Architecture & Engineering

Connect your existing systems to unlock the value of your data.

Your data is there, but scattered across dozens of systems that don't communicate. We design and deploy Data platforms that centralize, organize and make your data usable by the entire organization.

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Benefits/Impacts

What it brings

Concrete results for your organization, measurable from the first weeks of intervention.

01

Unified access to all your data

No more manual extractions and Excel files changed by email. Your teams have access to consolidated, up-to-date, and reliable data from a single point of entry.

02

An AI-ready infrastructure

No powerful AI model without a solid Data platform. The infrastructure we build directly feeds your analytical use cases and future artificial intelligence systems.

03

Controlled infrastructure costs

Architecture sized to the right needs, optimization of cloud resources and elimination of redundancies. You pay for what you use, not for what you store in duplicate.

04

Seamless integration with your existing IS

We are not replacing your technical ecosystem. We connect your ERP, CRM, business tools and external sources in a coherent architecture that respects your constraints.

05

Autonomy of business teams

Data Analysts and Data Scientists access data without relying on IT for each request. The platform is designed to be used, not managed.

Chapters

Our expertise

The skills and know-how that we mobilize to deliver results on this expertise.

Architecture Data

Design of Data Lake, Data Lakehouse and Data Mesh architectures adapted to your data volume, your use cases and your technical constraints.

Data Engineering

Implementation of frameworks, roles (Data Owners, Data Stewards) and processes that guarantee the reliability and traceability of your data.

SI integration

Design of systems whose decisions are documented, repeatable and auditable. Indispensable for high-risk uses.

Cloud migration

Register of treatments, impact analyses and management of rights applied to your Data and AI treatments.

Data Ops

Framing the principles applicable to your AI uses: detection of biases, fairness, transparency and human supervision.

Real-time Data Processing

Drafting and deploying Data and AI charters, circuits for validating models and setting up governance committees.

Tools/Partnership

How do we implement this expertise

We are technology agnostic and work with the three main clouds on the market. Our choice of tools is dictated by your existing context, your data volumes and your security constraints.

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Amazon Web Services

Google Cloud

Azure

Apache Airflow

Power BI

Amazon Web Services

Google Cloud

Azure

Apache Airflow

Power BI

Amazon Web Services

Google Cloud

Azure

Apache Airflow

Type of projects

What we deliver

Operational deliverables, not recommendations. Here are the mission formats that we deploy on this expertise.

01

Data Platform Design and Deployment

Complete cloud architecture: ingestion, storage, storage, transformation, and distribution of data. From specification to production, with documentation and skills transfer.

02

Data infrastructure modernization

Migration of your on-premise or legacy systems to a modern cloud architecture. Reduced costs, improved performance, and eliminated technical debt.

03

Building data pipelines

Development and industrialization of ETL/ELT flows that feed your analytical and AI use cases. Automated, tested and monitored pipelines.

04

Integration of heterogeneous sources

Connection of all your internal and external systems, in a unified Data ecosystem. APIs, connectors, real-time or batch synchronization as needed.

Business Cases

They transformed their data infrastructure with us

We do not deliver POCs. We deliver systems that work, with a measurable impact on the business of our customers.

See all our business cases

Supply Chain Optimization Application

A pharmaceutical distribution player had to rethink its entire supply chain: pharmacy assortment, inventory management and delivery channels. Operational research alone was no longer enough. We built the application that transformed its operations.

Social media trend detection

In a market where consumer behaviors change faster than decision cycles, a customer needed to anticipate trends instead of experiencing them. We built the platform that turns social media noise into actionable signals.

Yield of agricultural fields

The climate subsidiary of a French insurance leader needed to predict field yields across Germany to price a new drought insurance offer. Internal data was not enough. We built the predictive models that made the product marketable.

Customer needs analysis platform

A large French group needed to understand the current and future needs of its customers by aggregating a massive volume of consumer data. After a POC of 8 weeks, we industrialized a complete platform deployed on all French entities and is now being extended at the group level.

Skin scoring and analysis

A major player in luxury cosmetics had developed a skin scoring algorithm. Problem: no one could verify what the AI was based on to make its diagnosis. We built the visualization system that makes predictions transparent and deployable in stores and on mobile.

Beaconing detection

A large group's security team needed to identify beaconing signals, regular network traffic sent by potentially compromised machines to servers controlled by attackers. The volume of logs made manual detection impossible. We built the anomaly detection system capable of processing this data on a large scale.

FAQS

Your questions, our answers

All the answers to understand our approach, how we work and what you can expect from our collaboration.

What is a data platform?

A data platform is a centralized infrastructure that collects, stores, and makes available data from all of your business systems in a usable format. Diamétral designs and deploys cloud data platforms that eliminate manual extractions, eliminate Excel file sharing and offer a single point of access to your teams.

Why modernize your legacy systems?

Modernizing legacy systems unlocks the value of data stored in legacy applications, reduces maintenance costs, and unlocks AI use cases. Diametral is progressively modernizing via a cloud-native approach that coexists with the existing one, avoids the big bang and allows measurable gains from the first weeks.

How much does a cloud data platform cost?

The cost of a cloud data platform depends on the volume, the number of sources and the targeted use cases. Diametral sizes the architecture as accurately as possible during the Design phase, avoids typical overprovisioning and applies FinOps logic from the design stage so that each euro of compute is justified by a business value.

How do I integrate a data platform with my ERP/CRM?

Diametral integrates the data platform with ERP (SAP, Oracle, Microsoft) and CRM (Salesforce, HubSpot) with native connectors, APIs and ELT pipelines. This integration is done without replacing your existing tools, relying on application events and master repositories to ensure consistent flows.

What is the difference between ETL and ELT?

ETL transforms data before loading it into the warehouse, while ELT loads it raw and then transforms it inside the cloud. Diametral favors ELT over modern architectures because it exploits the native computing power of cloud warehouses (Snowflake, BigQuery, Databricks) and greatly accelerates analytical iterations.

Vue aérienne d'un marais avec de petits cours d'eau sinueux traversant des zones de végétation brune et des berges sableuses.

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Your data deserves a good infrastructure.

Describe your technical background. A Data Diametral architect assesses with you the first projects to be launched.

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