What you'll learn:
- Python Spark PySpark industry standard coding practices - Logging, Error Handling, reading configuration, unit testing
- Building a data pipeline using Hive, Spark and PostgreSQL
- Python Spark Hadoop development using PyCharm
This course will bridge the gap between academic learning and real-world applications, preparing you for an entry-level Big Data Python Spark developer role. You will gain hands-on experience and learn industry-standard best practices for developing Python Spark applications. Covering both Windows and Mac environments, this course ensures a smooth learning experience regardless of your operating system.
You will learn Python Spark coding best practices to write clean, efficient, and maintainable code. Logging techniques will help you track application behavior and troubleshoot issues effectively, while error handling strategies will ensure your applications are robust and fault-tolerant. You will also learn how to read configurations from a properties file, making your code more adaptable and scalable. Key Modules :
Python Spark coding best practices for clean, efficient, and maintainable code using PyCharm
Implementing logging to track application behavior and troubleshoot issues
Error handling strategies to build robust and fault-tolerant applications
Reading configurations from a properties file for flexible and scalable code
Developing applications using PyCharm in both Windows and Mac environments
Setting up and using your local environment as a Hadoop Hive environment
Reading and writing data to a Postgres database using Spark
Working with Python unit testing frameworks to validate your Spark applications
Building a complete data pipeline using Hadoop, Spark, and Postgres
Prerequisites:
Basic programming skills
Basic database knowledge
Entry-level understanding of Hadoop
This course uses high-quality AI-generated text-to-speech narration to complement the powerful visuals and enhance your learning experience.
