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POWERED BY AGENTIC INTELLIGENCE

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Enterprise Data Observability.

Bring quality, lineage visibility, governance, monitoring, and operations together across your data ecosystem.

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| Core Pillars |

Complete visibility across your entire data landscape

Enterprise-grade contracts, policies, and lineage

AI-powered remediation and optimization

| Enterprise Data Trust Crisis |

Data Quality Chaos

Inconsistent standards and manual rule creation result in huge amount of enterprise data going unused due to quality concerns.

Lineage Blindness

Inability to trace data from source to consumption makes schema changes risky and compliance audits difficult.

Cost Opacity

Cloud costs spiral without granular visibility into pipeline-level resource consumption.

Governance Gaps

Data contracts live in spreadsheets, leading to broken downstream consumption and no clear accountability.

Manual Operations

Capture and remediation processes rely on human intervention, resulting in inconsistency and operational risk.

| Key Capabilities |

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| Insights |

Introducing Databricks GenAI Partner Accelerators for Data Engineering & Migration
December 9, 20256 min read

Introducing Databricks GenAI Partner Accelerators for Data Engineering & Migration

Enterprises face increasing pressure to modernize their data stacks. Teams need to move away from legacy ETL systems and complex on-premises platforms and shift toward simpler, scalable architectures.

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Eagle Eye | Lakebase — A Next-Generation Agentic Observability Platform on Databricks
January 28, 20266 min read

Eagle Eye | Lakebase — A Next-Generation Agentic Observability Platform on Databricks

Eagle Eye is a comprehensive data observability platform powered by Aquila, a goal-driven agentic AI, and built natively on the Databricks ecosystem. At the foundation sits Databricks Lakebase (PostgreSQL), which serves as the system of record for metadata, observability intelligence, and persistent agent memory.

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Leading Materials Science Company
Apr 20, 202615 min read

Leading Materials Science Company

Fragmented data across the Databricks layer led to manual validation and lack of centralized monitoring. This case shows how unified observability, ML driven anomaly detection, and automated profiling improved data consistency and accelerated issue resolution.

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