Elasticsearch 7 and Elastic Stack - In Depth and Hands On! - Introducing Logstash

Logstash is a powerful tool for data processing, acting as a bridge between data sources and destinations like Elasticsearch. It can import data from various sources, transform it, and export it to multiple destinations. Logstash can structure unstructured data, filter, and anonymize it, ensuring legal compliance. It also offers buffering capabilities to manage data spikes and guarantees at least once delivery. Modern usage involves integrating with Filebeat for efficient data handling, while legacy methods involve direct use on servers. The ELK stack has evolved into the Elastic stack, emphasizing the Beats framework.

What is the primary role of Logstash in data processing?

• To store data permanently

• To visualize data

• To sit between data sources and destinations, processing data

• To delete unnecessary data

Which of the following is NOT a feature of Logstash?

• Data transformation

• Geolocation lookups

• Data visualization

• Data anonymization

How does Logstash handle data spikes from web servers?

• By discarding excess data

• By storing data temporarily on the server

• By slowing down data processing

• By serving as a buffer to manage the load

What is the modern approach to ingesting web logs into Elasticsearch?

• Using Logstash directly on web servers

• Using Filebeat to send data to Logstash

• Using a database to store logs

• Using a cloud service to process logs

What does the 'L' in the ELK stack stand for?

• Linux

• Log Analyzer

• Logstash

• Load Balancer