John Holland Group Accelerates Cloud Migration and Data Governance with SqlDBM

Infrastructure

Construction

Business Challenge

John Holland Group, one of the largest infrastructure construction firms in Australia with operations across New Zealand and Singapore, manages over 70 years of historical data. By 2023, the organization faced growing demand for advanced analytics, governance, and reporting to support large-scale infrastructure projects. However, their existing on-premises SQL Server environment was fragmented and not scalable.

To meet evolving business needs and regulatory standards (including ISO certification readiness), the company launched an ambitious replatforming initiative: migrate to a modern cloud data lake on Databricks.

The project required

The data team needed a robust modeling solution that could guide architecture decisions, facilitate collaboration, and ensure governance from day one.

Solution: SqlDBM + Modern Data Stack

John Holland selected SqlDBM as the core data modeling platform to guide its Databricks migration and align cross-functional teams. Working alongside dbt for transformations, SqlDBM played a critical role in planning, documenting, and governing the new data landscape.

Vertical

Data team

Headquarters:

Australia · New Zealand · Singapore

Website

https://johnholland.com.au/

SqlDBM became our authoritative source for documenting Databricks’ gold layer models, showing how dimensions and facts are related, streamlining our transition to the cloud.

David Zmood,
Data Platform Architect, Data & Analytics
Applications & Emergin Technology · John Holland Group

Strategic Modeling for Cloud Readiness

Using SqlDBM, the team reverse-engineered legacy structures, visualized complex dependencies, and streamlined their migration roadmap.

“SqlDBM became our authoritative source for documenting Databricks’ gold layer models, showing how dimensions and facts are related, streamlining our transition to the cloud.”

Governance Through a Centralized Modeling Forum

To maintain architectural consistency, John Holland established a Data Modeling Forum. This internal review body meets twice weekly, requiring all contributors to present their models and justify alignment with enterprise standards.

“When someone works on new data ingestion into the lake, they must explain how they’re modeling it. SqlDBM helps us review and reconcile these changes, preventing redundant tables and ensuring consistency across projects.”

Collaboration Across Stakeholders

SqlDBM enabled engineers, analysts, and report writers to collaborate in a shared modeling environment. This eliminated silos and improved onboarding.

“SqlDBM empowered our teams by offering a shared repository for collaboration, enabling engineers, analysts, and report writers to work more effectively.”

End-to-End Integration

Integrated seamlessly with Databricks and dbt, SqlDBM helped unify the data stack from architecture to transformation.

“SqlDBM bridged the gap between our Databricks data lake and dbt workflows, enabling seamless data transformations and advanced analytics.”

Results

In just nine months, John Holland transformed a disconnected legacy system into a centralized, governed, and modern cloud data platform. Key outcomes included:

Accelerated Migration- Visual models helped anticipate issues and streamline transition “By visualizing our data structures early, SqlDBM helped us avoid bottlenecks and stay on schedule.”
Improved Data Accuracy- Redundancies and inconsistencies were resolved before deployment “SqlDBM allowed us to uncover redundancies and improve our models’ quality, avoiding issues downstream.”
Increased Collaboration- Teams across disciplines aligned through a unified modeling approach “Collaboration across teams has never been smoother; SqlDBM provided the clarity we needed.”
Governed and Scalable Architecture- The new platform supports AI-driven use cases and audit-readiness “Our documentation in SqlDBM served as a foundation for both governance and collaboration, making our data assets accessible and reliable.”

Lessons Learned

Modeling is a Critical Success Factor “By meticulously modeling our data, we simplified a complex migration into manageable phases.”
Governance Must Be Intentional “The Data Modeling Forum ensured quality and consistency, with SqlDBM at the center of every decision.”
Stack Integration Drives Efficiency “SqlDBM served as the backbone of our tech stack, linking Databricks’ scalability with dbt’s transformation capabilities.”

Conclusion

John Holland Group’s data modernization journey illustrates the transformative power of strategic data modeling. SqlDBM delivered the blueprint for a successful Databricks migration, fostering collaboration, accelerating delivery, and enabling a future-ready data architecture.

“SqlDBM revolutionized our approach to data modeling, creating a foundation for innovation and scalability within our modern tech stack.”

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John Holland Group Accelerates Cloud Migration and Data Governance with SqlDBM

Infrastructure

Construction

Business Challenge

The project required

Solution: SqlDBM + Modern Data Stack

Data team

Australia · New Zealand · Singapore

https://johnholland.com.au/

Strategic Modeling for Cloud Readiness

Governance Through a Centralized Modeling Forum

Collaboration Across Stakeholders

End-to-End Integration

Results

Lessons Learned

Conclusion

Request Enterprise Trial

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Resources

Strategic advisors

Kent Graziano

Gordon Wong

For cloud data platforms

Try modeling now

Platform

Data Modeling

Model Governance

Snowflake Schema Monitoring

Integration

See all

Why SqlDBM?

Have questions?