Admissions Open for JANUARY Batch
Master building, automating, and managing ETL pipelines to efficiently transform and load data for real-world applications.
Days : Tue & Thu
Duration : 12 Hours
Timings: 10 AM - 12 PM IST
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Building ETL Pipelines
Learn ETL process to design robust data pipelines, extract from diverse sources, transform, load, and manage errors, logging, scheduling.
Online Live Instructor-Led Learning
12 Hours
10 AM - 12 PM
Sat & Sun
By end of this course
Get stronger in
Develop skills in data extraction from various sources (files, databases) and advanced data transformation techniques
Master data loading strategies, implement robust error handling, logging, and build reusable ETL code.
Get familier with
Learn about APIs, how to make API calls with Python's requests library, and parse JSON responses.
Understand how to implement data quality checks, including validation rules for required fields, data ranges, and formats.
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners ready to advance their data engineering skills through hands-on ETL pipeline projects
Prerequisites
Strong understanding of Python for data tasks and SQL for database interaction, as covered in Module1.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Strong understanding of Python for data tasks and SQL for database interaction, as covered in Module 1.
Who Should Enroll?
This course is for learners ready to advance their data engineering skills through hands-on ETL pipeline projects
By the end of this course
Get Stronger in
Understand core ETL concepts, real-world use cases, and the data engineer’s role in pipeline construction.
Develop skills in data extraction from various sources (files, databases) and advanced data transformation techniques.
Master data loading strategies, implement robust error handling, logging, and build reusable ETL code.
Get Familiar in
Learn about APIs, how to make API calls with Python’s requests library, and parse JSON responses.
Understand how to implement data quality checks, including validation rules for required fields, data ranges, and formats.
Course Contents
Content
Understand Extract, Transform, Load concept. Learn real-world examples of ETL use cases. Explore the role of data engineers in building pipelines. Understand batch vs real time processing.
Content
Extract data from multiple CSV files. Read Excel files using pandas. Parse JSON and XML data. Handle different file encodings and delimiters.
Content
Learn what APIs are and why they’re important. Make API calls using the requests library. Handle API authentication with keys. Parse JSON responses and convert to Data Frames.
Topics
Connect to different databases (PostgreSQL, MySQL). Extract data using SQL queries. Implement incremental extraction using timestamps. Handle large datasets with chunking.
Content
Clean data: remove duplicates, handle nulls, fix data types. Rename columns to standard formats. Filter unwanted records. Validate data quality.
Content
Merge data from multiple sources. Create calculated columns. Apply business logic transformations. Aggregate and summarize data.
Content
Load data into databases using Pandas. Implement “append” vs “replace” strategies. Handle duplicate records during loading. Create database tables from DataFrames.
Content
Implement try -except blocks in your pipelines. Create meaningful error messages. Use Python’s logging module to track pipeline execution. Save logs to files for debugging.
Content
Organize code into functions and modules. Create config files for connection settings. Use environment variables for credentials. Build a reusable ETL template
Content
Learn about cron jobs and task schedulers. Use Python’s schedule library for basic automation. Understand why scheduling matters in data engineering.
Content
Implement validation rules in pipelines. Check for required fields, data ranges, and formats. Create data quality reports. Handle data quality failures gracefully.
Content
Build a complete ETL pipeline: Extract data from an API, transform it (clean, merge, aggregate), load into PostgreSQL, add error handling and logging, schedule to run daily.
Content
Understand Extract, Transform, Load concept. Learn real-world examples of ETL use cases. Explore the role of data engineers in building pipelines. Understand batch vs real time processing.
Content
Extract data from multiple
CSV files. Read Excel files
using pandas. Parse JSON
and XML data. Handle
different file encodings and
delimiters
Content
Learn what APIs are and why
they’re important. Make API
calls using the requests
library. Handle API
authentication with keys.
Parse JSON responses and
convert to DataFrames.
Topics
Connect to different databases (PostgreSQL, MySQL). Extract data using SQL queries. Implement incremental extraction using timestamps. Handle large datasets with chunking.
Content
Clean data: remove duplicates, handle nulls, fix data types. Rename columns to standard formats. Filter unwanted records. Validate data quality.
Content
Merge data from multiple sources. Create calculated columns. Apply business logic transformations. Aggregate and summarize data.
Content
Load data into databases using Pandas. Implement “append” vs “replace” strategies. Handle duplicate records during loading. Create database tables from DataFrames.
Content
Implement try -except blocks in your pipelines. Create meaningful error messages. Use Python’s logging module to track pipeline execution. Save logs to files for debugging.
Content
Organize code into functions and modules. Create config files for connection settings. Use environment variables for credentials. Build a reusable ETL template
Content
Learn about cron jobs and task schedulers. Use Python’s schedule library for basic automation. Understand why scheduling matters in data engineering.
Content
Implement validation rules in pipelines. Check for required fields, data ranges, and formats. Create data quality reports. Handle data quality failures gracefully.
Content
Build a complete ETL pipeline: Extract data from an API, transform it (clean, merge, aggregate), load into PostgreSQL, add error handling and logging, schedule to run daily.
What is covered: Vectors, matrices, operations like addition and multiplication.
Application: Data representation, image processing, neural networks.
Example:
1. Image as Matrix: A grayscale image is a matrix of pixel values. Neural networks process these matrices to recognize objects.
2. Matrix Multiplication: Used to combine weights and inputs in every layer of a neural network.
What is covered: Studying change (derivatives), finding minimum/maximum values.
Application: Training models by minimizing error (loss), adjusting weights.
Example:
1. Gradient Descent: The process of finding the best model parameters by moving in the direction that reduces error, like rolling a ball downhill.
2. Backpropagation: Calculating how much each weight in a neural network should change to improve predictions.
What is covered: Measuring uncertainty, analyzing data, making predictions.
Application: Predicting outcomes, evaluating models, handling randomness.
Example:
1. Spam Detection: Using probability to decide if an email is spam based on words it contains.
2. Model Evaluation: Calculating accuracy, precision, and recall to see how well a model performs.
What is covered: Logic, graphs, counting, combinations.
Application: Social networks, recommendation systems, logical reasoning.
Example:
1.Friend Recommendations: Using graph theory to suggest new friends on social media.
2.Counting Possibilities: Calculating how many ways a password can be formed.
This section includes a comprehensive evaluation covering all course topics, designed to measure understanding and mastery of key mathematical concepts presented throughout the course
Phase 1: From Coders to Creators
You’ll set up your professional coding environment by installing VS Code and Jupyter, introduce ChatGPT as a coding co-pilot, and learn to build effective prompts to generate code, establishing a productivity mindset for modern development.
Learn to reframe coding as building blocks for real applications by working with CSV, JSON, and image datasets from relatable domains like YouTube, food, and books, developing a system-level thinking approach.
Master abstraction, reusability, and clarity in logic by breaking down real-world use cases like meal planners and birthday reminders into modular code components using functions, loops, and conditions.
Build a functional CLI project such as a task tracker or GPA calculator, solving real-world problems like smart schedulers or basic calculators while developing ownership and confidence in your coding abilities