Data Engineering with Python

You will learn

1. Introduction to Data Engineering:

   • Understand the role of data engineering in the data lifecycle.
   • Explore the key concepts, challenges, and best practices in data engineering.

2. Python Fundamentals for Data Engineering:

   • Refresh and expand your Python skills, emphasizing features relevant to data engineering tasks.
   • Learn about data structures, libraries (such as NumPy and Pandas), and Pythonic coding practices for efficient data manipulation.

3. Data Processing with PySpark:

   • Delve into PySpark, the Python library for Apache Spark, for distributed data processing.
   • Learn to manipulate and transform large datasets using PySpark's resilient distributed datasets (RDDs) and DataFrames.

4. Working with Big Data Technologies:

   • Explore popular big data technologies such as Hadoop and Apache Kafka, and understand how to integrate Python into these ecosystems.
   • Implement Python scripts to process and analyze data stored in distributed file systems.

5. Building ETL Pipelines:

   • Master the art of Extract, Transform, Load (ETL) processes using Python.
   • Design and implement ETL pipelines to ingest, clean, and transform data from various sources.

6. Data Modeling and Storage:

   • Explore data modeling concepts and databases commonly used in data engineering.
   • Implement Python scripts to interact with relational and NoSQL databases for effective data storage and retrieval.

7. Orchestrating Workflows with Apache Airflow:

   • Learn to use Apache Airflow to orchestrate complex data workflows.
   • Develop and schedule data pipelines efficiently using Python-based DAGs (Directed Acyclic Graphs).

8. Data Quality and Testing:

   • Understand the importance of data quality in data engineering.
   • Implement testing strategies and best practices for ensuring data accuracy and reliability.

9. Version Control and Collaboration:

   • Integrate data engineering projects with version control systems, such as Git.
   • Collaborate effectively with team members using Python and version control practices.

10. Real-world Applications and Case Studies:

    • Apply your knowledge to real-world scenarios through hands-on projects and case studies.
    • Gain insights into industry best practices and emerging trends in data engineering.

By the end of this course, participants will be equipped with the skills and tools needed to excel in the dynamic field of data engineering with Python. Whether you're a beginner looking to enter the field or an experienced data professional seeking to enhance your Python capabilities, this course provides a practical and in-depth exploration of data engineering principles and practices.

• We check your knowledge before we start the session.

• We build foundational topics first and core topics next.

• Theory classes with a real-time case study.

• Demo on every topic.

• Mock exam on every topic you understand.

• Exam Preparation

• Interview Preparation