Data engineering for AI projects
No AI model is better than the data that fuels it. We build modern data platforms — data lakes, warehouses, streaming pipelines — with governance, quality, and lineage, so that every AI decision rests on solid and traceable ground.
Optimized data pipelines to fuel your AI models with quality, speed, and governance.
Use cases
- Multi-source data platforms for corporate groups
- Custom Customer Data Platforms (CDP)
- Real-time analytics for e-commerce
- Feature stores for data science teams
- Reverse ETL to CRM and marketing tools
Measurable benefits
- Reliable and timely data
- Reduced cloud costs with optimized architectures
- Self-service analytics for business users
- GDPR compliance and governance by-design
Technical details
Storage
- Snowflake, BigQuery, Databricks
- Data lake on S3/GCS with Iceberg/Delta
- PostgreSQL, ClickHouse for analytics
- Lakehouse architecture
Ingestion & transformation
- Airbyte, Fivetran for SaaS connectors
- dbt for versioned SQL transformations
- Apache Spark for batch
- Kafka + Flink for streaming
Quality & governance
- Great Expectations for data quality
- dbt tests + alerting
- Catalog: DataHub, Atlan, OpenMetadata
- Automatic end-to-end lineage
Orchestration
- Apache Airflow, Prefect, Dagster
- Schedule + event-driven triggers
- Retry, backfill, SLA monitoring
- Full observability
FAQ
Can I start without a data warehouse?
Yes, but it is the first step we recommend. We build scalable data foundations from scratch with Snowflake/BigQuery/Databricks.
What does data lineage mean?
It is the map that tracks every piece of data from the source to the final report. Critical for audits, debugging, and compliance.
How much does a data platform cost?
It range from basic setups (~15k€) to enterprise platforms with hundreds of pipelines. Cloud costs are separate and managed based on consumption.