{"id":28605,"date":"2023-10-12T17:03:24","date_gmt":"2023-10-12T15:03:24","guid":{"rendered":"https:\/\/www.dbi-services.com\/blog\/?p=28605"},"modified":"2026-02-20T10:05:47","modified_gmt":"2026-02-20T09:05:47","slug":"logs-from-filebeat-to-logstash-to-elasticsearch-3","status":"publish","type":"post","link":"https:\/\/www.dbi-services.com\/blog\/logs-from-filebeat-to-logstash-to-elasticsearch-3\/","title":{"rendered":"Logs from Filebeat to Logstash to Elasticsearch – 3"},"content":{"rendered":"\n

We sow together how to install<\/a> and configure<\/a> Filebeat, Logstash and Elasticsearch. Our log data has been sent from Filebeat to Logstash, then forwarded to Elasticsearch. In this blog, we will see how to configure Kibana and discover our data.<\/p>\n\n\n\n\n\n\n\n

Data Stream vs Index<\/h2>\n\n\n\n

A data stream lets you store append-only time series data across multiple indices while giving you a single named resource for requests. Data streams are well-suited for logs, events, metrics, and other continuously generated data.<\/p>\n\n\n\n

You can submit indexing and search requests directly to a data stream. The stream automatically routes the request to backing indices that store the stream\u2019s data. <\/p>\n\n\n\n

A data stream consists of one or more hidden, auto-generated backing indices.<\/p>\n\n\n\n

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This correspond to our use case, so we have a Data Stream “logs-generic-default<\/em>” and hidden index “.ds-logs-generic-default-2023.10.12-000001<\/em>“.<\/p>\n\n\n\n

Data view<\/h2>\n\n\n\n

In fact, Kibana requires a data view to access the Elasticsearch data that you want to explore. The data view is based on an index pattern which selects the data to use.<\/p>\n\n\n\n

An index pattern can point to one or more indices, data stream, or index aliases. For example, an index pattern can point to your log data from yesterday, or all indices that contain your data.<\/p>\n\n\n\n

To create your data view, click on “Create data view” in “Discover”:<\/p>\n\n\n\n

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Or, follow these steps:<\/p>\n\n\n\n