CASE STUDY | secure waste infrastructure
Secure Waste
Modernizing Data & Analytics Through an Architecture Review & Databricks MVP
INDUSTRY
Energy
SOLUTION AREA
Data Strategy
Modern Data Architecture
Use Case Analysis
Databricks
MS Fabric & PowerBI
Unity Catalog
TECHNOLOGY
The Challenge
Secure’s leadership team sought an unbiased data strategy and architecture partner to guide their modernization journey. They needed to assess their current state and establish a future-ready platform that could enable BI, AI/ML, orchestration, governance, and multi-cloud capabilities.
Key challenges included rising platform costs and inefficiencies in platform upgrades and maintenance, a fragmented data ecosystem across BI, data infrastructure and data science without a unified architecture or governance, and the lack of a future state architecture and roadmap that would seamlessly support existing and future use cases and goals for scaling.
The Data Elephant Difference
Data Elephant was engaged to conduct a data capability and architecture assessment and deliver a prioritized roadmap, followed by a Databricks MVP build. The engagement balanced strategy with implementation planning, ensuring immediate value while enabling long-term scalability.
Our approach emphasized cloud-native best practices, strict security, and predictable automation through Infrastructure as Code (IaC). Data Elephant was selected due to our local presence, energy expertise, and experience across platforms being leveraged today and considered for the future including AWS, Azure native services, and Databricks.
Evaluated architecture options across Azure (Fabric), AWS, Snowflake, and Databricks.
Identified opportunities for BI, AI/ML, orchestration, multi-cloud, and governance improvements
Recommended an Azure Databricks Lakehouse Architecture leveraging UC for governance, Azure Data Factory and Fabric to support Power BI integration.
Delivered a Databricks Sales Analytics MVP, migrating existing sales model from SQL to Databricks through a co-creation model for knowledge transfer.
The Outcome
The migration delivered immediate and long-term value for Appreciation Engine, transforming both their data platform and their operational model.
Results at a Glance
FUTURE STATE
Designed a modern, scalable data architecture that supports BI, ML, GenAI, and governance needs
VISIBILITY & TRUST
End-to-end data lineage through Unity Catalog and Collibra
UNBIASED APPROACH
Delivered a comparative assessment of Azure, AWS, Snowflake, and Databricks to support an informed, strategic decision.
COST & SCALE
Provided a path to reduce platform costs while ensuring scalability for the next five years of data growth.
