Data360: AI-First Enterprise Data Intelligence Platform

Case Study: AI Engineering & Data Operations Transformation

Data360 is a production-grade, enterprise data intelligence platform that fundamentally transforms how organizations interact with their data infrastructure. Traditional data operations suffer from fragmented metadata, siloed lineage, and heavy dependence on IT teams for even basic data questions. With AI-enabled engineering, Hexaview built Data360 as a unified operational intelligence layer that brings together metadata, lineage, pipelines, and AI-driven analytics into a single, scalable platform.

What previously took days of manual investigation across tools can now be resolved in seconds—delivering true data democratization at scale.

The Challenge

Fragmented data knowledge spread across Databricks, legacy systems, and custom pipelines.
Heavy dependency on IT and data engineering teams for data discovery and troubleshooting.
Limited visibility into pipeline health, lineage, and data quality.
Lack of self-service analytics for non-technical business users.
Difficulty performing impact analysis and root-cause detection across platforms.

The AI-Enabled Solution

Unified Metadata Aggregation: Multi-source ingestion with intelligent deduplication and source tracking.
PostgreSQL-Backed Intelligence Layer: 25+ optimized tables for scalable, low-latency queries.
Databricks Deep Integration: Unity Catalog sync, DLT pipelines, and Lakeview dashboards.
Dual AI Agents: Natural language analytics via LangChain and Databricks AI/BI Genie.
Persona-Based Views: Curated data experiences for business, operations, and engineering users.
Production-Grade Security: RBAC, encrypted tokens, and controlled access boundaries.

The Journey: From Fragmentation to Unified Intelligence

AI-Enabled SDLC: Designed and built using AI-augmented engineering practices.
Metadata & Lineage First: Column-level lineage, medallion architecture visualization, and schema impact analysis.
Operational Visibility: Task-level pipeline execution monitoring and DLT data quality expectations.
Platform Boundaries: Databricks handles execution; Data360 delivers unified discovery and visibility.
Performance at Scale: Intelligent caching and optimized queries deliver sub-second response times.

Key Features

Unified Data Catalog
Aggregates metadata from Databricks Unity Catalog, legacy systems, and extensible future sources into a single view.
Medallion Architecture Visualization
Interactive Bronze–Silver–Gold flow representation with real-time pipeline status.
DLT Pipeline Intelligence
Visualize Delta Live Tables pipelines, data quality expectations, and execution metrics.
Embedded Databricks Dashboards
Lakeview dashboards embedded via iframe, enabling in-platform interaction and monitoring.
AI-Powered Data Discovery
- Global AI Agent using LangChain with multi-LLM support (OpenAI, Anthropic, Perplexity).
- Natural language to SQL generation, execution, and auto-visualization.
- Context-aware deep linking to Databricks AI/BI Genie.
Cross-Platform Lineage & Impact Analysis
Column-level lineage, schema change impact detection, and root-cause analysis in minutes.
Persona-Based Curation
Tailored views for Client Reporting, Operations, and Data Engineering teams.

Maintenance Revolution: Vibe Maintenance

Before: Manual troubleshooting, ticket-driven data access, days of investigation.
After: Unified visibility, AI-powered discovery, answers in seconds.
Automated Documentation: AI-generated metadata explanations and lineage context.
Operational Confidence: Faster onboarding and dramatically reduced support load.