When considering the list for Open Source Identity and Access Management solutions out there Keycloak is an interesting option as it is the upstream open source project to RedHat’s commercial SSO (Single Sign On) product. …

Apache Superset is one of the neater data visualization and exploration tools that I have come across. Positioned as an open-source cloud-native application, Superset was created by Maxime Beauchemin (the creator of Apache Airflow — another open-source Python-based project for workflow management) while he was working at Airbnb.

The outcome that every organization expects to drive through the adoption of product analytics is about scaling customer empathy by understanding exactly how users interact with their product and use these insights for data-driven growth and innovation. …

If you are a Data Engineer or Data Scientist who uses the Azure Cloud to build your analytics pipeline or service, you would frequently come across the problem of controlling who gets access to the resources in your pipeline and what they can do once they have access, in the…

The “worth” or value of networks is often expounded upon across a wide variety of subjects from the world wide web to the value of digitally-related companies. The bone of contention that arises mainly is, how can this be quantified with a mathematical base. There are a few empirical laws…

Bloom Filters are quite simply put space-efficient, probabilistic data structures which help answer the question of set membership. From a mathematical lens say that you need to verify whether an element x belongs to a set A , if the Bloom Filter as a data structure returns true for x…

At it’s heart Azure Synapse Analytics, quite simply put is a Massively Parallel Processing (MPP) Service, which run on the Microsoft SQL Server engine, providing concurrency, elasticity and scalability. How Azure Synapse Analytics differs from other on-premises data warehouses lies purely in the manner that it has been architected for…

When working with data engineering solutions in the real world, the main problem faced by data engineers in general is that data does not usually end up nicely formatted for analytics on it’s own ‘magically’ . It usually takes several preprocessing steps which involve getting data from the source, ingesting…

To those whom learn Statistics in their high schools and undergraduate courses it comes across often as a rather “boring” , “pedantic”, “math-heavy” subject with no intrinsic real world applications. I feel that this maybe due to the fact that sometimes the motivation for learning the subject is not clearly…