Advanced Python Developer Training Plan¶

This six-month training plan is designed to help you become an advanced Python developer, focusing on streaming technologies and Airflow for orchestration. The plan assumes 10 hours of learning per week and combines reading, documentation, and hands-on projects.

Month 1: Advanced Python Concepts (40 hours total)¶

Goal: Master advanced Python features and best practices.

Week 1-2 (20 hours)¶

Reading:
Fluent Python by Luciano Ramalho (focus on chapters about iterators, generators, context managers, and metaprogramming).
Python's itertools and asyncio documentation.
Project:
Build a script that processes a large dataset using iterators and generators to optimize memory usage.
Add asynchronous tasks using asyncio to simulate concurrent data processing.

Week 3-4 (20 hours)¶

Reading:
Python's typing module and best practices for type hints.
Articles on Python design patterns:
Unit Testing with pytest:
- Getting Started with pytest
- Real Python: Testing Your Code with pytest
Project:
Refactor an existing project to include type hints and implement design patterns.
Write unit tests using pytest to ensure code quality:
- Write tests for each design pattern implemented in your project.
- Use pytest fixtures to set up reusable test data.
- Add parameterized tests to validate multiple scenarios.

Month 2: Streaming Technologies Basics (40 hours total)¶

Goal: Learn the fundamentals of streaming and how Python integrates with streaming platforms.

Week 1-2 (20 hours)¶

Reading:
Designing Data-Intensive Applications by Martin Kleppmann (focus on chapters about streaming systems).
Kafka's Python client or Apache Pulsar's Python client.
Project:
Set up a local Kafka or Pulsar instance.
Write a Python producer and consumer to stream and process real-time data (e.g., simulate a stock price feed).

Week 3-4 (20 hours)¶

Reading:
Articles on event-driven architectures and stream processing:
Documentation for Kafka Streams or Pulsar Functions:
- Kafka Streams Documentation
- Apache Pulsar Functions Documentation
Database for Streaming Data:
- Apache Cassandra Documentation
- Amazon DynamoDB Documentation
Project:
Build a Python application that processes streaming data and performs transformations (e.g., filtering, aggregation).
Store the processed data in a database like Apache Cassandra or Amazon DynamoDB.

Month 3: Airflow Basics (40 hours total)¶

Goal: Understand Airflow's architecture and create basic workflows.

Week 1-2 (20 hours)¶

Reading:
Apache Airflow's Quick Start Guide.
Data Pipelines with Apache Airflow by Bas P. Harenslak and Julian de Ruiter (introductory chapters).
Link to the book on Amazon
Project:
Install Airflow locally and create a simple DAG to automate a daily task, such as downloading and processing a dataset.

Week 3-4 (20 hours)¶

Reading:
Airflow's TaskFlow API.
Articles on Airflow best practices:
- 10 Best Practices for Apache Airflow (Astronomer, 2024).
- Optimizing Apache Airflow: Tips and Tricks (Medium, 2024).
- Scaling Apache Airflow for Large Workflows (Towards Data Science, 2024).
Project:
Create a DAG that integrates with external APIs or databases.
Use XComs to pass data between tasks and implement error handling.

Month 4: Advanced Streaming with Python (40 hours total)¶

Goal: Dive deeper into streaming frameworks and advanced use cases.

Week 1-2 (20 hours)¶

Reading:
Apache Flink's Python API or Spark Structured Streaming's Python API.
Project:
Build a Python application that processes streaming data using Flink or Spark.
Implement windowing and aggregation to analyze real-time data trends.

Week 3-4 (20 hours)¶

Reading:
Articles on stateful stream processing and fault tolerance:
Project:
Extend your streaming application to handle stateful processing and implement fault-tolerant mechanisms.

Month 5: Advanced Airflow (40 hours total)¶

Goal: Learn advanced Airflow features and best practices.

Week 1-2 (20 hours)¶

Reading:
Airflow's Best Practices.
Continue Data Pipelines with Apache Airflow (advanced chapters). Link to the book on Amazon
Project:
Create a DAG that orchestrates a multi-step data pipeline, including data extraction, transformation, and loading (ETL).
Add monitoring and alerting for failed tasks.

Week 3-4 (20 hours)¶

Reading:
Articles on Airflow plugins and custom operators:
- Creating Custom Operators in Apache Airflow (Astronomer, 2023).
- Extending Apache Airflow with Plugins (Medium, 2023).
- Building Custom Airflow Plugins for Your Workflows (Towards Data Science, 2023).
- Best Practices for Developing Airflow Plugins (Astronomer, 2023).
Project:
Develop a custom Airflow operator to handle a specific task in your pipeline.
Use Airflow plugins to extend functionality.

Month 6: Integration and Final Project (40 hours total)¶

Goal: Combine streaming and orchestration into a cohesive project.

Week 1-2 (20 hours)¶

Project:
Build a pipeline that streams data (e.g., from Kafka) and orchestrates processing using Airflow.
Example: Stream weather data, process it in real-time, and store results in a database using an Airflow DAG.

Week 3-4 (20 hours)¶

Documentation:
Write detailed documentation for your project, explaining the architecture and decisions made.
Polish:
Refactor and optimize your code.
Add unit tests and integration tests to ensure reliability.

Outcome¶

By dedicating 10 hours per week, you’ll: - Master advanced Python concepts and best practices. - Gain in-depth knowledge of streaming technologies like Kafka, Pulsar, Flink, or Spark. - Become proficient in Airflow for orchestration, including advanced features. - Build a portfolio of projects showcasing your expertise in Python, streaming, and orchestration.