Comprehensive AWS data engineering with Python and Lambda Course Overview

Comprehensive AWS data engineering with Python and Lambda Course Overview

Comprehensive AWS Data engineering with Python and Lambda

Dive into our Comprehensive AWS Data engineering with Python and Lambda course, designed for aspiring data engineers. Over 80 hours, you'll master critical skills from Python fundamentals to Apache Spark and AWS Glue integration. Learn to handle Big Data challenges, perform complex data transformations, build efficient ETL pipelines, and leverage Serverless computing with AWS Lambda. Through hands-on labs, you’ll apply these concepts in practical scenarios, ensuring you’re job-ready. By the end, you'll have a solid foundation in Data engineering and be equipped to tackle real-world data problems using industry-standard tools and technologies.

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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.

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