Accelerating Data Science Workflows on Databricks with aiDE™

A leading U.S.-based residential property management company leveraged WizarD™ aiDE, Systech’s AI-powered data engineer, to modernize its Data Science workflows on Databricks. Powered by a multi-agent ecosystem, aiDE™ automated code generation, enforced enterprise governance standards, and delivered production-grade notebooks at scale. As part of this initiative, the organization needed to migrate over 50 fact and dimension notebooks built on legacy STTM, SQL, and Python code. 

Traditionally, large-scale notebook migrations require approximately three weeks of manual coding, debugging, and validation. With aiDE™ handling the heavy lifting by automating repetitive coding, refactoring legacy scripts, and ensuring consistency across 50+ notebooks, engineering teams were able to focus on architectural strategy and performance optimization. 

Beyond accelerating migration, aiDE™ eliminated accumulated technical debt by transforming legacy scripts into Databricks-native PySpark notebooks. It established a standardized, error-free delivery model and created a reliable foundation for long-term growth within the Databricks ecosystem. 

 Business Needs 

The client needed a faster, more efficient approach to modernize their Data Science workflows on Databricks. Key priorities included: 

• Accelerate Migration: Reduce manual effort in converting legacy notebooks and scripts to Databricks. 

• Improve Productivity: Enable the Data Science team to focus on analytics rather than repetitive coding tasks. 

• Ensure Timely Delivery: Keep project timelines on track without bottlenecks. 

• Enable Scalability: Develop a standardized approach for future notebook migrations and AI-assisted development. 

Key Challenges 

• High Manual Effort: Converting legacy scripts into Databricks notebooks was time-consuming, repetitive and prone to delays. 

• Complex Code Conversion: Translating Python to PySpark and reverse-engineering old scripts caused errors and slowed delivery. 

• Inconsistent Quality Risks: Manual notebook creation increased the risk of mistakes and uneven outputs. 

Solution 

Systech deployed aiDE™, an AI-powered data engineer that operates through specialized agents. It automates notebook generation, accelerates code conversion through a Translation Agent, and ensures standardized, error-free delivery through a Governance Agent. Together, these agents bring intelligence, consistency, and structure to the migration process. 

• Analyze Legacy Workloads: Review over 50 legacy notebooks and scripts written in STTM, SQL, and Python. 

• Automate Notebook Generation: Convert legacy scripts into Databricks notebooks. 

• Accelerate Code Conversion: Translate Python scripts to PySpark and update legacy notebooks. 

• Standardize and Validate Outputs: Ensure notebooks follow a consistent format for easier maintenance and fewer errors. 

Impact 

The aiDE™ implementation delivered measurable outcomes across automation, speed, and migration efficiency. 

• ~80% Automated Python-to-PySpark Translation: aiDE™ didn’t just move code; it modernized it, reducing manual refactoring and eliminating coding errors.  

• 3 Weeks to 3 Minutes: What used to take a team of engineers weeks was compressed into minutes, allowing the project to stay ahead of schedule. 

• Legacy Debt Erased: By converting legacy scripts into Databricks-native notebooks, the team secured a standardized, production-ready foundation for future AI growth. 

With aiDE™ handling the heavy lifting, the team focused on strategic priorities and scaling analytics on Databricks. 

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