Decoding Data Mesh: A Technical Exploration

In the ever-evolving landscape of data management, traditional centralised approaches often fall short of addressing the challenges posed by the increasing scale and complexity of modern data ecosystems. Enter Data Mesh, a paradigm shifts in data architecture that...

Harnessing the Power of the Data Lakehouse

As organisations continue to collect more diverse data, it is important to consider a strategic & viable approach to unify and streamline big data analytics workloads, ensuring it is optimised to drive data-driven decisions and enable teams to continue innovating...

The Modern Data Stack with dbt Framework

The Modern Data Stack with dbt Framework In today’s data-driven world, businesses rely on accurate and timely insights to make informed decisions and gain a competitive edge. However, the path from raw data to actionable insights can be challenging, requiring a...

The Importance of Feature Engineering in ML Modelling

The Importance of Feature Engineering in ML Modelling When building Machine Learning (ML) models, we often encounter unorganised and chaotic data. In order to transform this data into explainable features, we rely on the process of feature engineering. Feature...