Enterprise Data Platform with Medallion Architecture

U.S. Manufacturer Unifies Enterprise Data, Cuts Manual Work 80–90%

A U.S.-based industrial solutions provider growing through acquisitions needed to unify fragmented ERP data into a scalable reporting foundation. We designed and implemented a Medallion Architecture in GCP with an automated Python-based ingestion framework, standardizing sales, customer, and location data across all entities—eliminating manual Excel consolidation and enabling real-time, acquisition-ready executive reporting in Power BI.

Chemical Manufacturing

Data Engineering

Modern data architecture

Faster Acquisition Data Injection

80–90

reduction in manual Excel-based processing

U.S.-based industrial solutions provider founded in 1888, serving North America and global markets. Delivers industrial cleaners, food safety chemicals, water treatment, and restroom care programs. Manufactures in NY, IL, and Wales, with distributors in 50+ countries. Recognized leader in certified green housekeeping and sustainable operations.

Google Cloud Platform (BigQuery)

Python

Power BI

Google Validation API

Google Geocoding API

THE CHALLENGE

The Client Needed a Unified Data Architecture to Achieve Scalable M&A Growth

The company grows through acquisitions, but each acquired business brings its own ERP, data formats, and naming conventions. Data is manually consolidated via CSV/Excel, creating delays, errors, and no reliable way to compare historical sales or track customer churn across entities.

There was no unified data model, no standardized ingestion process, and no governance layer to ensure consistency.

Growth was increasing reporting complexity instead of accelerating decision-making.

Fragmented Multi-Source Data is hard to consolidate for reporting

Different naming conventions for customers, products, locations, and contracts across systems, preventing clean alignment and made unified reporting impossible.

Manual, Error-Prone Data Onboarding

Data from acquisitions was uploaded and validated manually, creating delays and errors. Data ingestion relied on CSV/Excel workflows, increasing processing time, operational risk, and reconciliation errors.

Disjointed ERP Systems

Acquired companies used different systems (e.g., QuickBooks, others) that could not integrate directly with Great Plains.

No Historical Visibility

Missing prior-year alignment prevented tracking retention, YoY growth, and revenue impact post-acquisition.

Unify your fragmented enterprise data in 30 days

Gain complete executive visibility across all entities with a board-ready reporting platform.

THE SOLUTION

Scalable Medallion-Based Data Platform on GCP for Acquisition-Driven Reporting

We designed and implemented a scalable Medallion Data Architecture (Bronze → Silver → Gold) in GCP, powered by a reusable Python processing framework that automatically ingests, validates, normalizes, and harmonizes acquisition data.
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The solution established a unified data platform with standardized sales, customer, and location master tables, fully integrated with Power BI. It delivers a true single source of truth and enables seamless acquisition onboarding — allowing newly acquired companies’ data to be integrated quickly and automatically without rebuilding pipelines or relying on manual Excel consolidation.
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The system automatically generates sales, customer, and location reports integrated with Power BI — creating a scalable, automated single source of truth for current and future acquisitions.

Data Standardization Through Medallion Architecture

We implemented a Medallion Architecture (Bronze → Silver → Gold) in GCP to standardize acquisition data. Raw data is ingested in Bronze, cleansed and normalized in Silver, and transformed into business-ready sales, customer, and location models in Gold. This created a consistent, governed data structure across all acquired entities.
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Phase 0

Discovery, Assessment & Technical Decision

Fully understand the current state of the GCP environment, data sources, and M&A requirements, and define the optimal medallion architecture implementation approach.

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Phase 1

Bronze Layer: Land raw data from acquired companies in Cloud Storage and register external tables in BigQuery.

Silver Layer: Transform raw multi-source data into a cleaned, validated, and standardized format aligned with the canonical data model.

Gold Layer: Transform harmonized Silver layer data into Power BI-optimized dimensional models, enabling fast, intuitive business analysis of M&A financial and operational performance.

ERP-Agnostic Integration Layer

We implemented a standardized ingestion and transformation layer within GCP that decouples ERP differences from reporting.

Instead of building custom integrations per acquisition, the new framework processes raw exports and transforms them into harmonized datasets ready for reporting.

This significantly reduced engineering effort for future acquisitions.

Automated Ingestion & Validation Framework

We built a Python-based general processing framework that includes:

Automated table mapping engine
Address & customer matching logic
Automated validation rules
Reusable onboarding templates

The framework cleanses, processes, and transforms new company data automatically, reducing manual effort and eliminating recurring mapping errors.

Unified Reporting & Executive Visibility

We built a Unified Data Platform with:

Standardized sales, customer, and location master tables
A consistent reporting layer
Power BI-ready datasets connected directly to the Gold layer

Executives now have immediate visibility into YoY performance, customer retention, and acquisition impact through ready-to-use Power BI dashboards.

THE RESULT

From M&A Data Fragmentation to a Unified, Automated Reporting Platform

The client implemented a scalable Medallion Architecture (Bronze → Silver → Gold) in GCP supported by a reusable Python-based processing framework, creating a unified data platform with standardized sales, customer, and location master tables fully integrated with Power BI.
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This enabled automated acquisition data onboarding, eliminated manual Excel consolidation, improved data accuracy and governance, unified ERP outputs into a single reporting model, and provided immediate year-over-year sales and retention visibility across all entities.
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As a result, leadership gained faster decision-making capabilities, reduced operational risk, and a scalable foundation for future M&A growth.
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Unified YoY Sales & Retention Reporting Across 100% of Entities

70%

Faster Acquisition Data Injection

80–90%

Reduction in Manual Processing

Enterprise Data Platform with Medallion Architecture

How we provide data integration solutions

Step 1 of 5

Free consultation

It's a good time to get info about each other, share values and discuss your project in detail. We will advise you on a solution and try to help to understand if we are a perfect match for you.

Step 2 of 5

Discovering and feasibility analysis

One of our core values is flexibility, hence we work with either one page high level requirements or with a full pack of tech docs.

At this stage, we need to ensure that we understand the full scope of the project. We receive from you or perform a set of interviews and prepare the following documents: integration pipeline (which data we should get and where to upload), process logic (how system should work); use cases and acceptance criteria; solution architecture. Ultimately we make a project plan which we strictly follow.

Step 3 of 5

Solution development

At this stage, we build ETL pipelines and necessary APIs to automate the process. We attract our DevOps team to build the most efficient and scalable solution. Ending up with unit tests and quality assurance tests to ensure that the solution is working properly. Focus on Results is one of our core values as well.

Step 4 of 5

Solution delivery

After quality assurance tests are completed, we deliver solutions to the client. Though we have over 15 years of expertise in data engineering, we are expecting client’s participation in the project. While developing the integration system, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high-level of communication is also our core value.

Step 5 of 5

Support and continuous improvement

We understand how crucial the solutions that we code for our clients are! Our goal is to build long-term relations, so we provide guarantees and support agreements. What is more, we are always happy to assist with further developments and statistics show that for us, 97% of our clients return to us with new projects.

Success stories

All Success Stories

80%+ Reduction in Manual Job Data Handling Using an AI Platform

The client productized its healthcare recruitment services by replacing manual job data processing with an AI-powered platform. We built an LLM-driven microservice architecture that automates the ingestion, extraction, validation, and deduplication of thousands of unstructured job postings every day. The solution powers both web and mobile applications, significantly improving processing speed and data accuracy. As a result, the platform reduced operational costs by 20–40% while enabling scalable growth.

0.9s

job posting processing time

80–95%

reduction in manual job data handling

View case study

Cut Manual Data processing with an AI Platform

How a 34-State U.S. Dessert Franchise Gained Full Performance Visibility

Tifa Chocolate & Gelato is a U.S.-based dessert franchise operating across 34 states. As the business scaled, the company implemented a centralized, data-driven platform to gain clear visibility into franchise performance. We unified fragmented data, strengthened the reporting foundation, and delivered executive dashboards. Today, leadership operates from a trusted single source of truth, accesses insights faster, and scales the brand nationwide with confidence.

< 5-minute

data-to-dashboard latency

11 executive dashboards

executive dashboards

View case study

How a 34-State U.S. Dessert Franchise Gained Full Performance Visibility

Tifa Chocolate & Gelato is a U.S.-based dessert franchise operating across 34 states.

Unified Data Platform for AI Capacity Planning Platform

A UK-based AI cloud provider partnered with Dataforest to transform fragmented operational data into a unified, Medallion-based analytics platform on Databricks. By integrating billing, infrastructure, CRM, and contract systems, the company gained trusted visibility into GPU/ASIC utilization and revenue performance. The new foundation enables accurate demand forecasting, confident capacity planning, and scalable growth for enterprise-grade AI infrastructure.

System Integrations Completed

100%

Efficiency Improvement Achieved

View case study