Databricks Implementation

Databricks on Google
Cloud Platform
Use Case

Tier-1 Railroad

Use Case

Databricks Implementation on GCP
for a Tier-1 Railroad

A major North American railroad was implementing Databricks on Google Cloud Platform to modernize its data platform and support advanced analytics and AI use cases.

The challenge was not just deploying Databricks, but doing it correctly within the constraints of GCP.

Constraints

The organization had to be careful about:

Identity and access complexity using service accounts and IAM across projects

Network design decisions, especially around Private Service Connect and VPC architecture

Storage and governance setup for GCS and Unity Catalog

Coexistence with existing platforms, including BigQuery and legacy systems

Regional constraints impacting feature availability and future scalability

A poorly designed implementation would lead to rework, governance gaps, and production instability.

What We Did

KData led the Databricks implementation on GCP, starting with architecture definition before any deployment.

Defined the target operating model across environments, domains, and ownership boundaries

Designed identity and access patterns using service accounts aligned with Unity Catalog governance

Architected the network, including VPC structure and private connectivity requirements

Established storage strategy on GCS with clear separation of data domains and external locations

Defined coexistence strategy between Databricks and existing platforms

Deployed Databricks workspaces aligned with the target architecture

Validated workloads, access patterns, and data flows before production rollout

Outcome

The result was a production-ready Databricks platform on GCP, built correctly from the start.

Clear governance model across data, identity, and access

Stable and secure network architecture aligned with enterprise requirements

Scalable foundation for data engineering, analytics, and AI workloads

No rework required post-deployment due to early architectural decisions

The platform was ready to support both current operations and future expansion.

Our Databricks on GCP Implementation Approach

A structured approach that prioritizes architecture before deployment.

Define the Operating Model

  • • Ownership across data, infrastructure, and platform
  • • Environment strategy across dev, staging, and production
  • • Domain separation and workspace strategy

Design Identity and Governance First

  • • Service account model aligned with Unity Catalog
  • • IAM roles and access boundaries for GCS and Databricks
  • • Catalog, schema, and storage mapping

Establish Storage Architecture

  • • GCS bucket structure aligned to data domains
  • • External locations and governed paths
  • • Lifecycle, access control, and isolation strategy

Design Networking Early

  • • VPC architecture and subnet design
  • • Private connectivity using Private Service Connect
  • • Alignment between workspace, compute plane, and data access

Validate Platform Constraints

  • • Region selection based on feature availability
  • • Serverless versus classic compute decisions
  • • Compatibility with security and networking requirements

Define Coexistence Strategy

  • • Role of BigQuery and existing systems
  • • Migration versus federation decisions
  • • Data product ownership and consumption patterns

Deploy and Validate

  • • Workspace deployment aligned with architecture
  • • End-to-end validation of pipelines, access, and governance
  • • Controlled production rollout

Optimize and Harden

  • • Performance tuning and cost optimization
  • • Governance enforcement and monitoring
  • • Operational readiness and support model

This approach reflects best practices from real Databricks on GCP delivery playbooks.

What to Watch Out For on GCP

Designing the workspace before defining the architecture

Underestimating service account and IAM complexity

Choosing the wrong network model early

Assuming serverless will work for all workloads

Treating storage as just a bucket

Overusing BigQuery federation

Ignoring region and feature constraints

Mixing responsibilities across teams without clear ownership