Log ingestion and data preparation
Collecting, parsing, and transforming log data
Data Ingestion
- System collection: Central collection of log and event data from various sources such as servers, applications, containers, cloud services, or network components via agents, APIs, or standardized protocols such as Syslog.
- Data transfer & streaming: Reliable and scalable transport of log data to the central platform via streaming mechanisms, messaging systems, or secured network protocols.
- Buffering & queueing: Temporary buffering of log data to stabilize the data pipeline and compensate for load peaks, network interruptions, or delayed processing steps.
- Format handling: Support for different log formats and protocols to ingest heterogeneous data sources without changes to the original log structure.
- Timestamp capture: Consistent capture and forwarding of time information from log sources to ensure correct chronological classification of events.
- Data validation: Verification of incoming log data for completeness and structural consistency to detect faulty or incomplete events at an early stage.
- Scalable data ingestion: Ability to ingest large volumes of log and event data in parallel and with high performance to ensure continuous data collection even in dynamic IT environments.
Data Processing
- Parsing & normalization: Structuring and standardizing log data into a consistent data model to facilitate analysis and correlation.
- Rule-based classification: Categorization and prioritization of events based on predefined or custom-defined rules, e.g., by severity, source, or event type.
- Metadata enrichment: Contextual enhancement of log data with additional information such as asset data, user context, or geo information to improve analysis, security detection, and troubleshooting.
- Event indexing: Indexing of log and event data to support fast search queries, real-time analyses, and security analytics.
- Data integrity & timestamps: Ensuring the integrity and traceability of log data through verified timestamps and integrity checks.
- Compressed storage: Efficient storage and archiving of large volumes of log data to optimize storage requirements and performance.
- Compliance & forensics: Provision of traceable and audit-proof log data for compliance requirements, audits, and forensic investigations.
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