What is the Difference Between Data Observability and Data Monitoring?

The text discusses the evolution of data monitoring solutions in today's modern data stacks. It highlights that traditional data monitoring tools are outdated and unable to scale, making them insufficient for real-time data insight expectations. The solution proposed is data observability, which takes a proactive approach to solving data quality issues beyond simple monitoring and alerts. Data observability provides comprehensive insights into the internal state of a system by collecting and analyzing data from various sources in real-time. It also emphasizes that data observability platforms use machine learning to combine and analyze metadata around data quality, making it easier to identify and fix problems. The text further explains how data observability delivers better data insights than data monitoring and optimizes cloud-based data stacks by identifying bottlenecks, optimizing resource usage, addressing data quality issues, understanding query performance, and troubleshooting.