ELT Automation: Medallion Architecture with Agentic AI & Automatic Data Governance

In today's fast-paced, data-driven world, getting clean, reliable data into the hands of business users is a critical challenge. The traditional Extract, Load, Transform (ELT) process, while effective, can be slow and resource-intensive, often becoming a bottleneck that prevents organisations from moving at the speed of business. But what if this process could be accelerated with modern ELT automation to be fast, intelligent, and reliable, while ensuring data quality and compliance?

This isn't a futuristic fantasy. By combining the Medallion Architecture with a powerful Agentic AI approach, we're building a new paradigm for ELT automation that streamlines the entire ELT workflow, ensuring data quality and accelerating time-to-insight.

The Challenge: Manual ELT and Data Governance Bottlenecks

Traditional ELT pipelines, even when automated, often require significant manual intervention. Data engineers must not only write and maintain complex SQL scripts but also manually define and enforce automated data governance policies. This includes everything from data lineage tracking to data profiling and enforcing complex data quality rules. This creates a reliance on skilled personnel, slows down development cycles, and introduces potential for human error and data trust issues.
The goal is to move from a manual process to a more autonomous system, an "agentic" approach with humans in the loop, where AI agents can take the lead in creating, validating, orchestrating, and executing data pipeline automation tasks and, crucially, ensuring data validation is built-in by design.

The Solution: Medallion Architecture Meets Agentic AI

The Medallion Architecture, with its three distinct layers (Bronze, Silver, and Gold), provides the perfect framework for this intelligent AI data pipeline and ELT automation.

Bronze Layer (The Raw Ingestion Layer):

Data is ingested "as is" directly from the source into your data lakehouse, such as Google Cloud Platform (GCP) BigQuery (BQ). This layer serves as a raw, immutable copy of your data, preserving lineage and providing a single source of truth for reprocessing. Once the initial data sync and append-only logic is completed, our agentic approach starts here, which enriches the metadata for bronze layer tables by adding object and field-level descriptions with a human in the loop.

Silver Layer (The Refined and Normalised Layer):

The real magic begins here. Instead of manually coding transformations, our LLM Agentic AI approach leverages a Vertex AI data pipeline. It automatically generates and executes Dataform jobs to create views (Silver - Layer 1) and tables (Silver Layer 2). This layer carries forward the enriched metadata from the bronze layer, cleans the data, enforces naming standards, and ensures unique records. Key logic, such as Change Data Capture (CDC) and incremental updates, is automatically managed by the AI agents, ensuring data freshness and consistency with minimal human oversight.
A critical aspect of this layer is automated data quality enforcement using Dataform assertions. Metadata is automatically derived from the bronze layer and applied to key data checks such as uniqueness, not-null constraints, and data types while continuously monitoring these rules to proactively detect and resolve data quality issues. This layer exemplifies advanced ELT automation in action.

Gold Layer (The Business-Ready Layer):

This is where data becomes actionable. We have developed agents using Google’s ADK (Agent Development Kit) to create the final Dataform jobs. These agents provide intelligent SQL guidance, enrich the data with metadata, and apply business-specific data quality checks, with human-in-the-loop oversight to ensure accuracy and trust. The outcome is a clean, aggregated dataset ready for business intelligence (BI), analytics, and machine learning models. All interactions happen through a chatbot that enables business users to interact directly with the Gold layer, guiding the process and receiving real-time answers. Domain Knowledge Base: We have also developed domain-specific knowledge bases (e.g., for Finance or Marketing) that capture unique business rules and metrics, enabling agents to generate more accurate and insightful Gold layer tables, further supporting ELT automation across the organisation.

Agent Flow Architecture

The Agentic AI Difference: Human-in-the-Loop Control & Secure Governance

Our approach goes beyond simple automation. It integrates a comprehensive automated data governance framework directly into the ELT automation pipeline by leveraging Dataform's capabilities, orchestrated by our AI agents. It’s designed to be a collaborative tool, not a black box.

Human-in-the-Loop Design: The AI agents are built to be your partners, not replacements. They automate repetitive tasks, generate code, and suggest optimisations, but every critical decision, from approving a complex transformation to validating a new data assertion, is a human choice. The AI provides the blueprint, and you provide the final approval, ensuring full control and accountability. This approach reinforces ELT automation while keeping humans in control.

No Data Sharing with LLMs: We understand that data privacy is non-negotiable. Our architecture is engineered to ensure your sensitive data never leaves your environment or is shared with an external LLM. The AI operates on metadata and schemas, generating code that is executed securely within your own GCP project. This approach provides all the benefits of ELT automation without any of the data exposure risks.

Automated Data Lineage: The AI generates Dataform code with explicit dependencies, allowing for data pipeline automation and transparent data lineage tracking. This provides a complete, end-to-end view of your data's journey, which is crucial for root-cause analysis, impact assessments, and regulatory audits.

Proactive Data Profiling: While our AI orchestrates the process, Dataform’s assertions provide a form of continuous data profiling by validating data against pre-defined rules, ensuring data quality is proactively managed throughout the ai data pipeline.

Continuous Data Quality Checks: Beyond simple metadata, our AI ensures Dataform assertions enforce a wide range of data quality checks, from basic constraints like uniqueness to more complex business rules. They constantly monitor for data drift, ensuring that the data remains consistent and reliable over time, strengthening overall ELT automation.

Blend of Deterministic and Probabilistic ELT Flows: Our approach combines the reliability of deterministic transformations, such as SQL definitions for some tables, strict governance checks, and lineage tracking, with the adaptability of probabilistic LLM-powered flows, which assist in generating SQL for more complex or evolving transformations. This combination ensures high trust in predictable tables while leveraging AI intelligence to improve accuracy and efficiency in areas where human intuition or evolving logic is needed, making ELT automation seamless across the pipeline.

Key Advantages of This Approach

This unique combination of Medallion Architecture and Agentic AI delivers significant benefits:

Accelerated Time-to-Value: By leveraging ELT automation and data pipeline automation, we drastically reduce the development time for ELT pipelines. What used to take weeks of manual coding can now be done in a fraction of the time.

Unbreakable Data Quality & Trust: The self-correcting nature of the AI agents, combined with automated data governance and metadata-driven assertions, ensures that data quality rules are consistently applied across the pipeline, leading to more reliable and trustworthy insights.

Increased Agility: The system can adapt quickly to changes in source schemas or business requirements, automatically adjusting the AI data pipeline and data governance protocols without the need for manual refactoring.

Cost Optimisation: Automated, self-healing workflows reduce the need for extensive data engineering teams and minimise manual effort, leading to a more efficient and cost-effective data operation.

Democratized Data Access: By creating a user-friendly chatbot for the Gold layer, we empower business users to directly interact with the data and guide the transformation process, breaking down the barriers between technical and business teams and enhancing ELT automation adoption.

The Future of Data Automation is Here

The ELT automation Medallion Architecture with Agentic AI isn't just an improvement; it's a fundamental shift in how we approach data. It transforms data engineering from a manual, code-heavy discipline into a strategic, high-leverage function where AI handles the heavy lifting, and human expertise is focused on high-level strategy and innovation.

Are you ready to unlock the full potential of your data and drive your business forward? Contact us today to schedule a demo and see how our Agentic AI solution can revolutionise your ELT automation.