What is Datable.io?

Datable.io provides a specialized streaming data pipeline engineered for security and DevOps teams seeking efficient telemetry data management. The platform empowers users to filter out irrelevant noise, enrich logs with critical context such as threat intelligence and geoIP data, and strategically route pertinent security events to designated tools. By processing data prior to ingestion by SIEMs or detection systems, it effectively tackles challenges like slow query performance, missed threat detections, and alert fatigue stemming from the rapid growth of telemetry volumes.

Integrating with over 100 sources and destinations, the system allows seamless connection to existing infrastructure without necessitating complex migrations. It features an intuitive interface for controlling data flow, enabling users to direct high-value data to premium analysis tools and historical logs to cost-effective storage solutions like Amazon S3. This methodology assists organizations in significantly reducing their observability expenditures while preserving complete system visibility and maintaining robust control over their security data ecosystem.

Features

Data Filtering: Eliminate low-value logs and noise before ingestion into downstream systems.
Data Enrichment: Augment data with context using threat intelligence feeds, GeoIP lookups, and custom user data.
Smart Routing: Conditionally direct telemetry data to different destinations based on content or source.
AI Code Generation: Utilize AI to automatically generate scripts for data transformations.
ML Pattern Detection: Identify unusual or anomalous patterns within log data using machine learning.
PII Masking: Automatically detect and redact personally identifiable information from data streams.
No-Code Pipeline Builder: Design and configure data processing pipelines via an intuitive graphical interface.
Log Deduplication: Identify and remove duplicate log entries to reduce data volume.
Multi-Source/Destination Integration: Connects with a wide range of over 100 data sources and destinations.
Live Data Sampling: Preview samples of incoming data in real-time before finalizing pipeline configurations.

Use Cases

Reducing SIEM and observability tool expenditures by filtering out non-essential data.
Enhancing the accuracy of security alerts by minimizing noise.
Enriching security logs with contextual information for improved analysis and investigation.
Directing specific data types to appropriate specialized tools (e.g., security events to SIEM, application metrics to APM).
Standardizing telemetry data formats from diverse sources for consistent processing.
Masking sensitive Personally Identifiable Information (PII) before storage or transmission.
Lowering data storage costs by routing archival data to less expensive storage tiers.
Detecting anomalies and unusual patterns in log streams using machine learning capabilities.