Agentic ELT Automation Suite

**Overview**

Designed for enterprise data engineering teams, this solution delivers end-to-end medallion layer automation (L1 to Golden) on Databricks powered by **LLM-driven agentic ELT orchestration and human-in-the-loop governance.**

This Agentic ELT Accelerator enables organizations to automatically enrich metadata, generate standardized Silver layers, deploy Delta Live Tables pipelines and create Gold-layer analytics using conversational AI. By combining **LLM-powered agents, Streamlit-based control workflows and Databricks-native execution**, the framework transforms raw data into governed, business-ready insights with minimal manual effort.

With intelligent metadata enrichment, automated SQL generation and one-click deployment across Silver and Gold layers, the accelerator reduces engineering effort by up to **60%**, while improving **data quality, standardization, lineage and trust** across the Lakehouse.

**Business Challenge**

Organizations building analytics on Databricks commonly face the following challenges:

1. **Poor or Missing Metadata**

Tables and columns often lack meaningful descriptions, making datasets hard to discover, understand and govern. Manual documentation is time-consuming, inconsistent, and rarely maintained.

2. **Manual Medallion Layer Engineering**

Creating L1 views, Silver curated tables and Gold analytics requires repetitive SQL development, naming standardization, constraint handling and deployment orchestration—slowing down delivery and increasing errors.

3. **Lack of Governance in Automation**

Pure automation risks incorrect assumptions, while manual validation slows progress. Teams need **AI-driven acceleration with human control**, not black-box pipelines.

**Architecture Overview – Medallion + Agentic Orchestration**
**Silver Layer 1 (L1)**

Standardized relational views generated automatically from source schemas, enriched with metadata and deployed using automated Databricks jobs.

**Silver Layer 2 (L2)**

Curated Delta Live Tables built using agent-generated DLT SQL with primary keys, SCD logic, constraints, audit columns and data quality rules.

**Gold Layer**

Conversational analytics layer where users describe business requirements in natural language and agents generate optimized SQL models accordingly.

**Agentic Control Plane**

A Streamlit-based application orchestrates configuration, review, approval, generation and deployment—bridging AI automation and enterprise governance.

**Pipeline Flow**
**Metadata Enrichment Agent (Foundation Layer)**

The pipeline begins with LLM-driven metadata enrichment, applied at both table and column levels.

**How It Works**

- **The agent scans selected tables and schemas.**
- **It generates intelligent descriptions for:**

1. **Tables**
2. **Columns**
3. **Semantic relationships**

**Before persisting any metadata:**
- **AI-generated comments are presented to users for review.**
- **Users can approve, edit or refine descriptions.**
- **Once approved, metadata is saved back to the catalog.**

**Key Capabilities**

- **Automatic schema documentation**
- **Semantic enrichment using LLM reasoning**
- **Human-in-the-loop (HITL) validation for governance**
- **Improved discoverability and trust in Unity Catalog**
- **This ensures metadata accuracy without sacrificing control, forming a reliable foundation for downstream Silver and Gold layers.**

**Silver SQL Generator Agent (L1 → L2)**
**After metadata enrichment:**
**L1 (Silver Layer 1)**

- **Standardized SQL views are generated automatically.**
- **Naming conventions are enforced (UPPER_SNAKE_CASE, numeric handling).**
- **Metadata and audit columns are added.**
- **SQL scripts are stored in a configurable folder.**

**A single-click Deploy SQL Runner Job:**

- **Creates a Databricks Job**
- **Executes all SQL files**
- **Publishes views into the target catalog**

**L2 (Silver Layer 2)**
**The agent generates Delta Live Tables (DLT) SQL by identifying:**

- **Primary keys**
- **Sequence columns**
- **Delete flags**
- **Data quality constraints (e.g., NOT NULL rules)**

**Generated DLT SQL includes:**

- **Streaming source view definitions**
- **Target table schemas with constraints**
- **SCD Type 1 apply-changes logic**
- **Z-Ordering**
- **Audit columns**

**The application automatically:**

- **Creates a DLT pipeline**
- **Executes all generated DLT SQL**
- **Publishes curated Silver L2 tables**
- **Conversational Gold Agent**

**The Gold layer is driven by a conversational interface.**

- **Users describe business requirements in natural language.**

**The agent:**

- **Clarifies intent**
- **Generates complete Gold-layer SQL**
- **Applies consistent logic and optimizations**
- **SQL is production-ready and aligned with upstream Silver models.**
- **This enables analytics teams to move from questions to datasets, without writing complex SQL.**

**Key Capabilities**

- **LLM-Driven Metadata Enrichment with HITL governance**
- **Automated L1, L2 and Gold SQL Generation**
- **DLT Pipeline Creation and Execution**
- **One-Click Deployment via Databricks Jobs**
- **Conversational Analytics Modeling**
- **Enterprise Naming, Quality and Audit Standards**
- **Unity Catalog Lineage & Governance Alignment**

**Marketplace Deliverables**

- **Streamlit-based Agentic Control Application**
- **Production-ready SQL and DLT templates**
- **Automated Databricks Jobs & DLT Pipelines**
- **Configuration-driven metadata and layer generation**
- **End-to-end implementation documentation**

**Why Choose This Agentic ELT Accelerator**

- **True End-to-End Medallion Automation**
- **AI Acceleration with Human Control**
- **Faster Time-to-Value**
- **Governed, Repeatable and Scalable**
**Built Natively for Databricks Lakehouse**

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