Horizon

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READING INSTRUMENT · 01

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DATA ENGINEERING

Data Engineering
the foundation before the intelligence

We design pipelines, warehouses and quality controls that make data usable for analytics, software and AI. The architecture adapts to your sources, uses and operating constraints.

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What the foundation brings

01

Unification

Your scattered sources converge into a single, governed reference.

02

Freshness

The freshness needed for each decision is defined and made visible in the relevant flows.

03

Quality

Controls can follow schemas, missing values, uniqueness and distributions at each stage.

04

Cost

Storage and compute are sized and reviewed against expected usage.

05

Foundation

The same base serves today's analytics and tomorrow's models.

Our Approach

A data architecture is built to last and to evolve. Both at once.

  1. 01

    Audit & Scoping

    Mapping of your sources, quality baseline, analytical needs. The target architecture and migration plan follow from there.

  2. 02

    Proof of Concept (POC)

    An end-to-end pipeline on a limited scope. Technology choices get validated before generalizing.

  3. 03

    MVP & Industrialization

    Infrastructure deployment, pipeline development, automated quality controls. Documentation and tests from the start.

  4. 04

    Production & Optimization

    Supervision, cost reviews and progressive source extension, with controls that protect existing flows.

Deliverables

  • 01Documented data architecture
  • 02Configured Data Lake and Data Warehouse
  • 03Automated ETL/ELT pipelines
  • 04Orchestration (Airflow, Dagster, Prefect)
  • 05Data Catalog and documentation
  • 06Data quality controls (Great Expectations, dbt tests)
  • 07Supervision dashboards
  • 08Data Engineering team training

Key Technologies

AWSGoogle CloudCloud RunKubernetesTerraformPulumiArgo CDGitHub ActionsIstioOpenTelemetryBigQuerySnowflakeDatabricksSparkFlinkKafkadbtAirflowIcebergDelta Lake

Frequently Asked Questions

NeuroVista, Paris

Build your data foundation

Describe your sources, uses and constraints so we can shape the right architecture together.

Discuss your data project