Your customer bought it. We help them use it.

You know the account. They bought Databricks, Fabric or Snowflake with big plans, and six months later the data engineer they were counting on is still fighting the Epic extract. Nobody is using what they bought, and your renewal is on the line. We get their data flowing in days, so the people who wanted answers get them, and the consumption you sold shows up.

HIMSS '23 · '24 · '25
Featured by Microsoft and Databricks at three consecutive annual conferences
75+ connectors
Including schema-aware Epic and Workday, which stall more deployments than anything else
Days, not months
Time to a governed, production lakehouse on your customer's existing platform

When a stalled deployment puts your renewal at risk

When your customers buy Microsoft Fabric, Databricks, or Snowflake, they plan big AI and analytics projects. Then the data work takes longer than expected, because their IT team doesn’t have the specialized data engineering skills to get it running.

The deployment stalls, consumption stays flat, and the renewal gets harder.

We’ve been on a lot of those calls. You’re upbeat, the customer is polite, and everyone in the room knows the Epic extract is three months behind. Databasin itself started on that same kind of extract, at an academic medical center.

For your customers, Databasin handles the data engineering: it connects their systems and turns hand-written pipelines into repeatable ones their own team can run.

Faster consumption
We get data flowing into your platform within days. A stalled deployment turns into active, growing consumption.
A stronger account relationship
We take on the hard part of the deployment, so you’re the partner who helped them succeed.
We work alongside your platform
Databasin runs on top of your platform rather than replacing it, and we bring you into every win.

How to spot a Databasin opportunity in your book.

Keep an ear out for these phrases during customer calls. If you hear them, it's time to bring us in.

We bought [Databricks / Fabric / Snowflake] but we're not really using it yet.
→ Classic stalled activation. This is our entry point.
We've been trying to get Epic data out of Clarity for months.
→ Databasin solves this in days. Schema-aware Epic connector, built in production at WashU Medicine.
Our team doesn't have the skills to manage the platform long-term.
→ The exact skills gap we fill. Databasin abstracts the engineering complexity so their team doesn't have to own it.
We're paying an implementation consultant to do this, but we can't afford them forever.
→ We replace the run-state dependency. A consulting engagement has an end date. Databasin keeps running after the consultants leave.

Two stalled deployments we helped finish

Both are anonymized, and both started with an introduction from the platform’s account team.

Snowflake · Health System Rescue
Saving a stalled Snowflake deployment at a Midwest health system
The Challenge
The customer's internal engineering team spent months unable to get their complex Epic EMR data ingested and reporting-ready. The deployment was completely stalled, putting the Snowflake investment at risk.
The Play
The Snowflake AE introduced Databasin to the Chief Data Science Officer as an integration layer, not a competing platform.
The Win
Databasin's pre-configured Epic connector resolved the integration complexity. Data began flowing into Snowflake within days. That gave the customer the analytics capabilities they had bought Snowflake for, and it protected the partner's account.
Microsoft · Databricks · Research Acceleration
Accelerating a complex cancer research platform at a major academic medical center
The Challenge
The Chief Research Information Officer needed a modern cloud solution for complex cancer research data, but lacked the internal team to manage the technical demands of an Azure and Databricks architecture.
The Play
Building on Databasin's production deployment at WashU Medicine, the Microsoft AE introduced Databasin as the automated framework to manage the infrastructure, so the research team could focus on the science.
The Win
Databasin provided the implementation layer for ingestion and modeling, so the research team could start working in their Azure and Databricks environment sooner.

From stalled account to closed deal in four steps.

One thing we’re not: a consultancy. We don’t staff your customer’s project for a year. We make the work small enough that their own team can run it after we leave.

01
You identify the stall
A customer with an active deployment who can't get Epic, Workday, or their legacy systems into the platform and delivering value.
02
We meet the customer together
A joint call with your AE and one of our co-founders, usually Jake Gower, who leads our partnerships, with Chris Lundeberg when it gets technical. We diagnose the stall and show how Databasin works alongside the platform they bought.
03
Databasin deploys alongside your platform
BYO mode means Databasin layers on top of your customer's existing environment. Their existing investment stays in place.
04
Your deal closes, customer succeeds
The customer gets value from the platform they bought. Your deployment succeeds. We structure co-sell terms that work for both sides.

How Databasin works with Azure, Databricks and Snowflake

Microsoft
Azure · Fabric · Synapse
Your customer is on Azure but Epic or Workday data isn't flowing. Databasin's connectors land data into their Azure tenant, so PHI never leaves their environment. Medallion architecture gets them to governed gold data without custom engineering.
Referral signals
Health system that can't get Epic into Fabric or Synapse
Customer asking about HIPAA-compliant data architecture
Azure OpenAI project stalled with no governed data layer underneath
Databricks
Delta Lake · Unity Catalog · MLflow
Your customer bought Databricks for the lakehouse and ML capabilities, but custom-built ingestion is consuming all their engineering capacity. Databasin's modules replace the custom ETL, freeing the team to use Databricks for analytics and ML.
Referral signals
Engineers maintaining 20+ custom pipelines instead of building analytics
Databricks deployment underutilized due to ingestion backlog
Epic or Workday identified as blockers in QBR
Snowflake
Data Cloud · Cortex AI · Marketplace
Your customer is on Snowflake but the data stack is still fragmented: separate ETL tools, no medallion architecture, no governed semantic layer. Databasin adds the structured ingestion and transformation layer so the Snowflake investment starts paying off.
Referral signals
Customer paying for Fivetran or MuleSoft on top of Snowflake
Raw data going straight to BI with no medallion architecture
Cortex AI stalled because underlying data isn't governed
Introduce a customer

We'll review the opportunity and follow up within one business day. Jake or Chris will reply personally.