ETL with Python: Tools, Scripts & Frameworks Explored

The ETL Course is specifically designed to provide a direct and concise approach to efficiently extracting, transforming, and loading data from MySQL to BigQuery. The main objective of this course is to equip learners with the necessary skills and knowledge to effectively perform ETL process in a practical manner.

Course Structure

To ensure a seamless learning experience, the course is divided into short and focused How-Tos. This allows learners to complete the course over a weekend, enabling them to be well-prepared to showcase their newly acquired skills on Monday morning. The course structure is carefully designed to facilitate quick comprehension of the concepts and their practical application.

Course Content

The course covers a comprehensive range of key areas that are essential for successful ETL processes. These areas include:

Setup

• Setting up a GCP Account: Learners will be guided through the process of creating a Google Cloud Platform (GCP) account, which is a prerequisite for performing ETL tasks.
• Credential and Authentication for security: This section emphasizes the importance of securing credentials and provides insights into authentication methods to ensure data security.
• Python Environment Setup: Learners will learn how to set up a Python environment, which is crucial for executing ETL tasks.

Extract

• Using Python to connect to MySQL: Learners will gain hands-on experience in establishing a connection between Python and MySQL, enabling effective data extraction.
• Utilizing Python's pandas for data export: This section focuses on leveraging the power of Python's pandas library to efficiently export data from MySQL.
• Python library usage for file path management: Learners will explore various Python libraries that facilitate effective file path management during the extraction process.

Transform

• Applying Python functions for data transformation: This section delves into the utilization of Python functions to transform extracted data, ensuring its compatibility with the target system.
• Utilizing Python pandas for data transformation: Learners will learn how to leverage the capabilities of Python's pandas library to perform data transformation tasks efficiently.
• Using inline SQL during the Extract phase for data transformation: This section introduces the concept of using inline SQL statements during the extraction phase to perform data transformation operations.

Load

• Harnessing the BigQuery Python library: Learners will be introduced to the BigQuery Python library and its functionalities, enabling them to seamlessly integrate with BigQuery.
• Connecting to BigQuery: This section provides step-by-step guidance on establishing a connection between Python and BigQuery, facilitating the loading of transformed data.
• Loading data into BigQuery: Learners will gain practical experience in loading transformed data into BigQuery, ensuring its availability for further analysis.
• Understanding Incremental Loads vs Truncate and Load: This section explores the differences between incremental loads and truncate and load approaches, enabling learners to make informed decisions based on specific requirements.
• Exploring other data handling options during the Load phase: Learners will be exposed to various data handling options available during the load phase, expanding their knowledge and understanding of different techniques.

By covering these key areas, the ETL Course equips learners with the necessary skills and knowledge to efficiently perform ETL processes from MySQL to BigQuery. The course's concise and practical approach ensures that learners can quickly grasp the concepts and apply them in real-world scenarios, making it an ideal choice for individuals seeking to enhance their ETL techniques.

After completing this course, you will gain proficiency in the following exciting skills:

• Employing Python to connect to MySQL
• Mastering the art of securing your database credentials to prevent exposure in your code
• Leveraging the OS module to streamline file management and minimize hard-coded elements
• Utilizing Python and the pandas library to dynamically transform data during the ETL Transformation phase
• Acquiring the expertise to effortlessly load data using GBQ's modules/libraries
• Embracing the learning journey with enthusiasm and continuous growth!

1. Business Intelligence Analysts
2. Data Analysts
3. Beginner Data Engineers
4. Beginner Software Developers
5. Data Power Users

1. Overall, great delivery and a ton of value for a 3 hour course. Worth the money for sure ~ Matthew W
2. Great introductory course! ~ Ravi B
3. Great course for a beginner. Course helps to understand ETL process using from SQL to Pandas to BigQuery ~ Amy A

1. Connect to MySQL using Python
2. Connect to BigQuery using Python
3. ETL data from MySQL to BigQuery using Python
4. Setting up their environment to use Python with MySQL and BigQuery

1. Having Python Installed, preferably using a virtual environment
2. An IDE like VS Code or PyCharm
3. GCP Account for BigQuery Access
4. Familiar with Python
5. Familiar with SQL