Develop the value of your information with Data Extraction, Transformation & Loading

Develop the value of your information with | Data Extraction, Transformation & Loading

Extract data from sources, Consolidate and enrich information, finally publish the results to your business community or downstreams.

the added-value we bring you

ETL Advisory

ETL product selection process
Workshop facilitation

ETL Architecture

ETL Architecture definition
ETL Best-practices definition
ETL Installation

ETL Project Management

ETL Program Management
ETL Project Management
Scope Management

ETL Design

Pre-defined templates to specify ETL data flows
Definition of ETL development guidelines
Design of ETL data flows

Turn-key ETL Implementation

Development of ETL data flows
Achieving optimal ETL performance
Tuning ETL processes
Integrating heterogeneous data sources
Extracting changed data
Detecting changes: Slowly Changing Dimension SCD
Detecting deleted records
Conforming and Cleaning

ETL Deyploment & Maintenance

ETL Flows monitoring
Evolutive maintenance of ETL flows
Corrective maintenance of ETL flows
On-call support

Our exclusive agile methodology to design & define data flows

Better support your business, Deliver faster, Improve quality


One of the main challenges when you develop ETL data flows is to be able to specify the business rules with no ambiguity.

For this, we developed our own methodology, based on 15 years experience designing and developing data flows.

This methodology is based on the observation that each data flow can fit into a template which can be leveraged to rationalize and industrialize the creation of ETL data flows.

data flows



  • Extract data using extraction services provided by sources
  • Store extracted data in a staging area


  • Reconcile data
  • Validate / Reject extracted data (functional & technical validation)
  • Enrich information


  • Publicate of information according to defined policy
  • Version information

This structures approach comes with:

  • Pre-defined business analysis template to gather and structure requirements
  • Implementation best-practices to industrialize the data flows development based on this methodology

Create data flows with