Elasticsearch is fine for time series data. A lot of tasks are actually easier with time series data. You add a field called `@timestamp` to your documents and a lot of analysis becomes possible, like date histograms, date range queries, ML jobs, etc.
Immutable time series data like logs and metrics are a great fit for Elasticsearch due to the way Lucene stores data. Documents in Lucene are immutable so an update in Elasticsearch is creates a new document and places a tombstone marker on the old one. Immutable data means you don't have to tolerate those inefficiencies.
Dashboards don't load the entire dataset by default. I can't remember what the exact default time range is but I think it's ~15 minutes or so. They're fairly quick to render in Kibana.
Elasticsearch is a great tool for observability data (logs, metrics, and APM data). Elastic's tooling makes a lot of this really easy in most cases.
Immutable time series data like logs and metrics are a great fit for Elasticsearch due to the way Lucene stores data. Documents in Lucene are immutable so an update in Elasticsearch is creates a new document and places a tombstone marker on the old one. Immutable data means you don't have to tolerate those inefficiencies.
Dashboards don't load the entire dataset by default. I can't remember what the exact default time range is but I think it's ~15 minutes or so. They're fairly quick to render in Kibana.
Elasticsearch is a great tool for observability data (logs, metrics, and APM data). Elastic's tooling makes a lot of this really easy in most cases.