## New highlights added [[2023-04-03]] > While Druid contains many features commonly found in search systems, such as the ability to stream in structured and semi-structured data and the ability to search and filter the data, Druid isn’t commonly used to ingest text logs and run full text search queries over the text logs ([View Highlight](https://read.readwise.io/read/01gx2qme2paafpecyvmy1zj7xw)) > Druid creates an indexed copy of raw data that is highly optimized for analytic queries. Druid runs queries over this indexed data, called a ['segment'](https://druid.apache.org/docs/latest/design/segments.html) in Druid, and does not pull raw data from an external storage system as needed by queries. ([View Highlight](https://read.readwise.io/read/01gx2qnz8n38xq77bav2hacb0q)) #### Where does Druid fit in my big data stack? > A common streaming data oriented setup involving Druid looks like this: Raw data → Kafka → Stream processor (optional, typically for ETL) → Kafka (optional) → Druid → Application/user > A common batch/static file oriented setup involving Druid looks like this: Raw data → Kafka (optional) → HDFS → ETL process (optional) → Druid → Application/user ([View Highlight](https://read.readwise.io/read/01gx2qr2nngx2v1vmskrnwnjsn))