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Data Engineering Courses

Data Engineering Courses Course

A comprehensive master’s program that equips learners with industry-relevant tools, cloud expertise, and hands-on data pipeline skills

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9.6/10 Highly Recommended

Data Engineering Courses on Edureka — A comprehensive master’s program that equips learners with industry-relevant tools, cloud expertise, and hands-on data pipeline skills

Pros

  • Covers both foundational and advanced tools used by professional data engineers
  • Cloud-focused curriculum with AWS, Azure, and GCP exposure
  • Includes hands-on projects, real-time processing, and ETL pipelines

Cons

  • Requires consistent commitment due to the depth of topics
  • Limited coverage of cutting-edge tech like Delta Lake or Databricks

Data Engineering Courses Course

Platform: Edureka

What will you learn in Data Engineering Courses

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  • Master the tools and technologies required to become a modern Data Engineer

  • Work with databases, data warehouses, and big data frameworks like Hadoop and Spark

  • Design ETL pipelines using Apache Airflow, Kafka, and data lakes

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  • Use cloud platforms such as AWS and Azure for scalable data storage and processing

  • Perform real-time data processing and stream analytics

  • Prepare for roles like Data Engineer, Big Data Engineer, and Cloud Data Engineer

Program Overview

Module 1: Python for Data Science

⏳ 2 weeks

  • Topics: Python basics, data structures, functions, NumPy, Pandas

  • Hands-on: Analyze data sets and perform data cleaning with Python libraries

Module 2: SQL & Data Modeling

⏳ 2 weeks

  • Topics: SQL queries, joins, subqueries, normalization, ER modeling

  • Hands-on: Design normalized databases and run analytical queries

Module 3: Big Data Hadoop & Spark

⏳ 3 weeks

  • Topics: HDFS, MapReduce, YARN, Spark RDDs, DataFrames, Spark SQL

  • Hands-on: Process large datasets and build pipelines using PySpark

Module 4: Apache Kafka

⏳ 1.5 weeks

  • Topics: Kafka architecture, producers, consumers, topics, streaming use cases

  • Hands-on: Implement real-time data pipelines using Kafka

Module 5: Data Warehousing with AWS Redshift & GCP BigQuery

⏳ 2 weeks

  • Topics: OLAP, schema design, data loading, querying, optimization

  • Hands-on: Load and analyze data using Redshift and BigQuery

Module 6: Cloud Platforms – AWS & Azure

⏳ 2 weeks

  • Topics: EC2, S3, Lambda, Glue, Azure Data Lake, Azure Data Factory

  • Hands-on: Build serverless ETL pipelines on AWS and Azure

Module 7: ETL & Data Pipelines with Airflow

⏳ 1.5 weeks

  • Topics: DAGs, task scheduling, dependency management, logging

  • Hands-on: Automate workflows and monitor ETL pipelines using Airflow

Module 8: Capstone Project

⏳ 2 weeks

  • Topics: Real-world data engineering challenges integrating multiple tools

  • Hands-on: Design, build, and deploy a full data pipeline from ingestion to visualization

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Job Outlook

  • Data Engineering is one of the fastest-growing tech careers

  • Roles like Data Engineer, Big Data Engineer, and Cloud Data Engineer are in high demand

  • Salaries range from $110,000 to $160,000+ globally depending on experience and location

  • Strong demand in industries such as finance, healthcare, e-commerce, and tech

Explore More Learning Paths

Strengthen your data engineering skills with these carefully selected courses designed to help you build, manage, and optimize data pipelines and large-scale data systems.

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Related Reading

  • What Does a Data Engineer Do? – Explore the responsibilities of data engineers and how they design, build, and maintain systems that enable data-driven decision-making.

FAQs

Can this course help me become a professional Data Engineer from scratch?
Teaches Python, SQL, and data modeling for data engineering tasks. Covers big data frameworks like Hadoop and Spark for large-scale data processing. Provides hands-on exercises with ETL pipelines, Apache Airflow, and Kafka. Includes cloud integration with AWS, Azure, and GCP for scalable data workflows. Prepares learners for roles like Data Engineer, Big Data Engineer, and Cloud Data Engineer.
Will I gain hands-on experience building ETL pipelines and real-time data processing systems?
Includes practical exercises with Apache Airflow for workflow automation. Teaches real-time data streaming and processing with Kafka. Guides learners in building end-to-end ETL pipelines from ingestion to visualization. Covers data warehousing with AWS Redshift and GCP BigQuery. Focuses on applying skills to enterprise-grade data engineering projects.
Is prior programming or cloud experience required for this course?
Designed for beginners but basic programming knowledge is helpful. Gradually introduces Python, SQL, and cloud platform fundamentals. Provides step-by-step lab exercises for hands-on practice. Encourages consistent practice to build confidence in large-scale data processing. Suitable for aspiring data professionals and career changers.
Can this course prepare me for high-demand roles in cloud and big data environments?
Focuses on enterprise-level data engineering tools and workflows. Provides skills for roles like Cloud Data Engineer, Big Data Engineer, and ETL Developer. Covers scalable data storage, processing, and pipeline deployment in cloud environments. Enhances employability in finance, healthcare, e-commerce, and tech sectors. Prepares learners to handle real-world, production-grade data challenges.
Will I learn to integrate multiple data tools and platforms for end-to-end workflows?
Covers Python, SQL, Hadoop, Spark, Kafka, Airflow, and cloud platforms. Teaches how to design complete data pipelines from ingestion to analytics. Provides hands-on capstone projects integrating multiple technologies. Focuses on real-world use cases and scalable data solutions. Prepares learners to deliver full-stack data engineering solutions.

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