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 actionable for the entire organization.

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+150 companies supported
— 5/5 on Google

Benefits / Impacts

What it Delivers

Tangible results for your organization, measurable within the first few weeks of implementation.

01

Unified Access to All Your Data

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

02

AI-Ready Infrastructure

No high-performing AI model without a solid Data platform. The infrastructure we build directly powers your analytical use cases and future artificial intelligence systems.

03

Controlled Infrastructure Costs

Right-sized architecture, cloud resource optimization, and elimination of redundancies. You pay for what you use, not for what you store redundantly.

04

Seamless Integration with Your Existing IT System

We don't replace your technical ecosystem. We connect your ERP, CRM, business tools, and external sources into a coherent architecture that respects your constraints.

05

Empowering Business Teams

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

Chapters

Our Expertise

The skills and expertise we leverage to deliver results in this area.

Data Architecture

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

Data Engineering

Industrialization of data pipelines: ingestion, transformation, and orchestration. Versioned code, automated tests, and monitoring to ensure the reliability of your flows.

IS integration

Connecting your ERP, CRM, and business tools to your Data platform via APIs, native connectors, and ELT flows. Data consistency and traceability across systems.

Cloud migration

Gradual migration of your on-premise and legacy systems to a modern cloud architecture, without a big bang approach and with measurable gains from the first few weeks.

Data Ops

CI/CD on Data pipelines, observability, incident management, and contractual SLAs to ensure reliable daily operation of your platform.

Real-time Data Processing

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

Tools / Partnerships

How we apply this expertise

We are technology agnostic and work with the three major cloud providers. Our tool selection is dictated by your existing context, data volumes, and 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

Project types

What we deliver

Operational deliverables, not just recommendations. Here are the types of engagements we offer for this expertise.

01

Design and deployment of Data Platforms

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

02

Data infrastructure modernization

Migrating your on-premise or legacy systems to a modern cloud architecture. Cost reduction, performance improvement, and technical debt elimination.

03

Building data pipelines

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

04

Integration of heterogeneous sources

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

Business Cases

They transformed their Data infrastructure with us

We don't deliver POCs. We deliver working systems with a measurable impact on our clients' business.

View 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, our way of working, 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 historical applications, reduces maintenance costs, and enables AI use cases. Diametral progressively modernizes through a cloud-native approach that coexists with existing systems, avoids a 'big bang' migration, and delivers measurable gains within the first few weeks.

How much does a cloud data platform cost?

The cost of a cloud data platform depends on the volume, number of sources, and targeted use cases. Diametral right-sizes the architecture from the Design phase, avoids typical over-provisioning, and applies a FinOps approach from conception so that every euro of compute is justified by business value.

How to integrate a data platform with my ERP/CRM?

Diametral integrates the data platform with ERPs (SAP, Oracle, Microsoft) and CRMs (Salesforce, HubSpot) via native connectors, APIs, and ELT pipelines. This integration is achieved without replacing your existing tools, by leveraging application events and master data repositories to ensure data flow consistency.

What is the difference between ETL and ELT?

ETL transforms data before loading it into the warehouse, whereas ELT loads raw data and then transforms it within the cloud. Diametral favors ELT for modern architectures because it leverages the native computing power of cloud data warehouses (Snowflake, BigQuery, Databricks) and significantly 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 robust infrastructure.

Describe your technical environment. A Diametral Data architect will evaluate with you the initial projects to launch.

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