAP landscape into an agile, cost-efficient and scalable system, enabling an organization to run better and respond faster to changing market
This article talks about predicting cost of AWS public cloud service usage in future, applying machine learning techniques with the input from AWS Cost explorer API. It has two portions 1. Retrieve Past Cost information (Data) from AWS using AWS Cost Explorer 2. Use the cost data and apply Machine Learning Techniques for future Predictions