- contact@verticalserve.com
Design, migrate, and operate the Databricks lakehouse that your AI actually runs on — Unity Catalog governance, medallion architecture, Delta pipelines, and Mosaic AI, built inside your environment by a principal-led squad.
We work across the full Databricks platform — from the first architecture decision to the pipelines, models, and governance that keep a lakehouse trustworthy years after go-live. Most engagements start with an assessment and end with your team running the platform themselves.
We assess your current data estate — warehouses, lakes, ETL sprawl, and the reporting that depends on them — and produce a target lakehouse architecture with a sequenced roadmap. You get a clear view of what to migrate, what to rebuild, what to retire, and what it costs before anyone writes code.
Bronze, silver, gold, and serving layers designed properly — raw landing with a real ingestion metadata contract, conformed dimensions and facts with correct grain, surrogate keys and SCD handling that survives history. We model for the questions the business actually asks, not for a diagram.
Catalog and schema design, group-based access control, row filters and column masks for PII, tag taxonomies, lineage, and audit. We set governance up as the default path rather than a retrofit, so analysts and AI agents get the same enforced permissions.
Hadoop, Teradata, Netezza, Oracle, Synapse, or Snowflake onto Databricks. We use automated conversion tooling for SQL, stored procedures, and workflow definitions, run old and new in parallel, and reconcile row-and-measure level before anything is cut over.
Lakeflow Declarative Pipelines, DLT, dbt on Databricks, and hand-built Spark where it earns its place. Incremental merge patterns, CDC handling, backfill and replay strategy, expectations and quarantine paths — pipelines that fail loudly and recover cleanly.
Kafka, Kinesis, and Event Hubs into Delta with Structured Streaming and Auto Loader. Exactly-once semantics, schema evolution, watermarking, and the operational runbook that keeps a streaming lakehouse healthy at 3am. Backed by our Kafka POD when the source side needs work too.
Serving layers engineered for sub-second dashboards — pre-aggregated marts, materialized views, liquid clustering aligned to real access patterns, warehouse sizing, and query tuning. We connect Power BI, Tableau, Looker, and AI/BI Genie onto a single governed semantic definition.
Feature engineering, experiment tracking, model registry, batch and real-time scoring on Model Serving, plus drift and performance monitoring. Model outputs land as governed tables with version and scoring lineage, so every prediction on a dashboard can be traced to the run that produced it.
RAG and agent systems built on Vector Search and the Mosaic AI Agent Framework, grounded in Unity Catalog so retrieval respects the same permissions as SQL. Evaluation harnesses, guardrails, and tracing come standard — we do not ship a demo and call it production.
Expectations at the pipeline boundary, reconciliation between layers, freshness SLAs, and quality scorecards published as first-class tables. The goal is that a broken number is caught by a test, not by an executive in a board meeting.
Cluster policies, serverless versus classic decisions, photon economics, job right-sizing, storage lifecycle, and chargeback by tag. We instrument system tables so spend is attributable to a team and a workload rather than a single opaque line item.
Workspace topology, Terraform provisioning, Databricks Asset Bundles, CI/CD for notebooks and pipelines, environment promotion, and secrets handling. The platform becomes reproducible infrastructure instead of a workspace someone clicked together.
Every Databricks POD begins with assets we have already built and hardened. They get adapted to your estate rather than rewritten from scratch, which is most of where the 12-week timeline comes from.
Deployable Unity Catalog blueprints for a domain — schemas, dimensions, facts, bridges, serving aggregates, and an ML output layer, with table comments and governance tags baked in. Our P&C insurance blueprint spans 120+ tables across nine schemas.
Converters for legacy SQL, stored procedures, and scheduler definitions, plus a reconciliation harness that compares source and target at row and measure level so cutover is a decision backed by evidence.
An automated audit of an existing workspace — schema drift against the intended model, clustering versus real query patterns, orphaned and empty tables, missing constraints and comments, and spend hot spots. Delivered as a prioritized findings report.
Our governance layer on top of Unity Catalog — business glossary, metric definitions, ownership, and data contracts, so a semantic definition exists in one place and both BI and AI agents read from it.
Expectation libraries, reconciliation tests between medallion layers, and freshness monitors that ship with the pipelines rather than being added after the first production incident.
Guardrails, tracing, and evaluation for the GenAI workloads that sit on the lakehouse — so retrieval quality and model behaviour are measured continuously, not assessed once at launch.
Our most developed domain blueprint. It is the reference we adapt for carriers, MGAs, and brokers who want an AI and analytics foundation that auditors and actuaries both trust.
The blueprint separates raw landing, cleansed entities, a conformed star schema, a pre-aggregated serving layer, model outputs, segmentation, external data, reference data, and data quality — each as its own Unity Catalog schema with its own access model and refresh contract.
The details are what separate a lakehouse that survives an audit from one that quietly disagrees with itself.
A Databricks POD is 4–6 senior engineers working inside your environment, on your backlog, with your team in the room.
Two weeks. Current estate, workload inventory, cost baseline, and governance gaps. Output is a target architecture and a migration or build sequence your team has signed off on.
Workspace topology, Unity Catalog structure, access model, CI/CD, and the first ingestion path landing real data in bronze. Everything provisioned as code from day one.
Medallion layers, pipelines, serving marts, and models delivered in two-week increments against a live backlog. Reconciliation and quality tests ship with each increment.
Runbooks, monitoring, cost guardrails, and paired delivery with your engineers until they are running it. You keep the code, the IaC, and the documentation.
Most clients begin with an assessment and convert into a full POD once the roadmap is agreed.
2–3 weeks, 2 principals
12 weeks, 4–6 engineers
Rolling, scales up and down
Field notes from the lakehouse work — architecture, governance, and the mistakes worth avoiding.