#ATAGTR2023 Speaker

Welcome to the 8th Edition of the Global Testing Retreat 2023!

About Speaker

As a technocrat with over 13+ years of experience, a versatile asset to any organization. Expertise spans across automation testing for web, API, mobile, desktop, data, and RPA. Consistently led teams to deliver high-quality software products, and skills in cloud, security testing, performance testing, and DevOps makes an invaluable contributor to a wide range of projects.

In addition to QA skills, highly knowledgeable in a variety of cutting-edge technologies such as AI, ML, IoT, and blockchain, Databricks, Azure etc and constantly learning about new developments in these areas. A passionate learner and researcher, always seeking out ways to enhance skills and stay ahead of the curve.

As a leader in the space, constantly exploring new technologies and methodologies to improve software testing processes. Believe in the power of emerging technologies like AI and blockchain to transform the way we work and live, and eager to explore how these technologies can be leveraged to drive innovation and growth.

Outside of work, enjoy pushing to new limits through adventurous activities that challenge both mentally and physically. Strongly believe that maintaining a healthy body and mind is essential for success in both personal and professional life.

If you’re looking for a dynamic and versatile technology professional who is committed to lifelong learning, dedicated to delivering results, and focused on personal growth, would welcome the opportunity to connect and discuss how can contribute to your organization’s success.

Toni Ramchandani

VP – Tech Delivery Head
An Investment Research

Hands on Lab (60 mins) - Automated Testing of Databricks PySpark Notebooks inside Notebooks context

Automated Testing of Databricks PySpark Notebooks inside Notebooks context


This Abstract explores the critical issue of testing code in Databricks notebooks, which are widely used for developing Spark applications that process and deliver vital data for businesses.

And addresses the challenges faced in testing Databricks notebooks and introduces a creative “hacker” way of conducting tests directly within the notebooks.

Also discusses why running popular Python testing frameworks like pytest or unittest within Databricks notebooks is not straightforward due to their unique nature.

While the provided method offers a viable solution, conducting automated testing may be a more efficient long-term strategy.

Overall, the abstract underscores the significance of rigorous testing in Databricks notebooks and presents insights into improving the testing process for data-driven business applications.


Lab Requirements

  1. Temp Azure Databricks notebook access
  2. Python
  3. Pytest 


  1. Python Knowledge: Familiarity with Python programming is essential as the testing approach relies on Python functions and libraries.
  2. Understanding of Databricks Notebooks: You should have a basic understanding of how Databricks notebooks work, including how to create and run code cells.
Hear what Toni has to say about the hands on lab session
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