Streamlining Databricks and DBT for Retirement Communities



The project aimed to optimise the data platform for our client, a leading retirement living and community services provider for elderly Australians. By leveraging Databricks, we were able to eliminate redundant layers, integrate industry best practices, and significantly improve efficiency in coding, deployment, and value delivery processes. Databricks played a pivotal role in streamlining our optimisation efforts, providing a robust platform for data processing, analysis, and deployment, ultimately enabling us to achieve our optimisation goals efficiently and effectively.

Databricks objective


  • In our journey to enhance client’s data platform, we faced significant challenges stemming from operational inefficiencies and scalability limitations within the existing architecture.
  •  These complexities prompted us to embark on a thorough redesign and optimisation strategy to address the issues at hand effectively.
Databricks Challenges

Business Challenges

  • Hard to identify data lineage.
  • No comparison of source data with current data for SCD2 implementation.
  • Enhancements take longer due to the non-standard and complex data layers
  • Same business transformations are applied in multiple places causing operational inefficiencies.
  • Row level security & column level security missing present significant vulnerabilities in data access control.
  • Surrogate keys are not reflected across all tables, making the alignment of data inconsistent and leading to inaccuracies in reporting and analysis.
Databricks Business Challenges

Strategic Approach

  • Simplification of the existing architecture by removing unnecessary components.
  • Adoption of industry-standard practices to ensure robust performance and reliability.
  • Enhancement of coding and deployment workflows for faster turnaround and value realisation.
  • Focus on scalable solutions to minimise future operational complexities and support growth.
Databricks Strategic approach


The project successfully revamped the data architecture, leading to enhanced operational efficiency, reduced complexity, and a solid foundation for scalable growth. This initiative has positioned the organisation for future expansion with a more agile and efficient data platform. Overall result achieved within 4 months of initiation of the project.

Databricks Results

Business Value Drivers for the Project

Main Benefits and Value Delivered

Efficiency Improvement

Streamlined processes resulting
in faster development and
deployment cycles.

Scalability and Growth

Established a scalable architecture
that accommodates future growth
without incremental complexity.

Best Practices Implementation

Leveraged cutting-edge industry
standards to ensure the platform’s
reliability and performance.

Operational Simplification

Reduced operational complexities,
enabling the team to focus on
innovation and value creation.

Architectural improvement of Databricks

The challenges and issues faced by the organisation’s current MVP data platform have been comprehensively detailed and analysed in the subsequent sections.

Following consultations with Databricks and the organisation’s design and architecture team, and after identifying problems with the current setup, recommendations have been made to streamline and enhance the existing architecture

The goal is structured around several key points.

  • Remove superfluous layers to simplify the system.
  • Aim for rapid scalability, reducing operational intricacies.
  • Adopt industry-standard best practices for improved performance.
  • Boost coding and deployment efficiency, ensuring quicker delivery of value.
  • Support future expansion with a streamlined approach to managing growth.
Architectural improvement of Databricks

Optimised Solution with Databricks

Optimised Solution with Databricks

Overall Solution Components

  • Integration with DBT
  • Implementation of Unity catalogue
  • Orchestration using Azure Data Factory (ADF)
  • Integration with Power BI for reporting and visualisation
  • Integration and implementation with Azure DevOps for CICD
  • Integration with multiple source systems i.e. Dynamics 365, Salesforce, ESG, Manual files

Business Outcome

Unified Analytics Platform

Integrating DBT with Databricks creates a unified platform for end-to-end analytics workflows, simplifying the data stack.


Both DBT and Databricks scale with business needs, ensuring data processing workflows can handle fluctuations in demand.

Cost-Effective Solutions

Leveraged cutting-edge industry standards to ensure the platform’s reliability and performance.

Optimised Performance

Databricks optimised environment boosts the performance of DBT transformations, resulting in faster data processing and analytics.

Streamlined Data Transformation

DBT simplifies data transformation with SQL statements, leveraging Databricks’ Spark-based processing for efficient execution.

Enhanced Collaboration and Version Control

DBT supports collaborative workflows and version control for SQL queries, enhancing teamwork among data professionals.

Automated Data Pipeline Management

DBT automates pipeline workflows, including testing and deployment, when integrated with Databricks, ensuring reliability and maintainability.


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Shahnewaz Khan

10 years of experience with BI and Analytics delivery.

Shahnewaz is a technically minded and accomplished Data management and technology leader with over 19 years’ experience in Data and Analytics.


  • Data Science
  • Strategic transformation
  • Delivery management
  • Data strategy
  • Artificial intelligence
  • Machine learning
  • Big data
  • Cloud transformation
  • Data governance. 

Highly skilled in developing and executing effective data strategies, conducting operational analysis, revamping technical systems, maintaining smooth workflow, operating model design and introducing change to organisational programmes. A proven leader with remarkable efficiency in building and leading cross-functional, cross-region teams & implementing training programmes for performance optimisation. 

Thiru Ps

Solution/ Data/ Technical / Cloud Architect

Thiru has 15+ years experience in the business intelligence community and has worked in a number of roles and environments that have positioned him to confidently speak about advancements in corporate strategy, analytics, data warehousing, and master data management. Thiru loves taking a leadership role in technology architecture always seeking to design solutions that meet operational requirements, leveraging existing operations, and innovating data integration and extraction solutions.

Thiru’s experience covers;

  • Database integration architecture
  • Big data
  • Hadoop
  • Software solutions
  • Data analysis, analytics, and quality. 
  • Global markets


In addition, Thiru is particularly equipped to handle global market shifts and technology advancements that often limit or paralyse corporations having worked in the US, Australia and India.