While working with a popular open-source search platform, a DevOps team needs to parse incoming data from multiple microservices. The aggregator tool in the stack must break down raw information, transform specific field values, and forward the content for indexing. Which approach helps the aggregator achieve a consistent structure?
Generate fresh configuration files after data is indexed
Combine all log files into a single text file
Store raw data in a cold archive for cost savings
Use a pipeline file that contains pattern matching rules
The aggregator is typically Logstash in the ELK stack, which uses pipeline configurations with parsing rules (such as grok filters) to standardize data fields before indexing. Combining logs into a single text file or generating new settings after indexing does not ensure proper field transformation. Storing raw information in a cold archive is useful for long-term retention, not data parsing.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is the ELK stack in DevOps?
Open an interactive chat with Bash
What does a pipeline file do in Logstash?
Open an interactive chat with Bash
What are grok filters, and why are they important in Logstash?
Open an interactive chat with Bash
CompTIA Cloud+ CV0-004
DevOps Fundamentals
Your Score:
Report Issue
Bash, the Crucial Exams Chat Bot
AI Bot
Loading...
Loading...
Loading...
IT & Cybersecurity Package Join Premium for Full Access