What leaders see
Work keeps moving, but only because people fill the gaps.
Teams rely on manual checks, side files, rekeying, status meetings, and individual knowledge to keep the process alive.
Services
Data Analytics · Data Warehouse
Data Warehouse & Integration
One warehouse for all your data, not one spreadsheet per department. Your operational data is scattered across ERP, WMS, CRM, MES, spreadsheets, and shared drives. Every report requires someone to pull data from 3-4 systems and reconcile it manually. We centralize everything into a cloud data warehouse with automated pipelines so your analytics run on a single, consistent source of truth.
01
The Problem
Data Trapped in Silos That Don't Talk to Each Other
The problem is not one broken tool. It is an operating gap between who owns the work, which record can be trusted, and how exceptions move through the business.
What is actually happening
The workflow has no clean source of truth.
Records, rules, approvals, and handoffs are split across systems, so each step introduces delay or reconciliation.
What gets worse
Automation amplifies the weak spots.
The faster the business moves, the more bad data, exception work, and decision ambiguity compound across the operation.
02
What Changes
What Data Warehouse & Integration includes.
Your operational data is scattered across ERP, WMS, CRM, MES, spreadsheets, and shared drives. Every report requires someone to pull data from 3-4 systems and reconcile it manually. We centralize everything into a cloud data warehouse with automated pipelines so your analytics run on a single, consistent source of truth.
01
Cloud Data Warehouse
Deploy Snowflake, BigQuery, or Azure Synapse as your central analytics warehouse. Schema designed for complex operating data models -- orders, inventory, production, quality, and financials.
02
ERP Data Integration
Automated extraction from current and legacy ERP systems via Python pipelines. Transaction data, master data, and configuration data pulled on schedule or in near-real-time.
03
WMS & MES Integration
Warehouse transactions, production completions, quality records, and shop-floor data integrated alongside ERP data. The warehouse sees the full operational picture.
04
Automated ETL Pipelines
Scheduled and event-driven data pipelines that extract, transform, and load data from source systems. Built-in data quality checks, deduplication, and standardization at every stage.
05
Data Quality Layer
Validation rules, anomaly detection, and data quality scoring applied during ingestion. Bad data is flagged and quarantined -- not loaded into the warehouse to corrupt downstream reports.
06
Semantic Layer & Data Models
Business-friendly data models that define "revenue," "inventory," "on-time delivery," and other metrics once. Every dashboard and report uses the same definitions -- no more conflicting numbers.
03
How It Fits Your Operations
How Data Warehouse & Integration fits your operation.
Operations layerWhich manual workflow is costing the most time, rework, or decision delay.
Governance dependencyThe workflow needs clear ownership, trusted data, and exception rules before automation is worth building.
Operating data involved
orders
approvals
documents
exceptions
reporting handoffs
What Launchpad defines before engineering begins
- What the business needs to change and why.
- Which systems, records, risks, and readiness gaps shape the work.
- What should be built, how it fits the architecture, and in what order.
Related Foundations
What to evaluate next.
Follow the dependencies behind this service instead of treating it as an isolated project.
01
Data analytics and business intelligence
Connect governed data to the operational measures and decisions leaders need to trust.
Explore next step02
API and integration engineering
Move approved records between ERP, cloud applications, portals, reporting, automation, and AI.
Explore next step03
AI automation and agents
Apply AI after the data foundation, permissions, workflow, and review boundaries are ready.
Explore next step04
Launchpad readiness and Roadmap
Assess readiness, dependencies, risk, architecture, and implementation order before engineering begins.
Explore next stepLaunchpad Before Engineering
Decide what to build and in what order.
Launchpad assesses the business and turns discovery into priorities, risks, readiness, architecture, and an implementation Roadmap. Metrotechs then engineers and supports the approved solution.
Metrotechs designs, builds, integrates, and supports the approved solution.
Launchpad keeps priorities, risks, owners, decisions, and delivery governance connected.
04
Delivery sequence
How Metrotechs delivers Data Warehouse & Integration.
Your operational data is scattered across ERP, WMS, CRM, MES, spreadsheets, and shared drives. Every report requires someone to pull data from 3-4 systems and reconcile it.
01
Source System Inventory
Catalog every data source, document data volumes, update frequencies, and access methods. Map the data flows that need to converge in the warehouse.
02
Schema & Model Design
Design the warehouse schema and semantic models based on your analytics requirements. Define dimensions, facts, and business metric calculations with stakeholder sign-off.
03
Pipeline Development
Build ETL/ELT pipelines for each source system. Implement data quality checks, transformation logic, and incremental refresh strategies.
04
Validation & Go-Live
Validate warehouse data against source systems. Reconcile counts, totals, and key metrics. Go live when data accuracy meets defined thresholds.
05
Monitoring & Maintenance
Deploy pipeline monitoring, data freshness alerts, and quality dashboards. Ongoing maintenance as source systems change or new data sources are added.
05
FAQ
Questions that usually decide the scope.
Straight answers to what operators ask before committing budget to this work.
BigQuery on GCP for most operators in our stack -- integrates cleanly with Python pipelines and ERP data exports. Snowflake is a strong alternative for teams with existing BI investments. We recommend based on your analytics tools and data volume.