Case Study

Cloud Migration Metadata Workflow

Metadata-driven pipeline for cloud migration planning, combining schema design, transformation logic, curated datasets, and repeatable Excel deliverables.

Power Query SQL Java dbt Data Agents Excel Metadata Modeling Data Standardization

Overview

This case study focuses on a metadata-driven pipeline that turned infrastructure and deployment data into standardized, migration-ready Excel outputs.

Excel was the delivery layer, but the main engineering work happened upstream: schema definition, transformation logic, curated datasets, reusable mappings, and report-generation metadata.

Business Problem

Cloud migration planning depends on reliable metadata. In practice, infrastructure data is fragmented, inconsistently named, and hard to reuse across releases.

The team needed a way to:

• standardize metadata coming from deployment and infrastructure sources
• reduce manual workbook preparation and repeated spreadsheet manipulation
• create reusable migration-oriented datasets for servers, storage, topology, and environment design
• generate structured Excel reports that could be reproduced across releases without rebuilding logic each time

The real need was a repeatable pipeline that turned raw metadata into usable migration artifacts.

Solution Approach

I designed the solution as a metadata-driven pipeline rather than a manual workbook process. It combined versioned bundles, schema definitions, transformation rules, merge logic, curated datasets, and report instructions.

At a high level, the workflow looked like this:

1. infrastructure and deployment metadata was organized into versioned bundles
2. schema definitions described entities, attributes, keys, and expected structures
3. SQL and Power Query-oriented designs defined how source data should be transformed into derived tables
4. merge and table-operation definitions assembled report-ready datasets
5. report metadata and Excel templates controlled how final outputs were rendered
6. Java consumed the prepared datasets and definitions to generate repeatable Excel deliverables

This made the workflow easier to govern, extend, and repeat.

Pipeline Architecture

1. Metadata Packaging And Governance

I built the repository as a bundle-based system with versioning, composition, indexing, and integrity tracking. That gave the workflow a governed structure instead of a loose collection of files.

2. Schema-Driven Standardization

I defined schema layers for Excel-facing and report-facing objects, including entities, attributes, data types, and custom metadata rules. That created a predictable contract for the rest of the pipeline.

3. Transformation Layer For Derived Tables

I designed the transformation layer so source metadata could be converted into workbook-ready structures. This included SQL design artifacts and Power Query-oriented preparation patterns used to derive reusable tables for infrastructure analysis.

Examples of the kinds of outputs this layer supported included:

• server sizing and environment-specific infrastructure selections
• storage and volume design datasets
• topology-to-repository mappings
• component, operating system, and deployment design relationships

The key point is that this was not just column cleanup. It was shaping metadata into reusable analytical building blocks.

4. Merge And Processing Pipeline

On top of the derived tables, I designed a merge and table-operation layer that combined datasets into report-ready outputs.

This layer handled operations such as:

• joins across server, storage, network, and deployment metadata
• filtering and attribute projection
• sorting and aggregation
• pipeline ordering for repeatable processing

That enabled richer migration datasets such as server summaries, Linux user and group views, storage information tables, and subnet-related outputs.

5. Excel Report Definition Layer

I also designed the report-definition side of the system. Worksheet-building instructions, formatting, images, table placement, and workbook patterns were defined as metadata instead of rebuilt manually each time.

My Role

I was responsible for the end-to-end design and implementation of the pipeline definition layer. That included:

• designing the bundle-based repository structure
• creating schema and metadata definitions
• building the transformation and derived-table design layer
• defining merge logic and report-ready dataset processing
• modeling Excel report-generation instructions and workbook patterns
• maintaining versioning, composition, and integrity metadata across releases

In practice, I owned the system that translated raw infrastructure metadata into standardized analytical outputs and Excel deliverables.

Outcome

The result was a more reliable workflow for cloud migration support and infrastructure reporting. Instead of manually assembled spreadsheets, the team had a structured pipeline that standardized metadata, built reusable intermediate datasets, and generated repeatable deliverables.

The value was not only the final workbook. It was making migration-related reporting more consistent, easier to maintain, and easier to scale.

Confidentiality Note

Descriptions are generalized to protect confidential project details while preserving the technical design, architecture, and delivery approach.