Databricks consulting
Databricks gives you a lakehouse, a Spark runtime, a governance layer and an ML platform in one product. The breadth is the advantage and also the problem: teams adopt one piece well and inherit the operational surface of all four.
Where Databricks costs get away from teams
Clusters that never sleep. Interactive clusters with long or absent auto-termination, kept alive by a notebook someone left open. The commonest line item on an inflated bill.
Interactive compute running production jobs. Jobs compute is materially cheaper per DBU than all-purpose. Scheduled work sitting on interactive clusters is a straight overpayment for identical work.
Over-provisioned autoscaling. A minimum worker count set for a peak that happens twice a month, paid for continuously.
Small files. Thousands of tiny Parquet files turn every read into metadata overhead. Compaction and optimisation are unglamorous and pay back immediately.
Spot instances avoided out of caution. For fault-tolerant batch work, spot workers with on-demand drivers cut cost substantially at modest operational risk.
Lakehouse architecture
We build medallion layouts (bronze for raw ingestion, silver for cleaned and conformed, gold for business-ready aggregates) with Delta Lake underneath for ACID guarantees and time travel.
The judgement call worth making early is where the boundary between silver and gold sits. Push too much business logic into silver and every downstream team inherits assumptions they didn’t agree to. Push too little and the same joins get rewritten in six places, differently each time.
Governance with Unity Catalog
Table-level and column-level access control, lineage, and a single namespace across workspaces. Teams that adopt Unity Catalog after the fact spend far more effort than teams that start with it, because permissions have already been encoded in a dozen ad-hoc places by then.
If you handle regulated or personal data, this is where GDPR obligations become enforceable rather than aspirational. Our engineers work inside the EU, so data residency stays a configuration question rather than a legal one.
Spark tuning
Skew, shuffle and partition sizing account for most of what makes a job slow. Adaptive query execution handles more of this than it used to, but a job that reads badly-partitioned source data will stay slow whatever you set. The fix is usually upstream of the job people ask us to look at.
MLflow and models
Experiment tracking, model registry and staged promotion. Databricks makes level 1 and 2 MLOps maturity reachable without assembling a platform from parts, which is an advantage for teams with a handful of models and no dedicated platform engineer.
Go deeper
- Data engineering services — pipelines and orchestration
- Data warehouse consulting — architecture across platforms
- Snowflake consulting — the warehouse alternative
- What is MLOps? — practices and maturity model
- Hire data engineers — embed engineers directly
Want a read on your Databricks spend or architecture?
Bring your cluster configuration and job history to a free 30-minute call. You get named savings and an architecture opinion, from an engineer who has run this in production.
staffai.eu · Senior AI and data engineers from Eastern Europe, on T&M