Pipeline data processing—log parsing, cleaning, and field extraction

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What problems does Pipeline data processing solve?

Use Pipeline to process raw data into queryable and associable fields

When raw log fields are messy, formatting is inconsistent, or contains sensitive information, Pipeline completes parsing, field extraction, desensitization, and standardization before data enters the platform, making subsequent queries, alerts, and association analysis more reliable.

01Is the original log hard to find? First, parse the key fieldsPipeline supports parsing, extracting, modifying, filtering, and aggregating data locally or centrally within DataKit.
02Once fields are unified, logs can be associated with metrics, links, and networksThe processed data structure is clearer, allowing for filtering, grouping, and aggregation by unified fields.
03Sensitive information cannot be stored in the database? Desensitization is applied before processing the chainPipeline can replace phone numbers, ID numbers, tokens, accounts, IPs, or business-sensitive fields before data is stored.

Is the original log hard to find? First, parse the key fields

Pipeline supports parsing, extracting, modifying, filtering, and aggregating data locally or centrally within DataKit. Teams can convert host, service, status, duration, trace_id, and other information in messages into fields or tags, making subsequent retrieval, alerting, and aggregation analysis more stable.

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Is the original log hard to find? First, parse the key fields

Once fields are unified, logs can be associated with metrics, links, and networks

The processed data structure is clearer, allowing for filtering, grouping, and aggregation by unified fields. For example, after standardizing service names, hosts, status codes, and request paths in logs, you can continue to associate APM Traces, infrastructure metrics, BPF network logs, and alert events.

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Sensitive information cannot be stored in the database? Desensitization is applied before processing the chain

Pipeline can replace, delete, or encrypt mobile phone numbers, ID numbers, tokens, accounts, IPs, or business-sensitive fields before data is stored. For security, compliance, and multi-team collaboration scenarios, the earlier desensitization is completed, the more controllable subsequent retrieval and sharing will be.

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Sensitive information cannot be stored in the database? Desensitization is applied before processing the chain

Frequently asked questions

What problems does Pipeline data processing mainly solve?

Pipeline is used to parse raw logs and observation data before data is stored, completing field extraction, format conversion, filtering, desensitization, and standardization, making subsequent retrieval, alerting, aggregation, and association analysis more accurate.

What data can the Pipeline process?

Pipeline can handle various types of observation data such as logs, metrics, objects, links, and networks. Common scenarios include parsing fields such as service, host, status, duration, trace_id, and standardizing field naming and types.

Why is data anxious before warehouse entry?

Immunodaction before entry can prevent sensitive information such as phone numbers, ID numbers, tokens, and accounts from entering subsequent queries, sharing, and archiving processes, reducing data leakage and compliance risks at the source.

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