Security Data Lake Architecture -- Cribl Alternatives
A security data lake architecture uses a data pipeline to route security telemetry to cost-effective storage for long-term retention, forensic investigation, and compliance. Rather than sending all data to an expensive SIEM, organizations route high-value data to the SIEM for real-time detection and full-fidelity data to a data lake for long-term storage and ad-hoc analysis. These Cribl alternatives help build this architecture with different approaches to data routing and storage.
Choose your data lake storage platform (S3, Azure Blob, Azure Data Explorer, Snowflake, etc.) and define your data schema and partitioning strategy. Plan for data retention periods, access patterns, and query requirements for security investigation and compliance.
Set up your data pipeline to route data to both your SIEM (optimized, reduced data for real-time detection) and your data lake (full-fidelity data for long-term retention and forensics). The pipeline becomes the fan-out point for your security data architecture.
Transform data into a common schema (OCSF, ECS, or custom) before writing to the data lake. Partition data by time, source type, and severity to optimize query performance. Add metadata tags for efficient filtering during investigations.
Deploy a query engine (Azure Data Explorer, Athena, Trino, or Spark) to enable ad-hoc security analysis and threat hunting against the data lake. Create saved queries and dashboards for common investigation workflows.
Configure automated data lifecycle policies: hot storage for recent data (0-30 days), warm storage for investigation-relevant data (30-90 days), cold storage for compliance retention (90 days to years), and automated deletion after retention periods expire.
Pay-as-you-go (compute + storage) / Reserved capacity discounts
The most complete security data lake solution with petabyte-scale storage, powerful KQL analytics, and native integration with Microsoft Sentinel. Provides both storage and analytics in a single platform at lower cost than SIEM retention.
Free (open source, MPL 2.0)
High-performance open-source pipeline ideal for routing data to data lake storage (S3, Azure Blob, GCS). Rust-based throughput handles the high data volumes required for full-fidelity data lake ingestion.
Free (open source) / Enterprise support available
Security-native pipeline with built-in support for PCAP and network telemetry formats, essential for comprehensive security data lake architectures that include network forensics data alongside log telemetry.
Free (open source) / Commercial support via vendors
Proven open-source collector with plugins for all major object storage and data lake destinations. S3, GCS, Azure Blob, and HDFS output plugins enable reliable data lake ingestion at scale.
From $0.10/GB processed / Enterprise custom
Managed pipeline with built-in data lake routing and sensitive data scanning. Ensures PII and sensitive data are detected and redacted before reaching the data lake, addressing compliance requirements.
Microsoft's fast data analytics service for real-time analysis of streaming security data
Pay-as-you-go (compute + storage) / Reserved capacity discounts
Microsoft-centric organizations wanting a scalable security data lake with powerful KQL analytics at lower cost than SIEM
High-performance open-source observability pipeline built in Rust by Datadog
Free (open source, MPL 2.0)
Teams wanting the highest-performance open-source pipeline with Rust-based reliability for high-throughput data routing
Open-source security data pipeline with native support for security-specific data formats
Free (open source) / Enterprise support available
Security teams wanting an open-source, security-native data pipeline with transparent code and no vendor lock-in
Open-source unified data collector and log aggregator from the CNCF ecosystem
Free (open source) / Commercial support via vendors
Cloud-native teams wanting a lightweight, proven open-source data collector with a massive plugin ecosystem
Managed observability pipeline for routing and transforming telemetry data at scale
From $0.10/GB processed / Enterprise custom
Organizations already using Datadog that want managed pipeline capabilities with enterprise support and monitoring
A security data lake stores full-fidelity security data in cost-effective object storage (like S3 or Azure Blob) for long-term retention and ad-hoc analysis. A SIEM provides real-time detection, alerting, and investigation on a subset of security-relevant data. The two are complementary: the SIEM handles real-time detection on optimized data, while the data lake provides comprehensive storage for forensics, threat hunting, and compliance at a fraction of the cost of retaining all data in the SIEM.
Security data lake storage typically costs 5-20x less than equivalent SIEM retention. S3 Standard storage costs approximately $0.023/GB/month compared to SIEM ingest costs of $1-5/GB. Azure Data Explorer provides both storage and analytics at significantly lower cost than Splunk or Sentinel for long-term data. Organizations that move long-term retention from SIEM to data lake commonly save 60-80% on data storage costs.
Yes, but the query experience differs from a SIEM. Azure Data Explorer provides KQL-based analytics that are familiar to Sentinel users. AWS Athena and Trino enable SQL-based queries against S3 data. The tradeoff is that data lake queries typically have higher latency than SIEM searches (seconds to minutes vs. sub-second). Data lakes excel at ad-hoc investigations and threat hunting over historical data, while SIEMs are better for real-time alert-driven investigation.
Azure Data Explorer serves as both the storage and analytics layer for a security data lake. It ingests streaming data at high throughput, stores it with flexible retention policies, and provides powerful KQL analytics for security investigation. It is particularly compelling for organizations using Microsoft Sentinel, as KQL queries transfer directly between the two platforms. ADX can handle petabyte-scale data at significantly lower cost than keeping all data in Sentinel.
Microsoft's fast data analytics service for real-time analysis of streaming security data
ComparisonHigh-performance open-source observability pipeline built in Rust by Datadog
ComparisonOpen-source security data pipeline with native support for security-specific data formats
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