Why do we need Logstash?

Logstash mainly started as a project for managing logs, but it has since been extended to analyze any type of data, be it event data, timestamped data, application logs, transactional data, CSV data, file input, and so on. Data can be structured, unstructured, or semi-structured, which makes it difficult to convert the data into a proper format. To manage logs of different types coming in from different systems, we require a tool which is powerful in handling the various different types of log data and analyzing it in near-real-time to generate insights from the log data. Logstash helps you to collect data from multiple systems into a central system wherein data can be parsed and processed as required. Also, Logstash helps you to gather the data from multiple systems and store the data in a common format, which is easily used by Elasticsearch and Kibana.

Logstash allows you to pipeline data, which can be extracted, cleansed, transformed, and loaded to gain valuable insights from the data. In such a way, Logstash does the work of Extract, Transform, and Load (ETL) , a popular term used in data warehousing and business intelligence domains. Logstash extracts the data from multiple systems, performs some operations to process or transform the log, such as filtering data, removing fields, adding fields, grokking data, and so on, followed by loading the processed data.

Logstash is often referred to as a Swiss Army knife, as it can process any type of data and, due to the fact that it contains a wide range of inputs for inputting data from different sources, filters to transform the data, and outputs to store the data, making Logstash a go-to tool for all your data.

Let's have a look at some of the key features of Logstash.