Comprehensive AWS data engineering with Python and Lambda Course Overview

Prerequisites

To successfully undertake the Comprehensive AWS Data Engineering with Python and Lambda course, we recommend that students have the following minimum prerequisites:

• Basic Programming Knowledge: Familiarity with Python programming, including understanding data types, variables, control flow, and basic data structures such as lists, tuples, and dictionaries.
• Understanding of Data Processing Concepts: Basic knowledge of data processing, ETL (Extract, Transform, Load) processes, and handling different file formats like CSV, JSON, and Parquet.
• Fundamentals of Cloud Computing: A general understanding of cloud computing concepts and AWS services, including S3 storage and basic AWS console navigation.
• Basic SQL Knowledge: Ability to write simple SQL queries for data manipulation and retrieval.
• Basic Understanding of Distributed Systems (Optional): While not mandatory, having a basic idea of distributed computing frameworks such as Apache Spark will be beneficial.

These prerequisites are designed to ensure that all students have a foundational understanding, enabling them to get the most out of the training and successfully complete the course.

Target Audience for Comprehensive AWS data engineering with Python and Lambda

Introduction:

The Comprehensive AWS Data Engineering with Python and Lambda course is designed for IT professionals seeking to build robust data engineering solutions using Python, PySpark, AWS Glue, and Lambda.

Target Audience and Job Roles:

• Data Engineers

  
  

• Cloud Solutions Architects

  
  

• Big Data Engineers

  
  

• Data Analysts

  
  

• Machine Learning Engineers

  
  

• Software Developers

  
  

• IT Project Managers

  
  

• System Administrators

  
  

• ETL Developers

  
  

• DevOps Engineers

  
  

• Business Intelligence (BI) Developers

  
  

• Database Administrators (DBAs)

  
  

• Technical Leads/Senior Software Engineers

  
  

• Data Science Enthusiasts

  
  

Learning Objectives - What you will Learn in this Comprehensive AWS data engineering with Python and Lambda?

Course Introduction

The Comprehensive AWS Data Engineering with Python and Lambda course is designed to equip students with essential skills in data engineering, utilizing Python, PySpark, AWS Glue, and AWS Lambda. It covers fundamental and advanced topics to prepare students for real-world data engineering challenges.

Learning Objectives and Outcomes

• Understand the principles and challenges of data engineering.
• Master Python programming basics and advanced data manipulation using PySpark.
• Explore Apache Spark for data processing and analysis.
• Efficiently handle data loading, cleaning, and transformation tasks.
• Perform data aggregation, joining, and combining operations with PySpark.
• Implement real-time data processing and streaming with PySpark.
• Deploy PySpark applications on cloud platforms like AWS EMR and Databricks.
• Migrate and build ETL pipelines using AWS Glue, including integration with other AWS services.
• Develop serverless data processing functions with AWS Lambda.
• Apply best practices and optimize performance for AWS Glue and Lambda.