In this live demo, Green Leaf Data Architect, Kiran, walks through three essential Delta Lake features that separate basic usage from production-ready data engineering:
- Time Travel – Debug pipelines, audit changes, and recover data with point-in-time access
- Optimize + Z-Order – Fix the small file problem and dramatically improve query performance
- Vacuum – Control storage costs while balancing data retention and recovery
This is how these features are actually used in real-world data platforms.
If you’re working in Databricks and are starting to see performance, cost, or reliability challenges, this is usually where to look first. These are small changes that have an outsized impact as your data platform scales.
If you’d like to walk through how these patterns apply to your environment, we’re happy to share what we’ve seen work across similar implementations.
Watch the full demo below!
Data Architect
Kiran Lokhande is a Data Architect with over a decade of experience designing and delivering scalable, high performance data platforms for enterprise analytics. She specializes in modern Lakehouse architectures, combining PySpark, Spark SQL, Delta Lake, and Azure technologies to build reliable, auditable, and future-ready data ecosystems. Kiran brings deep expertise in Data Vault 2.0, dimensional modeling, and end-to-end pipeline architecture, enabling organizations to transform complex raw data into trusted, business-ready insights.
At Green Leaf Consulting Group, Kiran has led the design of cloud-based Lakehouse solutions and developed robust data models that support critical insurance use cases, including claims, policies, and financial reporting. She is known for building highly optimized transformation frameworks, strong data quality controls, and governance standards that improve data consistency and transparency. With a blend of technical excellence and business understanding, Kiran consistently bridges the gap between strategy and execution, delivering scalable data solutions that drive confident decision making.
