Parseable, an open observability datalake, handles 100M time-series/min

Observability Data Lake Solution

Parseable is an open observability data lake designed for telemetry data. It is written in Rust and uses an open core model with open Parquet and open standards. The platform is promoted for its ability to manage high cardinality data through a columnar design.

Key Features and Capabilities

The platform unifies logs, metrics, and traces within a single binary, providing views for alerts, dashboards, and distributed traces. It also includes features like service maps, error pages, and AI analysis for root cause analysis. Parseable supports SQL and natural-language querying, along with advanced access control and governance.

Deployment and Integration

Parseable offers flexible deployment options, including self-hosted open-source, managed cloud, and enterprise deployments with Bring Your Own Cloud (BYOC). It is designed to integrate with existing telemetry agents, data sources, visualization tools, and authentication systems.

AI and Machine Learning Integration

The platform incorporates LLMs and traditional ML models to analyze telemetry data. This functionality is used for summarizing trends, identifying anomalies, understanding complex patterns, and performing root cause analysis to derive actionable insights.