When your logs don’t follow a standard format, it can be difficult to extract valuable information, like key-value pairs and nested JSON objects. Grok parsing lets you define flexible patterns that match unstructured log data so you can extract specific fields to query, filter, and visualize. In this video, you’ll learn how to: • Clone and adjust an existing integration pipeline to improve log processing • Edit Grok parsing rules to extract JSON objects from log messages • Create a new Grok parser to target specific attributes • Avoid breaking existing logs while adding new parsing logic By refining your Grok parsers, you can make your logs more useful for analytics, dashboards, or alerts, and get even more value from your logs. Useful resources: 👉 https://docs.datadoghq.com/logs/log_configuration/parsing/ 👉 https://docs.datadoghq.com/logs/guide/log-parsing-best-practice/ 👉 https://docs.datadoghq.com/logs/log_configuration/pipelines/

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