Learning how to use transforms
In this section, we are going to dive right into the world of transforming stream or event-based data, such as logs, into an entity-centric index.
Why are transforms useful?
Think about the most common data types that are ingested into Elasticsearch. These will often be documents recording some kind of time-based or sequential event, for example, logs from a web server, customer purchases from a web store, comments published on a social media platform, and so forth.
While this kind of data is useful for understanding the behavior of our systems over time and is perfect for use with technologies such as anomaly detection, it is harder to make stream- or event-based datasets work with Data Frame Analytics features without first aggregating or transforming them in some way. For example, consider an e-commerce store that records purchases made by customers. Over a year, there may be tens or hundreds of transactions for each customer. If the e-commerce...
