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Data Engineering Training

Live Online & Classroom Enterprise Training

Focuses on designing and maintaining scalable data pipelines. Includes ETL processes, data warehousing, and workflow automation using modern tools.

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What is Data Engineering Training about?

This course provides end-to-end training on data engineering, covering how to design, build, and manage scalable data pipelines and systems. It includes concepts in ETL/ELT, data warehousing, batch and stream processing, and data modeling, using tools like SQL, Python, Apache Spark, Airflow, and cloud platforms (AWS/GCP/Azure). 

What are the objectives of Data Engineering Training ?

  • Learn to build and optimize data pipelines 
  • Understand data storage, modeling, and ETL workflows 
  • Use tools like Spark, Kafka, and Airflow for batch and real-time processing 
  • Work with cloud data services (BigQuery, Redshift, Databricks)

Who is Data Engineering Training for?

  • Aspiring data engineers and backend developers 
  • Data analysts looking to transition into engineering roles 
  • Software engineers managing data at scale

What are the prerequisites for Data Engineering Training?

Prerequisites:    

  • Intermediate Python and SQL knowledge 
  • Understanding of databases (relational and NoSQL) 
  • Familiarity with Linux command line 
  • Basic knowledge of cloud platforms is helpful 

Learning Path:   

  • Foundations: ETL, data modeling, and pipelines 
  • Batch & stream processing with Spark and Kafka 
  • Workflow orchestration with Airflow or Prefect 
  • Deploy on cloud with AWS, GCP, or Azure 

Related Courses:   

  • Data Engineering on Google Cloud  
  • Data Engineer Nanodegree  
  • Apache Spark for Data Engineers  
  • Modern Data Engineering with AWS

Available Training Modes

Live Online Training

5 Days

Course Outline Expand All

Expand All

  • What Is a Data Engineer?
  • Data Engineering Lifecycle
  • Similar Careers to Data Engineering
  • Data Engineering Service Models
  • Data Engineer Leveling Guide
  • Technical Skills of a Data Engineer
  • What Is Data Architecture?
  • A Sample Data Architecture
  • Data Lakes, Swamps, Warehouses, and Marts
  • Modern Data Stack
  • Connecting to Data
  • The Non-optionality of Security and Privacy
  • PII
  • Principle of Least Privilege
  • Introduction
  • Course GitHub Repository
  • Setting Up the Environment
  • An Overview of Relational Databases
  • DDL, DML, DQL, DCL
  • OVER
  • CROSS JOIN
  • LATERAL JOIN
  • CROSS JOIN LATERAL
  • COALESCE
  • CASE
  • CONCAT
  • Recursive CTE
  • Stored Procedures and UDFs
  • Temp Table
  • Materialized View
  • Transactions
  • SQL Structures
  • Intro SQL statements
  • CREATE
  • ALTER
  • INSERT
  • UPDATE
  • DELETE
  • MERGE
  • DROP
  • Introduction to Data Pipelines
  • Data Pipeline Architecture
  • ETL vs. ELT
  • Designing a Data Pipeline
  • Introduction to Apache Airflow
  • Installation of Apache Airflow
  • Airflow UI
  • DAGs and Tasks
  • Airflow Architecture
  • Airflow Operators
  • Airflow Hooks
  • Introduction to the BashOperator
  • Introduction to the PythonOperator
  • Building an End-to-end Pipeline
  • Advanced Data Pipeline Concepts
  • Pipeline Failure
  • Ensuring Data Pipeline Reliability
  • Backfilling Pipelines
  • Change Data Capture
  • Data Pipelines and Data Orchestration
  • Apache Airflow
  • Relational Database Overview
  • Organizing Relational Databases
  • Relational Database Types
  • Interacting with Relational Databases: SQL
  • ACID Properties
  • Document Databases
  • Key-Value Database
  • Object Storage
  • Columnar Database
  • Graph Database
  • No-SQL Database Questions
  • Horizontal Scaling vs Vertical Scaling
  • Python
  • APIs
  • Shell Scripting
  • Cron
  • Version Control - Git - Mercurial
  • Testing
  • Docker and Containerization
  • Infrastructure Management
  • Logical Physical Data Model
  • Entity Relationship Diagrams
  • Normalization
  • Kimball and Inmon Data Warehousing

Who is the instructor for this training?

The trainer for this Data Engineering Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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