As businesses increasingly invest in technology and talent focused on data engineering and analytics, they want to know whether they are benefiting. So how do you calculate the return on investment fo

All software systems are in a constant state of evolution. This makes it impossible to select a truly future-proof technology stack for your data platform, making an eventual migration inevitable. In

Real-time data processing has steadily been gaining adoption due to advances in the accessibility of the technologies involved. Despite that, it is still a complex set of capabilities. To bring stream

Data has been one of the most substantial drivers of business and economic value for the past few decades. Bob Muglia has had a front-row seat to many of the major shifts driven by technology over his

For business analytics the way that you model the data in your warehouse has a lasting impact on what types of questions can be answered quickly and easily. The major strategies in use today were crea

Feature engineering is a crucial aspect of the machine learning workflow. To make that possible, there are a number of technical and procedural capabilities that must be in place first. In this episod

Feature engineering is a crucial aspect of the machine learning workflow. To make that possible, there are a number of technical and procedural capabilities that must be in place first. In this episod

Data transformation is a key activity for all of the organizational roles that interact with data. Because of its importance and outsized impact on what is possible for downstream data consumers it is

Architectural decisions are all based on certain constraints and a desire to optimize for different outcomes. In data systems one of the core architectural exercises is data modeling, which can have s

Data engineering is all about building workflows, pipelines, systems, and interfaces to provide stable and reliable data. Your data can be stable and wrong, but then it isn't reliable. Confidence in y