DATA ENGINEERING

Data Engineering
Data Pipelines

Without quality data, no high-performing Machine Learning. Data Engineering is the foundation that enables you to collect, transform, and make your data available for analysis and AI. At NeuroVista, we design modern data architectures that evolve with your needs: data lakes, data warehouses, ETL/ELT pipelines, and data quality platforms.

Concrete Use Cases

100x

Analytical query acceleration after migration to a modern data warehouse

99.9%

Data pipeline reliability through orchestration and monitoring

-70%

Storage cost reduction via intelligent partitioning and compression

15min

Near real-time data freshness vs previous daily batch

50+

Data sources integrated into a unified data lake

Our Approach

We build sustainable and scalable data architectures.

01

Audit & Scoping

Mapping of your data sources, existing quality analysis, and analytical needs identification.

02

Proof of Concept (POC)

End-to-end pipeline setup on a limited scope. Technology validation and performance testing.

03

MVP & Industrialization

Data infrastructure deployment (data lake, warehouse), pipeline development, and quality control setup.

04

Production & Optimization

Pipeline monitoring, performance and cost optimization. New source onboarding and team training.

Deliverables

Documented data architecture
Configured Data Lake / Data Warehouse
Automated ETL/ELT pipelines
Orchestration (Airflow, Dagster, Prefect)
Data Catalog and documentation
Data Quality controls (Great Expectations, dbt tests)
Monitoring dashboards
Data Engineering team training

Key Technologies

AWSGoogle CloudCloud RunKubernetesTerraformPulumiArgo CDGitHub ActionsIstioOpenTelemetryBigQuerySnowflakeDatabricksSparkFlinkKafkadbtAirflowIcebergDelta Lake

Frequently Asked Questions

Build your data foundation

Let's define together the data architecture that will support your AI ambitions.

Schedule a data audit