Data engineering agency to organize, ensure the reliability of, and maximize the value of your data

Scattered data, unstable pipelines, unreliable reporting, and AI projects slowed down by poor data quality: data engineering is becoming a strategic lever for transforming your data into actionable assets.

We help companies design, modernize, and scale their data architectures: data lakes, data warehouses, data lakehouses, pipelines, governance, AI preparation, and business-use-case-oriented models.

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What is data structuring (or data engineering)?

The data engineering encompasses all the practices that enable us to collect, transform, organize, secure, and make data available to business, analytics, AI, and IT teams.

In practical terms, this discipline addresses three key challenges: ensuring the reliability of data flows, making data understandable, and accelerating its use in decision-making tools, business applications, and artificial intelligence models.

Data Pipeline: ingestion, transformation, orchestration, and monitoring of data flows
Data Modeling : structuring tables, data repositories, dimensions, and metrics
Data Governance : quality standards, cataloging, ownership, traceability, and compliance

Tell us about your data project

Some statistics on the importance of structured data

63%

Many organizations do not yet have the right data practices in place for AI. Without data that is prepared, governed, and accessible, AI projects remain difficult to scale. Source: Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” 2025

72%

IT leaders believe that the lack of real-time data infrastructure is hindering the scaling of AI. Data architectures must support reliable, traceable, and continuously actionable data streams. Source: Confluent, 2026 Data Streaming Report, 2026

43%

Operations executives identify data quality as their top data priority. Data reliability is becoming a prerequisite for reporting, automation, analytics, and AI. Source: IBM Institute for Business Value, 2025

When should you restructure or modernize your data foundation?

A data architecture quickly reaches its limits when use cases multiply without a shared framework.

Audit your data foundation

There are several warning signs to watch for:

#01

Teams no longer trust the numbers used in reports.
#02

Manual processing takes too much time and leads to errors.
#03

Data is duplicated across CRM, ERP, e-commerce, PIM, and BI tools.
#04

Pipelines frequently break or lack supervision.
#05

AI projects are stalled due to a lack of clean, historical, or well-documented data.
#06

The choice between ETL, data lake, data warehouse, or data lakehouse is unclear
#07

Data governance remains theoretical, with no operational rules or designated responsible parties.

Our data engineering services

We approach data engineering as a business foundation, not merely as a technical project. The goal is to build a reliable, scalable foundation that can be leveraged by teams that make decisions, automate processes, or develop AI use cases.

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- Mapping of Sources, Flows, Tools, Processing Steps, and Critical Dependencies
- Analysis of data quality, duplicates, schema breaks, and inconsistencies
- Identifying business pain points: slowness, lack of trust, rework, silos
- Recommendation between datalake, data warehouse, data lakehouse, data hub or hybrid architecture
- Definition of data zones, including themedallion architecture when appropriate: bronze, silver, gold
- Developing a roadmap aligned with your BI, AI, business, and real-time needs
- Implementation of robust, tested, and monitored pipelines
- A Clear Distinction Between ETL vs. ELT based on your data volumes, tools, and constraints
- Industrialization of data cleaning, standardization, and enrichment
- Implementation of data governance that are concrete and enforceable
- Role Definitions: data owner, data steward, IT teams, business teams, analytics
- Monitoring of indicators related to quality, freshness, completeness, traceability, and usage
- Preparing Reliable Datasets for BI and reporting and analyticsanalytics engineering
- Structuring of data products that can be reused by business domain
- Data Preparation for AI: embedding, AI vectorization, semantic search, RAG, and vector databases

Why should you entrust your data engineering to DATASOLUTION?

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A user-centered approach

We start with real-world use cases: sales management, e-commerce performance, supply chain, customer insights, business process automation, and AI. The data foundation serves a clear purpose—not just a theoretical architecture.

Scalable architectures, not static ones

Your organization can move forward in stages: modernizing a critical data flow, redesigning a data warehouse, creating a data lake, transitioning to a data lakehouse, or deploying a domain-based data mesh model.

Actionable and Governed Data

A data project is successful when teams understand the data, trust it, and know how to use it. We incorporate data governance, documentation, and quality right from the design phase.

Our data engineering methodology

#01

Scope Definition: Aligning Business, Data, and IT Objectives

We identify priority use cases, critical data sources, security constraints, data volumes, IT dependencies, and expected metrics. This phase allows us to define a realistic goal, with clear trade-offs between speed, robustness, and scalability.

#02

Build: Standardizing Workflows and Models

Pipelines are built in batches, with testing, documentation, oversight, and quality standards. Transformations become traceable, data models become readable, and dependencies are managed. Teams can track what’s happening, make corrections more quickly, and evolve the processing without having to start from scratch.

#03

Governance: Ensuring Data Reliability Over Time

We establish naming conventions, cataloging procedures, quality controls, responsibilities, and approval workflows. Governance is not just a document—it becomes part of our day-to-day operations, serving the needs of our users.

#04

AI Acceleration: Preparing Data for New Applications

AI use cases require clean, contextualized, and well-structured data. We prepare content, historical data, and reference data for applications such as vectorization, embeddings, augmented search, and business assistants.