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Zero trust security is a security model that assumes all network traffic is untrusted, regardless of its source or destination. This approach requires strict verification of every request, regardless of whether it originates from inside or outside the network. In microservices architectures, where applications are composed of multiple independent services that communicate over the network, or across clouds, zero trust security is essential to protect sensitive data and prevent security breaches. In this article, we will explore how zero trust security can be implemented in microservices architectures to enhance security and protect sensitive data.

What does fitness mean to you? Every individual this question is asked to, will probably give a different answer. While one person may define fitness as the ability to run regularly, another might see it as simply being able to play with their grandchildren without fatigue and risk of injury. If we were to look at the common ground, it is about nurturing a body that can support and sustain us through our life. It is about the quality of life. In this blog, we will talk about the different components of fitness, small changes to help incorporate these in your life and finally micro workouts.

Creating microservices within the constraints of multi-tenancy, privacy and compliance can present challenges that need to be thought through. In this blog we will discuss some of these challenges especially around resource contention, data isolation, data governance and compliance, focussing on potential thought frameworks to consider.

Attempting to fit every component of a monolithic architecture into a microservice is like trying to fit a square peg into a round hole. Not everything belongs, and forcing it only distorts the integrity of both the structure and the function. In this blog we will provide a framework to identify which functionalities or modules are ripe for migration to a microservice and which are not.

This blog post will take you through the process of building a recommendation system and the concept of embeddings, vector databases and various use cases. These concepts are not only limited to recommendation systems but are widely used in various domains such as image recognition, natural language processing, semantic search, and anomaly detection. The ability to represent complex, high-dimensional data in a dense, lower-dimensional space is a fundamental technique in machine learning.