Project Type: B2B Resource
Project Timeline: Feb 2025 – April 2025
Client Region: APAC
Industry: General Product

Context & Objectives

The client had a large-scale Databricks environment with Unity Catalog managing thousands of catalogs, schemas, tables, and columns. Governance and metadata management were primarily Excel-driven, which made it cumbersome for analysts and data owners to maintain consistency, apply policy tags, and ensure proper access control. Manual IAM (Identity and Access Management) operations were time-consuming and error-prone, often leading to inconsistent policies, access errors, and long approval cycles.

Our objective was to modernize metadata management and governance by building a Metadata Accelerator, a framework that allowed analysts and business users to define metadata in familiar spreadsheets, automatically generate the necessary tables, tags, and policies in Unity Catalog, and enforce governance rules consistently. The solution aimed to reduce manual work, accelerate onboarding, and provide full auditability without requiring deep technical expertise.

Project Goals

Enable analysts and data owners to define metadata in familiar tools
We wanted business teams and analysts to work in a familiar environment like Excel or Google Sheets to define catalogs, schemas, tables, columns, and associated policy tags.

Auto-generate Unity Catalog artifacts with validation and rollback
Once metadata was defined, the system needed to automatically create or update Unity Catalog objects, including tables, columns, grants, and policy tags.

Standardize governance and reduce manual IAM work
A major goal was to enforce consistent governance practices across all Databricks workspaces. This included applying policy tags, grants, and access controls uniformly, minimizing the need for repeated IAM tickets and interventions.
This approach ensured compliance, reduced operational overhead, and made metadata management traceable and audit-ready.

Challenges

Modernizing metadata governance at scale required addressing both technical and operational hurdles. We faced a slow, error-prone Excel-driven framework, while the environment’s size and sensitivity added complexity. We needed to ensure that metadata updates could be performed quickly, safely, and consistently, without disrupting ongoing analytics workflows.

1. Manual & Error-Prone Processes
   We had to manage tables, columns, tags, and permissions manually in spreadsheets, which was slow and inconsistent. Each update carried a risk of errors, making governance difficult and increasing our workload.

2. Scale & Reliability
   With thousands of objects to manage, we had to design batch updates, validations, and rollback procedures to ensure changes could be applied safely at scale, without causing downtime or breaking workflows.

3. Governance & Security
   We needed to protect sensitive data with strict policy tags (PII/PCI), role-based access, and environment separation. Manual IAM updates increased the risk of misconfiguration and compliance issues, so we had to mitigate these risks.

4. Operational Efficiency & Auditability
   We wanted to safely delegate metadata management to business users while maintaining full traceability. Every change needed proper logging for audits, owner attestations, and to reduce repeated support tickets or manual approvals.

Solution Overview

To overcome the challenges of managing metadata at scale, we designed and implemented the Metadata Accelerator framework. Our solution automated the creation, updating, and governance of metadata in Databricks Unity Catalog while keeping the process intuitive for analysts and business users.

• Spec-Driven Metadata Definition
  We enabled analysts and data owners to define metadata in Excel or Google Sheets using structured templates. These templates captured catalogs, schemas, tables, columns, tags, owners, sensitivity levels, and retention rules. By using templates, we reduced errors, ensured consistency, and allowed business users to safely contribute metadata.

• Validation & Diff Checks
  Before applying any changes, we ran automated validations to ensure naming conventions, schema compatibility, and policy compliance. Diff reports highlighted differences compared to the existing catalog, helping our team catch issues before they could impact production.

• Automated Generation Engine
  We built a generation engine that read validated specs and applied them in Unity Catalog using the Databricks SDK and Databricks Asset Bundles (DAB). It automatically created or updated tables, columns, policy tags, grants, and RLS patterns. Batch processing enabled updates across thousands of objects efficiently.

• Wave-Based Execution & Rollbacks
  To minimize operational risk, we applied changes in controlled waves. Each wave included rollback bundles to safely revert changes if errors occurred. CI/CD checks ensured only approved specifications were applied, providing reliability and traceability for all updates.

• Governance, Security & Observability
  We enforced least-privilege access using Unity Catalog grants, policy tags, and secret scopes. Environment isolation kept development, staging, and production separate. Monitoring dashboards tracked drift, schema deviations, and compliance, while monthly governance reports provided transparency and audit readiness.

• Operational Efficiency & Cost Management
  By batching updates, executing workflows in parallel, and automating repetitive tasks, we minimized compute usage and operational effort. This allowed business users to contribute safely without waiting for engineering support, reducing manual intervention and operational overhead.

Through the Metadata Accelerator framework, we transformed metadata management from a slow, manual process into a fast, automated, and governed workflow. Teams could now safely onboard, update, and maintain metadata at scale while ensuring consistent governance, high reliability, and full auditability. The framework also empowered business users to contribute directly, reducing engineering bottlenecks and improving overall operational efficiency.

Constraints & Non-Functional Requirements

In designing the Metadata Accelerator framework, we carefully addressed several key non-functional requirements to ensure reliability, security, and efficiency.
Security and governance were a top priority: we enforced Unity Catalog policies and tags, applied least-privilege grants, used secret scopes, and maintained strict separation between development, staging, and production environments.
Reliability was built in through dry-run validations, automated diff checks, rollback bundles, and CI/CD checks, preventing errors from reaching production. Scale and performance were critical, as the system needed to handle thousands of tables and batch updates safely, with parallel execution to complete changes efficiently without downtime.
Finally, cost management was considered by minimizing compute usage, optimizing update batches, and avoiding long-running jobs, ensuring the framework remained operationally and financially efficient.

Data Model & Semantics

We designed the Metadata Accelerator to formalize and standardize metadata across Databricks Unity Catalog.

• At its core, the spec model captured all critical information for each object, including catalogs, schemas, tables, columns, associated tags, data owners, sensitivity levels, and retention rules. This provided a structured blueprint that analysts and business owners could follow consistently.

• To enforce governance semantics, policy tags for sensitive data such as PII and PCI were applied uniformly, RLS rules ensured proper access controls, and lineage visibility allowed teams to trace data flow for compliance and audit purposes.

• Additionally, operational semantics standardized naming conventions, validated schema compatibility, and tracked metadata drift over time.

  
  

Together, these practices ensured that the metadata was not only consistent and accurate but also secure, traceable, and easy to manage at scale.

Ops, Security, Quality & Performance

We ensured that the Metadata Accelerator framework operated efficiently, securely, and reliably while supporting large-scale metadata management.

• On the operations side, we executed changes in controlled waves, applied CI/CD approval checks before any modifications, and used rollback kits to revert changes if necessary. Batch updates allowed thousands of tables and columns to be processed efficiently without downtime.

• From a security perspective, we enforced least-privilege default grants for all users, applied policy tags consistently for sensitive data, isolated environments to separate development, staging, and production, and used secret scopes to protect sensitive configuration and credentials.

• To maintain quality, we implemented strict schema compatibility rules, standardized naming conventions, generated diff reports to highlight changes, and set up alerts for metadata drift to detect any deviations from defined standards.

Finally, for performance and cost management, we optimized the system with batch and parallelized execution, minimized recompute, and ensured that applying metadata changes at scale remained fast, reliable, and cost-effective.

Tech Stack

Data Sources:

• Excel and Google Sheets specifications capturing catalogs, schemas, tables, columns, tags, owners, and sensitivity levels.

Ingestion:

• Spec loader with built-in validators for naming conventions, schema compatibility, and diff detection.

Storage / Lakehouse:

• Unity Catalog tables and metadata artifacts generated automatically by the engine.

Orchestration:

• Batch apply engine with wave-based execution, dry-run validations, and rollback bundles to ensure safe deployment.

Transformation / Modeling:

• Automated creation and alteration of tables, columns, grants, and policy tags using the Databricks SDK and Databricks Asset Bundles (DAB).

Serving / Consumption:

• Metadata exposed in BI tools and Databricks notebooks for analysts and business users.

Governance / Security:

• Unity Catalog grants, policy tags, secret scopes, and environment separation to enforce least-privilege access and data protection.

Observability / Quality:

• Drift alerts, monthly governance reports, and lineage tracking to ensure metadata consistency and auditability.

DevOps / CI/CD:

• Versioned change sets, approval workflows, and CI checks on specs for reliable and controlled deployments.

FinOps / Cost Management:

• Batched updates, parallel execution, minimal compute usage, and audit at scale to optimize operational cost and efficiency.

Outcomes & Business Impact

The Metadata Accelerator framework significantly improved the way metadata was managed in Unity Catalog.
Metadata onboarding, which previously took weeks, could now be completed in just a few days, allowing analysts and data owners to define tables, columns, and policy tags quickly using familiar Excel or Google Sheets templates.
By automating the application of these specifications, we ensured consistency across the environment, with RLS rules, policy tags, and access grants applied uniformly, reducing errors and strengthening governance. Every change was fully auditable through detailed logs, owner attestations, diff reports, and drift alerts, making compliance verification straightforward and reliable.
At the same time, business users gained more ownership of their datasets, safely contributing metadata without heavy reliance on engineering teams, which improved efficiency while maintaining control and traceability.

Deliverables

Spec Templates: Excel/Google Sheets templates with validation rules for accurate metadata input.

Generation Engine: Configured engine for batch execution, dry-run validations, and rollback capabilities.

UC Policy/Tag Catalog: Predefined RLS patterns and policy tags applied consistently for governance.

Monitoring Dashboards: Alerts, drift detection, and monthly governance reports for transparency and audit readiness.

Conclusion: Enabling Scalable, Governed Metadata Management

The Metadata Accelerator transformed manual, error-prone Excel-based metadata governance into an automated, scalable, and reliable framework in Databricks Unity Catalog. By leveraging spec-driven templates, validation, batch execution, and CI/CD checks, we accelerated onboarding, reduced operational overhead, and enforced consistent governance across thousands of tables and columns.

Analysts and data owners could now safely define metadata, enforce policy tags, and track lineage without deep technical knowledge, while engineering teams focused on higher-value tasks. The solution not only solved governance and compliance challenges but also laid a foundation for repeatable, efficient, and audit-ready metadata management at scale.