As Augmented Reality (AR) becomes more prevalent, imagine entering a world where digital overlays seamlessly blend with your physical surroundings, enriching your reality with interactive information and immersive experiences. This is the promise of AR, a groundbreaking technology reshaping how we interact with the digital world.

Data integrity refers to protecting data from anything that can harm or corrupt it, whereas data quality checks if the data is helpful for its intended purpose. Data quality is a subset of data integrity. One can have accurate, consistent, and error-free data, but it is only helpful once we have the supporting information for this data. Data integrity and quality are sometimes used interchangeably in data management, but they have different implications and distinct roles in enhancing data usability.

Not long ago, I was handed a problem that’s no stranger to the world of programming: making asynchronous threads communicate effectively within the same process. Given the widespread nature of this issue, I expected to find an existing solution to resolve it. My search led me to the concept of message queue, which seemed promising for streamlining this communication challenge.

Outsourced engineering teams are gaining in popularity and many investors have started to treat them as a requirement.