Saturday, March 01, 2025
Date: 1st March 2025 to 8th April 2025
Time: 10 am to 5 pm
Venue: Computer Lab 1
Resource Person: Mr. Tarun Bothra
Participants: PGDM Students
Faculty Coordinator: Mr. Ganesh Pathak
Organizer: Center for Learning and Development, DYPBS
Objectives:
1. To introduce students to the concept of ETL (Extract, Transform, Load) in data management.
2. To provide hands-on training on industry-standard ETL tools.
3. To explain the importance of ETL processes in data integration and business intelligence.
4. To develop practical skills for designing and executing ETL workflows.
5. To enhance students’ ability to work with structured and unstructured data for reporting and analytics.
Workshop Overview:
A specialized training on ETL Tools was conducted by Mr. Tarun Bothra, a data integration and analytics expert. The session was designed for management students to gain an applied understanding of data flow, data transformation, and data warehousing concepts using ETL tools commonly used in the industry.
Workshop Highlights:
The workshop began with a foundational introduction to the ETL process, outlining how data is extracted from multiple sources, transformed into a structured format, and loaded into a data warehouse or analytics system. Mr. Bothra emphasized the role of ETL in enabling data-driven decision-making in modern organizations.
Participants explored key stages of ETL, including:
Extraction – techniques for collecting data from databases, cloud sources, Excel, and APIs.
Transformation – application of rules, data cleansing, formatting, joining, filtering, and aggregating.
Loading – inserting the cleaned and structured data into a target system such as a data warehouse or BI tool.
The sessions included demonstrations and exercises using popular ETL tools like Talend, Informatica, and Microsoft SQL Server Integration Services (SSIS). Mr. Bothra walked students through building data pipelines, creating data mappings, and scheduling automated ETL jobs.
Further, the workshop covered:
Data Quality and Governance: Best practices for ensuring accurate and consistent data during the ETL process.
Error Handling and Debugging: Identifying, tracking, and resolving data transformation errors.
Performance Optimization: Techniques to improve ETL process efficiency.
Integration with BI Tools: How ETL feeds dashboards and analytics platforms like Power BI and Tableau.
Real-time examples were used to demonstrate how businesses use ETL to consolidate data from multiple departments (sales, finance, marketing, HR) for unified reporting.
Interactive activities included:
ETL Design Challenges based on simulated business scenarios.
Case Studies analysing how companies like Amazon and Flipkart handle large-scale data processing.
Group Assignments to build end-to-end ETL pipelines using dummy datasets.
Q&A Rounds for clarifying conceptual and technical doubts.
Key Takeaways:
1. Clear understanding of ETL concepts and data flow processes.
2. Practical skills in designing and running ETL jobs using leading tools.
3. Awareness of data quality, governance, and error handling in ETL.
4. Knowledge of how ETL supports business intelligence and reporting.
5. Improved analytical and technical thinking for data-related business roles.
The training received excellent feedback for its relevance, practical orientation, and hands-on approach. Students felt more confident in handling real-world data challenges after participating in the workshop.