Services
Data Engineering
Build data pipelines and analytical models for the questions your team needs to answer.
01 — Scope
Make data useful and dependable.
When we can help
- AI initiatives are blocked because the data they need is scattered, stale or untrusted.
- Pipelines break silently and reports are reconciled by hand.
- Your tools make it difficult to trace relationships between customers, records or dependent systems.
- Analytical queries are slow or put pressure on production databases.
What we do
- Data platforms: lakehouse, warehouse and streaming architectures
- Batch and streaming pipelines with testing, lineage and monitoring
- Graph data models and graph databases for connected data
- Analytical databases for interactive reporting and exploration
- Datasets for AI retrieval, model features and evaluation
- Data quality, governance and access controls
02 — Experience
Selected projects
Selected experience of the engineers behind Vio Engine, including work before the company was formed and through partner consultancies. Client names are withheld.
03 — Delivery
What your team can expect
- Data quality checks and ownership defined with the teams that use the data
- Pipelines with tests, monitoring and a documented recovery process
- An analytical model tested against your real queries and performance requirements
Platforms and tools
Chosen around your environment and requirements.
- Snowflake
- Databricks
- BigQuery
- Postgres
- Kafka
- dbt
- Airflow and Dagster
- Neo4j, TigerGraph, FalkorDB, KuzuDB and other graph databases
- ClickHouse
- DuckDB
- OpenSearch
- Spark
- Python and SQL
We also build VioGraph, our software for exploring connected data on your own infrastructure.
Talk to Vio Engine
Tell us what you need to change, build or support.