Databricks Partner

Databricks Consulting for Enterprise AI at Production Scale

DS Stream is a Databricks Partner with 80+ enterprise projects delivered. We design, build and operate Databricks platforms, operating models and AI use cases, from Lakehouse data foundations to production agentic AI. We work with Fortune 500 CPG and Retail organizations that need governed AI with measurable business value.

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Key takeaways

DS Stream is a Databricks Partner with 80+ enterprise Databricks projects, from Lakehouse foundations to production agentic AI.
The DS Stream team holds Databricks certifications for Data Engineer, Data Engineer Professional and Machine Learning Professional.
Every DS Stream Databricks engagement includes Unity Catalog and the MLflow model registry, so every model is tracked, governed and auditable.
DS Stream reports 10x faster time to first AI product in production and a 60% reduction in model deployment effort through automation.
For the AI Factory, Phase 1 is an 8-week MVP that delivers a governance framework, a Databricks platform foundation and the first two use cases in production.
CPG is the deepest DS Stream credential on Databricks. Financial Services, Retail, Manufacturing and Healthcare are also served.

What does DS Stream deliver on Databricks?

DS Stream delivers Databricks end to end: scalable Lakehouse foundations and declarative data pipelines, governed ML and AI operations, business-facing analytics and production-grade agentic AI. The work draws on 80+ Databricks projects, a certified team, standardized delivery patterns and reusable accelerators that shorten time-to-value for Fortune 500 CPG and Retail organizations.

Databricks stack we work with

Lakehouse Platform
Unity Catalog
MLflow and Model Serving
Genie and AI/BI
Agent Bricks and Lakebase
Lakeflow Pipelines

What blocks production-scale AI on Databricks?

Most enterprises that scale AI on Databricks hit four recurring blockers. These are unclear platform risk, ownership gaps between data and AI teams, missing engineering standards and slow delivery cycles. DS Stream closes these gaps with a proven operating model and reusable patterns. As a result, pilots hold up under production data volumes, governance needs and operational demands.

Platform risk: unclear architecture ownership produces fragile platforms that cannot scale with business demand.
Ownership gaps: no single team owns the full AI platform lifecycle, which causes handoff failures and delays.
Missing standards: without delivery standards, each new project starts from scratch and adds inconsistencies.
Delivery gaps: pilot-stage approaches break under production data volumes, governance needs and operational rigor.

Who is Databricks consulting from DS Stream for?

It is for the leaders who own data and AI outcomes in large organizations. That includes Chief Data Officers, Heads of AI and AI Factory Leads, Heads of Data Engineering, CTOs and CIOs, and CPG commercial and operations leaders. Each of them needs Databricks running in production with governance built in. A platform purchase or a policy document is not enough.

Chief Data Officer: a strategy that turns data into business outcomes, with governance that actually runs.
Head of AI or AI Factory Lead: the 5-layer AIEP architecture, reusable archetypes and provisioning automation, so each new AI product is faster and cheaper to deliver.
Head of Data Engineering: streaming Lakeflow pipelines that replace brittle batch cycles, MLOps that cuts deployment effort by 60%, and agents that automate 80%+ of CPG data operations.
CTO or CIO: governance embedded at the architecture level, with Unity Catalog lineage, the MLflow registry and ISO 42001 controls.
CPG commercial and operations leaders: near-real-time retailer and SAP data instead of 2-hour batch cycles, and automated incident resolution instead of firefighting pipeline failures.

What you get

Databricks expertise across the full platform stack

Each capability is backed by production delivery, not certification alone.

End-to-end Databricks delivery

Scalable Lakehouse foundations and declarative data pipelines
Governed ML and AI operations
Business-facing analytics
Production-grade agentic AI

Certified team

Databricks Data Engineer certification
Databricks Data Engineer Professional certification
Databricks Machine Learning Professional certification

Standardized delivery patterns

Patterns for Databricks architecture, security, CI/CD and operations
Refined across 80+ enterprise projects

Reusable accelerators

Accelerators built on Databricks
Common components are not rebuilt from scratch
Shorter time-to-value for each new use case

Governance by design

Unity Catalog with centralized metadata, column-level access control, full lineage and immutable audit logging
MLflow model registry tracking every model version, experiment and deployment
ISO 42001-aligned governance frameworks

How does DS Stream build your Databricks practice?

DS Stream builds a Databricks practice in four steps. The starting point is your current state and the DS Stream reference architecture.

Assess
We audit the architecture, take a technology inventory and review your cloud footprint and governance to establish the current state.
Design
We run a blueprint whiteboarding workshop that uses the DS Stream reference architecture as the starting point for the target design.
Architect
We build the target architecture with a reusable component library and governance controls designed in from the start.
Map and decide
We select the right components: Databricks services, cloud AI services, MLOps and LLMOps tooling, and infrastructure-as-code standards.

What business impact does DS Stream report on Databricks?

10x
Faster time to first AI product in production.
Source: DS Stream Databricks page
60%
Reduction in model deployment effort through automation.
Source: DS Stream Databricks page
100%
Model visibility: every model is tracked, governed and auditable through MLflow and Unity Catalog.
Source: DS Stream Databricks page

Most common questions about Databricks consulting

What does it mean that DS Stream is a Databricks Partner?

For DS Stream, partnership means production delivery, not certification alone. DS Stream has completed 80+ enterprise Databricks projects. It has a team certified as Data Engineer, Data Engineer Professional and Machine Learning Professional. It also uses standardized patterns for Databricks architecture, security and CI/CD, plus reusable accelerators built on the platform.

We already run Databricks internally. Why engage DS Stream?

Having platform access is only the baseline. The hardest problems sit in the operating model, governance and the MLOps layer. DS Stream brings production delivery patterns from 80+ projects, reusable accelerators, ISO 42001-aligned governance frameworks and an AI Factory operating model that shortens time-to-value. We can compare approaches with your team and identify the gaps we can fill.

How does DS Stream handle Unity Catalog governance and model auditability?

Unity Catalog is part of every DS Stream engagement from day one. It provides centralized metadata management, column-level access control, full data lineage and immutable audit logging. The MLflow model registry tracks every model version, experiment and deployment. In a leading CPG AI Engineering Platform engagement, this approach delivered 100% model visibility and governance coverage.

How quickly can DS Stream deliver value on Databricks?

Engagements are phased and value-gated. For the AI Factory, Phase 1 is an 8-week MVP. It delivers a governance framework, a Databricks platform foundation and the first two use cases in production, and it captures an ROI baseline before Phase 2. For Agentic CPG Data Operations, a 4-week PoC delivers a working prototype with 5 automated sources. The PoC fee is credited toward full implementation.

We are still in the pilot phase. Is it too early to talk to DS Stream?

No. The pilot phase is the best time to engage. The most expensive mistake in enterprise AI is building pilots without a governance architecture and a reusable platform foundation, then retrofitting both after scaling. A short architecture conversation at the pilot stage can prevent months of rework and technical debt later.

How is DS Stream different from large system integrators on Databricks?

DS Stream is deliberately focused rather than broad. Its CPG AI Factory work on Databricks spans multiple years of ongoing platform partnership, 80+ projects of production patterns and reusable accelerators. DS Stream builds alongside client teams, and knowledge transfer is a design objective rather than a dependency model. Clients leave each engagement more capable of running the platform themselves.