One Lakehouse, Not Three Stacks
Data engineering, analytics, machine learning, and AI on a single open lakehouse, not three systems stitched together after the fact.
Partners
Unify Data, Analytics, and AI on Databricks, with Flentas
Most enterprises run one stack for reporting, another for machine learning, and a scramble of tools for AI, with governance bolted on at the end. The Databricks Data Intelligence Platform replaces that with one governed lakehouse for data engineering, analytics, and AI. Flentas designs, migrates, and operates it on AWS, so your teams work from a single, trusted copy of the data, all the way through to production AI.
Key Benefits
One Platform, One Governance Model
Most enterprises run one stack for reporting, another for machine learning, and a scramble of tools for AI, with governance bolted on at the end. The lakehouse replaces that with one governed platform, end to end.
Governed From Data to Agents
Unity Catalog governs data, models, and metrics in one place, with lineage, fine-grained access, and consistent metric definitions.
Open Formats, No Lock-In
Built on Delta Lake and open formats, so your data stays portable and your options stay open.
A Foundation Production AI Can Trust
Mosaic AI and Agent Bricks build domain-specific AI agents grounded in governed lakehouse data — not disconnected from it.
Proof Points
2,500+Accounts unified in a governed, OCSF-based data platform
500M+Events processed per day in near real time on the platform pattern
100%Of heavy reporting moved off a production database onto a governed lake
How It Works
How We Deliver
We prove value on one governed foundation, then extend the same platform and pattern to analytics and AI. It is the discipline behind data and AI programs that scale rather than stall.
1
Assess
We review your data and AI estate, design the target lakehouse architecture, and hand you a prioritized roadmap with the first use cases and a business case.
2
Build
We stand up the lakehouse on AWS, migrate priority workloads, build governed pipelines and Unity Catalog governance, and put a first analytics or AI use case into production.
3
Scale
We extend into self-service analytics with Genie and production AI with Mosaic AI and Agent Bricks, and operate the platform with cost and quality under control.
Technology Stack
Technologies & Tools We Use
Lakehouse Foundation
Delta Lake and open formats, standing up the platform on AWS and migrating off legacy warehouses.
Governed Data Engineering
Streaming, batch, and change-data-capture pipelines with Lakeflow, with data quality and observability built in.
Unified Governance
Unity Catalog governs data, models, and metrics with lineage, fine-grained access, and consistent definitions.
Analytics & Self-Service
Databricks SQL for BI, and AI/BI Genie for natural-language questions from business teams.
Machine Learning & Generative AI
Mosaic AI trains, serves, and governs models; Agent Bricks builds production-ready, domain-specific AI agents.
FinOps for the Platform
Right-sized serverless compute and visible spend, so scale does not mean runaway cost.
Use Cases
Industries & Scenarios We Serve
Legacy Data Warehouses
Aging, costly, and rigid warehouses migrated to the lakehouse with tooling-assisted, predictable migrations.
AI Pilots Stuck in Notebooks
Models and agents are ready, but the governed context they need is not — the lakehouse gives them trusted, well-described data to ground on.
Fragmented BI and ML Stacks
One stack for reporting, a different one for machine learning, and ad-hoc AI tooling — unified onto a single governed platform.
Regulated, Data-Intensive Workloads
Financial services and other regulated sectors need governance and lineage built in from day one, not stitched on after.
Case Studies
Proof, Not Promises
100%Of Reporting Moved to a Governed, Open-Format Lake
Cashew Payments had no data-lake strategy and reporting queries were degrading production performance. Flentas built change-data-capture into a governed S3 data lake with a Delta Lake layer — a ready bridge to a Databricks lakehouse.
500M+Events Per Day on a Governed, Open Platform
HPE needed unified governance and open formats at high volume. Flentas built a cloud-agnostic, OCSF-governed data platform across roughly 2,500 accounts processing more than 500 million events per day.
“We had a warehouse for reporting, a separate cluster for ML, and three different opinions about what "active users" meant. Flentas stood up the lakehouse, put Unity Catalog around all of it, and for the first time the data science team and the BI team are querying the same governed tables. Our first agent went from pilot to production in six weeks.”
Head of Data PlatformData-Intensive Enterprise, AWS Estate
Get Started
Is Your Data Estate Ready for Governed, Production AI?
A data and AI assessment reviews your estate, designs the target Databricks lakehouse on AWS, and hands you a prioritized roadmap from first pipeline to governed AI.
- AWS Advanced Consulting Partner
- 200+ Migrations Delivered
- 96.5% Client Retention