Turning Data into AI-ready Intelligence

It ensures only enriched telemetry reaches Databricks, providing enrichment, tagging, structure and visibility to data in motion to reduce the noise and improve clarity for your governed lakehouse.

While Databricks provides a powerful lakehouse for security data, getting the data to Databricks still poses a challenge to security, data, and engineering teams as network complexity and data volumes explode. DataBahn effortlessly connects, collects, and ingests data into Databricks. And it doesn't just transport the data - it parses, normalizes, filters, enriches, and analyzes the data in motion. This means that the data that reaches Databricks is immediately usable for investigation, analysis, and AI, making it easier to turn raw logs into insight.

DataBahn connects data sources with Databricks seamlessly. It supports cloud, on-prem, and hybrid data collection via agent-driven or agentless data collection from 500+ template sources; and it uses AI to parse and normalize custom applications too.

DataBahn is a Databricks cybersecurity solution partner, powering security data collection and ingestion for Databricks. The two solutions are tightly integrated, enabling seamless data flow. DataBahn ingests telemetry from diverse sources, deduplicates and filters data, applies enrichment and schema normalization – all while that data is in flight. We work closely with Databricks, tiering the data to fit into their medallion architecture to deliver high-quality datasets for analytics and AI into Databricks' governed lakehouse.

DataBahn delivers filtered, enriched, and orchestrated data ready for AI deployment into Databricks for faster and more effective automation and AI-powered decision-making.

AI operations need reliable, well-governed data. DataBahn prepares telemetry so that Databricks can apply ML models, automate detection, or support agentic workflows. The outcome is a smoother AI pipeline: from raw logs to contextual intelligence, enabling faster and more accurate insights. AI solutions can be deployed on Databricks storage for analytics and detection, and leverage Reef for visibility and querying of data in motion for accurate real-time analytics and threat detection.

DataBahn complements Databricks by handling upstream data operations – collection, parsing, normalization, analysis, and ingestion – and delivering clean and structured data into Databricks.

Databricks brings the scale and governance enterprises need to unify their stored data in a unified, central destination. With DataBahn, data arrives in that storage, optimized and deliberately managed to be usable, insightful, and actionable in real-time. This lays the foundation for a new era in cybersecurity, where enterprises leverage generative AI to unlock unprecedented visibility, clarity, speed, and agility, transforming telemetry data into actionable insights and intelligence.

DataBahn enables lossless data collection from off-the-shelf and custom applications, telemetry health monitoring, and remediation for pipeline breaks and schema drift.

As telemetry formats evolve or new sources are added, DataBahn simplifies the increasing complexity of collecting and ingesting data into storage solutions such as Databricks. With over 500 out-of-the-box integrations and an AI-powered auto-parser, adding new sources and translating data formats for movement into Databricks is automated for enterprise SOCs. With Smart Edge and Cruz, DataBahn provides failover handling and self-healing to ensure lossless data collection and movement for the ultimate data resilience.

DataBahn's Agentic AI adds automation and context-aware enrichment which learns and evolves, delivering continued improvement and optimization for enterprise security teams.

Enterprises today are on a path to AI-powered security and data operations as the key to turn vast volumes of data into intelligence. Storing that data and transporting it intelligently are two key components to creating that vision; but the outcome of that vision requires adaptive systems that don't just automate basic processes but can learn, evolve, and improve. Cybersecurity needs sophisticated, non-deterministic, and context-rich analytics that can build a deep understanding of what data matters and most importantly, why it matters, how that data is used, and what needs to be changed or alerted.

Leveraging agentic AI can automate data collection & log aggregation for smarter and automated pipelines, prioritize by contextual and dynamic security value, improve data governance, and provide insights into coverage gaps and vulnerabilities.

It centralizes visibility – in DataBahn for data in motion, and in Databricks for data at rest. This provides full, contextual, lineage-rich observability.

Instead of fragmented log collection and effort wasted in querying and analyzing data at the destination, security teams get a unified, context-rich view of the data from the source and throughout its lifecycle. Governance, search, and lineage tools make it easier to understand what flowed in, how it was enriched, where it's stored – enabling clearer, faster security decision-making.

It creates flexible, AI-native pipelines with governed storage that scales with evolving needs.

DataBahn's adaptive and flexible ingestion capabilities and Databricks' scalable governance create a durable data infrastructure. It ensures SOCs are prepared for new telemetry types and sources, AI innovations, regulation shifts, or expanding workloads – without rebuilding core pipelines or extensive security and data engineering effort.