A secure architecture decision
Make workspace, catalog, access, lineage, data-domain, and control boundaries explicit enough for security, governance, and engineering leaders to evaluate together.
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Databricks lakehouse risk review
Databricks lakehouse consulting that turns platform risk into decisions.
Use an independent Databricks architecture review to test lakehouse security, Unity Catalog governance, workload cost, production reliability, and analytics readiness before the next platform commitment.
Discuss your situationAt a glance
Know what the engagement is before the first call.
Scope is confirmed on the strategy call, but the working boundaries stay visible from the start.
- Best fit
- Enterprise data leaders evaluating Databricks lakehouse architecture, preparing to scale an existing estate, or seeking independent evidence before a major platform decision.
- Review scope
- Workspace and catalog design, identity and access, Unity Catalog, lineage, Delta patterns, cluster policies, jobs, SQL warehouses, workload reliability, cost drivers, analytics service levels, and operating ownership.
- Deliverables
- Executive risk brief, architecture findings, security and governance heat map, cost and workload baseline, analytics benchmarking, decision records, and a prioritized remediation or readiness roadmap.
- Typical timeline
- Usually 1-3 weeks for a Lakehouse / Unity Catalog Audit and 3-5 weeks for a broader lakehouse readiness assessment.
- Pricing
- $12,500-$25,000 for the Lakehouse / Unity Catalog Audit; $25,000-$45,000 for a broader Databricks Lakehouse Readiness Review.
- Not included
- Penetration testing, legal compliance opinions, managed operations, vendor-paid recommendations, and open-ended implementation are not included.
The tension
A lakehouse can be technically live while security boundaries, ownership, compute economics, workload design, and trusted analytics remain unresolved. The longer those risks stay implicit, the more expensive they become to correct.
What changes
Outcomes your organization can feel.
Clear evidence of progress, not a binder that becomes shelf decor.
An explainable cost baseline
Connect compute, scheduling, storage, job design, SQL workloads, and operating choices to the cost drivers leadership can govern.
A credible readiness benchmark
Compare current reliability, governance, ownership, and analytics performance with the evidence required to scale, modernize, or support AI workloads.
Ways to engage
Start where the pressure is highest.
Databricks architecture review
Assess the current or proposed lakehouse design, Unity Catalog model, workload boundaries, Delta patterns, production practices, and decision gaps.
Lakehouse security audit
Review identity, access, catalog controls, sensitive-data boundaries, lineage, ownership, and audit evidence. Lakehouse security audits identify exposure; they do not replace penetration testing or legal advice.
Cost and analytics benchmark
Use cost optimization consulting and analytics benchmarking to identify workload inefficiency, service-level gaps, and the changes worth funding first.
Good fit
This work is built for leaders who...
- Are evaluating Databricks architecture before a major investment, migration, or scale decision.
- Need an independent lakehouse readiness assessment instead of another implementation proposal.
- Have rising platform spend without a defensible workload and ownership baseline.
- Need security, governance, engineering, and analytics leaders to act from the same risk picture.
Decision support
Review the evidence before you schedule.
Use the proof, pricing, and procurement details to decide whether a conversation is worth your time.
Next step
Get an independent risk view before the next lakehouse commitment.
Bring the messy version. We will figure out where the leverage actually is.
30-Minute Strategy and Architecture Fit Call