What will you learn in Data Engineering Courses
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
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.
Related Courses
Data Engineering Foundations Specialization Course – Learn the core principles of data engineering, including data modeling, ETL processes, and database management.
Introduction to Data Engineering by IBM – Get hands-on experience with data engineering concepts, tools, and workflows used in industry projects.
DeepLearning.AI Data Engineering Professional Certificate Course – Master advanced data engineering techniques, including big data processing, cloud integration, and scalable data pipelines.
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.
Specification: Data Engineering Courses
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FAQs
- 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.
- 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.
- 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.
- 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.
- 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.
