Collection & ingestion
Connect all sources—APIs, IoT, files, databases, real-time streams—without imposing a single format.
In short: Your data enters the chain, whatever its original format.
Discover →Our approach
Reduce the lag between available information and useful decisions.
We cover the full data lifecycle. Each building block is designed to work alone or integrate into a global, industrialized, monitored platform.
The goal is not only to store more data, but to produce a unified operational repository, accessible to teams, APIs and automations — with measurable quality and full traceability.
Our building blocks
Capabilities organized around your usages.
Collection, quality, processing, fusion, orchestration, and automation: end-to-end continuity from source to service.
Quality & governance
Validation, deduplication, lineage, metadata and controlled recovery.
Discover →Processing & enrichment
ETL/ELT, streaming, business rules, geospatial and AI preparation.
Discover →Data fusion
Multi-source correlation, entity resolution, and a unified operational picture.
Discover →Orchestration
Workflows, DAGs, dependencies, priorities and pipeline monitoring.
Discover →Automation & action
Alerts, tickets, business processes and controlled, supervised, reversible actions.
Discover →Value chain
End-to-end continuity, from source to service.
Six steps to turn raw streams into decisions and actions.
01
Collect
Connection to internal, external, physical, and software sources.
02
Make reliable
Validation, deduplication, quality control and traceability.
03
Transform
Normalization, enrichment, computation, and analytics preparation.
04
Fuse
Signal correlation, entity resolution and contextualization.
05
Orchestrate
Coordination of pipelines, dependencies, rules, and services.
06
Automate
Triggering of actions, alerts and business processes.
Why RDMC
What an industrialized data pipeline changes.
Fragmented data
With RDMC Data
Sources
Ad hoc connectors, silos
Industrialized multi-source ingestion
Quality
Late manual checks
Measurable rules from intake
Vision
Excel sheets / extracts
Unified operational repository
Decision
Long lead times, little automation
Adapted latency + controlled actions
Evolution
Rebuild for every new need
Progressive addition of sources and services
In figures
Shorten the distance between data and decision.
MultiSources & formats
RT / BatchProcessing modes
Edge→CloudAdapted deployments
MCOSupervised chain
Frequently asked questions
Data Engineering & Automation — details.
Do you work in real time and batch?
Yes. Depending on the business need, we set up continuous (streaming) or scheduled (batch) processing—sometimes combined on the same platform.
Can you integrate IoT and application sources together?
Yes. That is precisely what fusion is for: correlating sensors, APIs, files, databases, and external streams to rebuild a coherent operational context.
Where do you deploy these platforms?
Public cloud, private cloud, on-premise, or edge—based on latency, sovereignty, and operational constraints.
How do you approach data quality?
Through measurable rules from ingestion: schemas, anomalies, duplicates, lineage, metadata, and controlled recovery mechanisms.
Does automation replace operators?
No. We design controlled, explainable, and often human-in-the-loop automations: people validate critical decisions.
Related expertise