We need a practical and scalable approach to understand the cause-effect relationship between data sources and events across complex infrastructure of VMs, containers, networks, micro-services, regions, etc. Machine learning is particularly useful for such problems where we need to identify “what changed”, since machine learning algorithms can easily analyze existing data to understand the patterns, thus making easier to recognize the cause. This is known as unsupervised learning, where the algorithm learns from the experience and identifies similar patterns when they come along again.
Elasticsearch is currently the most popular way to implement free text search in your application. This blog post is an introduction to Elasticsearch including components and data types. It covers the some of the basic but important concepts of Clusters, different types of Nodes, Documents, Mappings, Indices, and Shards.