Data engineering you can trust. Analytics people use.
Most analytics programmes do not fail on visualization. They fail because nobody believes the numbers. Our data engineering and Databricks consulting services build the pipeline and the trust layer underneath it at the same time.
What our data engineering and Databricks services deliver
Lakehouse architecture
- Delta Lake and medallion architecture
- Unified batch and streaming: Auto Loader, Structured Streaming, DLT
- Spark performance engineering
- Migration from legacy warehouse and Hadoop estates
ML and AI on Databricks
- MLflow lifecycle and model registry
- Feature stores and model serving
- LLM fine-tuning and RAG with Mosaic AI
- Kafka-sourced real-time feature pipelines
Quality, governance and cost
- Data contracts and validation at ingestion
- Unity Catalog lineage, cataloguing and access control
- Reconciliation against source and freshness SLAs
- Cluster policies, auto-scaling and cost attribution
Self-healing pipelines and BI
- Schema-drift and anomaly detection with triggered remediation
- Orchestration with Airflow and dbt, CDC from source systems
- Governed semantic layer and dimensional models
- Self-service BI, operational dashboards and forecasting
A dashboard nobody trusts is worse than no dashboard. Quality checks, lineage and reconciliation ship with the pipeline, not in a later phase.
Data engineering tools we work with
Related services and industries
Questions about data engineering and Databricks
Do we need Databricks?
No. We recommend Databricks when one platform for data engineering and machine learning fits your team. We also build on Snowflake, on open-source Spark and on-premises stacks when that is the better fit.
How do you make people trust the numbers?
Data contracts at ingestion, reconciliation against source systems, lineage you can follow from report to source, and freshness targets that are monitored. Those ship with the first pipeline, not later.
Can you take over an existing platform?
Yes. We can start by stabilizing and governing an existing estate, cutting runaway compute cost and fixing quality issues before building anything new.
Start with one real problem.
Tell us about the asset estate or system you are working on. A senior engineer will reply, not a sales team.