How AIOps Conquers Performance Gaps on Big Data Pipelines - The New Stack

If your data pipelines are growing in complexity and beyond the point where you can manage them, you're not alone. Today, they have become so massive and are crisscrossed by so many dependencies that it can be hard to see how all the components fit together, and hard to identify issues and opportunities that impact app performance and availability. Data stacks combine many disparate elements for data gathering and analysis, among other functions -- and exponential data growth in most organizations only adds to the challenge. In such an environment, simply monitoring performance and taking reactive measures when performance lags is no longer a viable approach. Today, with AIOps (Artificial Intelligence for IT Operations), a correlated data model helps you discover the full context of your apps and system resources so that you can adequately plan, manage, and improve performance.