A Trusted AWS Partner
dbSeer is a Databricks partner, combining our data foundations expertise with Databricks' unified analytics and AI platform. The pairing is deliberate: dbSeer's foundation-first, modular approach ensures that data is clean, connected, and properly architected before any analytics or AI layer is deployed — making Databricks exponentially more powerful.
Together, dbSeer and Databricks help organizations move from fragmented, siloed data to a unified data lakehouse architecture. That journey looks different for every client, which is why we anchor our engagements to the Databricks Data Maturity Framework — a structured model that maps where your organization is today across data, analytics, and AI capability, and defines what "next" looks like. Whether you're just beginning to consolidate data sources or you're ready to operationalize machine learning at scale, dbSeer's role is to close the gap between where you are and where the framework shows you can go. We build the foundation, run and monitor the environment as it grows, and when the data is ready, we move into Gen AI deployment with the confidence that the infrastructure underneath it will hold.
Key Benefits for Databricks Customers
Clean data foundation built before Databricks deployment — noise-free analytics
Unified data lakehouse architecture that scales with the business
Particularly powerful for businesses with siloed data wanting a single source of truth
AI-ready infrastructure enabling advanced analytics and cross-location reporting
Foundation-first approach ensures the right fit before any technology is deployed
Supported Services
When dbSeer implements Databricks, we’re not just standing up a platform and walking away. Every engagement starts with an honest assessment of where you are on the Databricks Data Maturity Framework — because the right foundation looks different depending on where you’re starting from. From there, we build what your data actually needs, run and monitor the environment on an ongoing basis, and continue advancing your maturity as the business grows. For clients with AI on the roadmap, that progression makes it possible to move from concept to deployed Gen AI faster than most organizations expect, not because we skipped steps, but because we took the right ones in the right order.
Common Use Cases
Franchise networks consolidating data across locations
Mid-market organizations modernizing from siloed/fragmented data
AI/ML deployment on a properly architected foundation
Organizations moving to a unified lakehouse from legacy warehouses
Getting Started
1
Set the Foundation
Before anything runs, we make sure your environment is configured correctly from the ground up: setting up your data catalog so assets are organized, discoverable, and governed across the organization from day one.
2
Optimize for Performance
With the foundation in place, we optimize job clusters to ensure workloads run efficiently without unnecessary compute spend, and configure each Databricks service so it's delivering the way it was designed to.
3
Monitor and Mature
A Databricks deployment that isn't actively managed tends to drift. dbSeer continuously monitors and tunes your environment to maintain quality over time and as your needs grow, we work alongside you to keep advancing your maturity on the Databricks framework.