Data Engineering and Warehousing

Design and manage scalable data pipelines, ETL processes, and data warehouse solutions.

⏱️ 8-10 Weeks
📊 Intermediate
🗃️ SQL 🔥 Apache Spark ☁️ AWS 🏭 Azure Data Factory
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⚙️
📊
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Master Data Engineering

Our comprehensive curriculum covers all essential aspects of modern data engineering and warehousing

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Scalable Data Pipelines

Design and build robust, scalable data pipelines that can handle terabytes of data efficiently.

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ETL/ELT Processes

Master Extract, Transform, Load processes and modern ELT approaches for data integration.

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Data Warehousing

Design and implement data warehouse solutions using modern cloud technologies.

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Cloud Platforms

Work with AWS and Azure services to build enterprise-grade data solutions.

Course Curriculum

A comprehensive learning path from data fundamentals to advanced engineering techniques

Module 1: Data Engineering Fundamentals

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  • Introduction to Data Engineering
  • Data Architecture and Design Patterns
  • Data Modeling Techniques
  • SQL Mastery for Data Engineers
  • Data Quality and Governance
  • Data Security Best Practices
SQL Data Modeling Architecture

Learning Outcomes

By the end of this module, you'll understand core data engineering concepts, be proficient in advanced SQL, and know how to design effective data architectures.

Module 2: Big Data Processing with Apache Spark

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  • Introduction to Big Data and Spark
  • Spark Architecture and RDDs
  • DataFrames and Spark SQL
  • Spark Streaming for Real-time Data
  • Performance Tuning and Optimization
  • Spark on Cloud Platforms
Apache Spark PySpark Big Data

Learning Outcomes

You'll master Apache Spark for processing large-scale data, build streaming applications, and optimize Spark jobs for performance.

Module 3: Cloud Data Engineering with AWS

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  • AWS Data Services Overview
  • Amazon S3 for Data Storage
  • Data Warehousing with Redshift
  • ETL with AWS Glue
  • Real-time Processing with Kinesis
  • Orchestration with Step Functions
AWS Redshift S3 Glue

Learning Outcomes

You'll build end-to-end data solutions on AWS, implement data warehousing with Redshift, and create serverless ETL pipelines.

Module 4: Azure Data Factory and Data Warehousing

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  • Azure Data Services Ecosystem
  • Building Pipelines with Azure Data Factory
  • Data Warehousing with Azure Synapse Analytics
  • Data Integration Patterns
  • Monitoring and Management
  • Cost Optimization Strategies
Azure Data Factory Synapse

Learning Outcomes

You'll master Azure Data Factory for building data pipelines, implement data warehousing solutions with Synapse, and optimize Azure data services.

Module 5: Advanced Data Engineering & Capstone Project

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  • DataOps and MLOps Practices
  • Data Pipeline Monitoring and Alerting
  • Infrastructure as Code for Data Platforms
  • Real-world Capstone Project
  • Performance Optimization Techniques
  • Career Preparation and Interview Skills
Terraform DataOps Monitoring Capstone

Learning Outcomes

You'll complete a comprehensive capstone project, implement DataOps practices, and be prepared for data engineering roles in the industry.

Start Building Scalable Data Solutions Today

Join thousands of data professionals who have transformed their careers with our comprehensive data engineering curriculum.

Enroll in the Course