Azure Data Engineering Masters: Build Scalable Solutions

Master Data Engineering with Azure: From Fundamentals to Real-World Projects in Spark, SQL, and Databricks

What you'll learn

Fundamentals of Data Engineering: Understand the core concepts, roles, and responsibilities within data engineering, including data lifecycle management.

SQL Proficiency: Master both basic and advanced SQL techniques for querying, data modeling, and optimizing database performance.

Python Programming: Gain hands-on experience in Python, focusing on essential programming concepts, data manipulation, and file handling.

Databricks & PySpark Skills: Learn to use Databricks for data processing and transformations with PySpark, including building efficient ETL pipelines.

Azure Services Expertise: Explore various Azure services, including Azure Data Factory, Azure Synapse, and Azure Storage, for data integration and analytics.

Data Visualization with Power BI: Create interactive dashboards and reports using Power BI, integrating data from multiple sources and leveraging AI tools.

Real-World Project Experience: Apply learned skills in practical projects that simulate industry scenarios, enhancing problem-solving and project management abi

Requirements

Basic Understanding of Data Concepts: Familiarity with fundamental data concepts and terminology.

Basic SQL Knowledge: Introductory knowledge of SQL is helpful but not mandatory.

Familiarity with Programming: Basic experience in any programming language, preferably Python.

Interest in Data Engineering: A keen interest in data engineering and cloud technologies.

Computer with Internet Access: A reliable computer and internet connection for accessing course materials and participating in hands-on labs.

Willingness to Learn: A proactive attitude towards learning and engaging with new technologies.

Description

Embark on a transformative journey in data engineering with our comprehensive Azure Data Engineering Masters 2025 course. This program equips you with the essential skills to design, implement, and manage scalable data solutions using Microsoft Azure technologies.Curriculum Highlights:Introduction to Data Engineering: Understand core concepts, the data lifecycle, and the differences between databases, pipelines, and cloud platforms. Explore the fundamental roles of data engineering and the significance of ETL processes.Spark Core: Gain in-depth knowledge of Apache Spark, its architecture, and core functionalities. Learn about RDDs, transformations, actions, and the execution of Spark applications.Spark SQL: Dive into the capabilities of Spark SQL, its features, and use cases. Master data manipulation using DataFrames and explore integration with Hive and other data sources.Spark Streaming: Discover real-time data processing with Spark Streaming. Learn about micro-batching, structured streaming, and how to build applications that handle live data streams.Python for Data Engineering: Build a solid foundation in Python with a focus on data structures, functions, and libraries like NumPy and Pandas. Understand how to visualize data using Matplotlib and Seaborn.SQL Basic and Advanced: Master SQL from installation to advanced querying techniques, including joins, window functions, and stored procedures. Learn to connect SQL with Python for enhanced data manipulation.Azure Cloud Fundamentals: Explore Azure's cloud services, including storage solutions, data integration with Azure Data Factory, and data processing using Databricks. Understand security and monitoring in the cloud environment.Complete Databricks with PySpark: Get hands-on experience with Databricks, learning about data ingestion, orchestration, and performance optimization. Engage in practical labs and projects to solidify your understanding.Capstone Projects: Apply your learning in real-world scenarios through comprehensive projects, including ADF pipelines, Databricks implementations, and CI/CD processes.Join us to build a robust skill set in data engineering, preparing you for exciting opportunities in the rapidly evolving field of data analytics and cloud computing. Whether you're a beginner or looking to deepen your expertise, this course will empower you with the tools and knowledge to excel.

Overview

Section 1: Introduction

Lecture 1 Welcome to the course

Lecture 2 Course Resources

Lecture 3 Introduction to the Module

Lecture 4 What is Data Engineering

Lecture 5 Data Lifecycle

Lecture 6 Databases, Pipelines and Cloud Platforms

Lecture 7 Batch vs. Streaming Data

Section 2: Data Engineering Basics (PRE-REQUISITES )

Lecture 8 Introduction

Lecture 9 What is ETL

Lecture 10 ETL Tools

Lecture 11 What is Data Warehouse

Lecture 12 Benefits of Data Warehouse

Lecture 13 Data Warehouse Structure

Lecture 14 Why do we need Staging

Lecture 15 What are Data Marts

Lecture 16 Data Lake

Lecture 17 Datalake vs Data Warehouse

Lecture 18 Elements of Datalake

Section 3: Spark Core

Lecture 19 Introduction

Lecture 20 Target Audience

Lecture 21 Spark Introduction

Lecture 22 Spark Introduction Continued

Lecture 23 Why Apache Spark

Lecture 24 Spark Features

Lecture 25 Big Data Introduction

Lecture 26 Big Data continued

Lecture 27 Big Data V's

Lecture 28 Big Data Capabilities

Lecture 29 Big Data Storage

Lecture 30 Big Data Problems

Lecture 31 Big Data Solutions to the problems

Lecture 32 Amazon example on big data

Lecture 33 Amazon example on big data continued

Lecture 34 ETL pipeline

Lecture 35 ETL and how spark Fits in

Lecture 36 Apache Spark Availability

Lecture 37 Spark official documentation

Lecture 38 Hadoop Stack

Lecture 39 Tools comparison

Lecture 40 Spark Architecture

Lecture 41 Spark MR difference

Lecture 42 Spark Core

Lecture 43 Spark Core - DAG's

Lecture 44 Spark code - Shared Variables

Lecture 45 Spark code - Shared Variables continued

Lecture 46 RDD - Spark data objects

Lecture 47 Transformation & Action - RDD

Lecture 48 Directed Acyclic Graph

Lecture 49 Directed Acyclic Graph continued

Lecture 50 Spark Application Execution

Lecture 51 Spark application execution continued

Lecture 52 Spark configurations

Lecture 53 Spark Configurations - Operations

Lecture 54 Spark Configurations - Spark context and sessions

Lecture 55 Spark Configurations - Spark Versions

Lecture 56 Google Colab - Practice

Lecture 57 Spark Examples - Notebook on Colab

Lecture 58 Spark Example configurations

Lecture 59 RDD examples - parallelize method

Lecture 60 RDD examples - Spark Transformations

Lecture 61 RDD examples - Spark Transformations - Union

Lecture 62 Quick Start VM - cloudera Practice

Lecture 63 Cluster Setup

Lecture 64 Cluster setup - Storage

Lecture 65 Cluster Resources

Lecture 66 Cluster - Application Execution Modes

Lecture 67 Cluster Architecture

Lecture 68 Quick Start VM - Vendors

Lecture 69 Spark Shell

Lecture 70 Spark Installation and configs

Lecture 71 Spark shell Scala, tools

Lecture 72 Word Count example spark

Lecture 73 Word Count Example flow

Lecture 74 Word Count example execution

Lecture 75 Output - Spark Application

Lecture 76 Analysis on output

Lecture 77 Spark User Interface

Lecture 78 Persist and Unpersist

Lecture 79 Shared Variables - Broadcast

Lecture 80 Shared Variables - Accumulator

Lecture 81 Spark Core Closure

Section 4: Spark SQL

Lecture 82 Introduction

Lecture 83 Spark SQL Features

Lecture 84 Spark SQL Use Cases

Lecture 85 Spark SQL Catalyst

Lecture 86 Spark SQL Catalyst cont

Lecture 87 Spark SQL HIVE

Lecture 88 Spark SQL Pandas df

Lecture 89 Spark SQL Code

Lecture 90 Spark SQL Official Documentation

Lecture 91 Spark SQL Dataset

Lecture 92 Spark SQL Spark Session

Lecture 93 Spark SQL create df

Lecture 94 Spark SQL df operations

Lecture 95 Spark SQL operations continued

Lecture 96 Spark SQL simple sql ex

Lecture 97 Spark SQL example continued part 1

Lecture 98 Spark SQL example continued part 2

Lecture 99 Spark SQL example continued part 3

Lecture 100 Spark SQL temp table

Lecture 101 Spark SQL on cluster

Lecture 102 Spark SQL HIVE 1

Lecture 103 Spark SQL HIVE 2

Lecture 104 Spark SQL - Movies Data

Lecture 105 Spark SQL - Load ratings data

Lecture 106 Spark SQL - Most Popular Movies

Lecture 107 Spark SQL - Top Rated Movies

Lecture 108 Spark SQL - Marmite Movies

Lecture 109 Spark SQL - SQL Operations

Lecture 110 Spark SQL Project Setup

Lecture 111 Spark SQL Cluster Launch

Lecture 112 Spark SQL Closure

Section 5: Spark Streaming

Lecture 113 Introduction

Lecture 114 Spark Streaming - Understanding real time data

Lecture 115 Spark Streaming - Micro batches

Lecture 116 Spark Streaming Architecture

Lecture 117 Spark Streaming Internals

Lecture 118 Spark Streaming Netcat source example

Lecture 119 Spark Streaming Application

Lecture 120 Spark Streaming Structured

Lecture 121 Spark Streaming Structured code architecture

Lecture 122 Spark Streaming Databricks Introduction

Lecture 123 Spark Streaming Structured example

Lecture 124 Spark Streaming Structured example 2

Lecture 125 Spark Streaming Structured example 3

Lecture 126 Spark Streaming - Cluster example

Lecture 127 Spark Streaming - Cluster example 2

Lecture 128 Spark Streaming Closure

Section 6: Python for Data Engineering: Core Concepts and Applications

Lecture 129 Introduction to Python

Lecture 130 Variables and Keywords

Lecture 131 Datatypes and Operators

Lecture 132 Data Structure - Lists

Lecture 133 Data Structure - Tuples

Lecture 134 Data Structure - Dictionary

Lecture 135 Data Structure - Set

Lecture 136 Functions in Python

Lecture 137 Map, Reduce and Filter

Lecture 138 Loops and Iterations

Lecture 139 File Handling in Python

Lecture 140 Control Structures

Lecture 141 OOPs Concept in Python

Lecture 142 NumPy Library

Lecture 143 Pandas Library

Lecture 144 Data Visualization

Lecture 145 Matplotlib Library

Lecture 146 Seaborn Library

Section 7: SQL Basic and Advanced

Lecture 147 Introduction

Lecture 148 Installation of MySQL Workbench

Lecture 149 Data Architecture - File Server vs Client Server

Lecture 150 Introduction to Structured Query Language (SQL)

Lecture 151 Constraints in SQL

Lecture 152 Table Basics - DDLs

Lecture 153 Table Basics - DQLs

Lecture 154 Table Basics - DMLs

Lecture 155 Joins in SQL

Lecture 156 Data Import and Export

Lecture 157 Aggregation Functions

Lecture 158 String Functions

Lecture 159 Datetime Functions

Lecture 160 Regular Expressions

Lecture 161 Nested Queries

Lecture 162 Views in SQL

Lecture 163 Stored Procedures

Lecture 164 Windows Function

Lecture 165 SQL-Python Connectivity

Section 8: Data Engineering Fundamentals

Lecture 166 Introduction

Lecture 167 DE Fundamentals

Lecture 168 ETL vs ELT

Lecture 169 Big Data Systems

Lecture 170 Data storage and processing

Lecture 171 Big Data ecosystems

Lecture 172 File formats and git

Lecture 173 CI/CD

Section 9: Azure Cloud

Lecture 174 Introduction

Lecture 175 Pre-Requisites

Lecture 176 Cloud Computing

Lecture 177 Azure Sub, RG and ARM

Lecture 178 Azure Storage Services

Lecture 179 Data Integration using Azure Data Factory

Lecture 180 Data Processing using Spark/Databricks

Lecture 181 Batch vs Real Time Processing

Lecture 182 Security

Lecture 183 Monitoring

Section 10: Hive

Lecture 184 Intro

Lecture 185 Hive and Evolution

Lecture 186 Hive Architecture

Lecture 187 Hive Meta and Tables

Lecture 188 Hive Data types and Tools

Section 11: Complete Databricks with PySpark

Lecture 189 Introduction

Lecture 190 Pre-Requisites

Lecture 191 What is Databricks

Lecture 192 Data Engineering with Apache Spark

Lecture 193 Delta Lake & Data Lakehouse

Lecture 194 Data Ingestion

Lecture 195 Data Orchestration

Lecture 196 Performance Tuning and Optimization

Lecture 197 Security and Governance

Lecture 198 Databricks Pracaticals #1

Lecture 199 Databricks Lab - Notebook 1

Lecture 200 Databricks Lab - Notebook 2

Lecture 201 Pipelines Lab

Lecture 202 SQL Lab

Lecture 203 Repos & Streaming Lab

Section 12: Azure Cloud Labs

Lecture 204 Azure Cloud Setup

Lecture 205 ADF Overview

Lecture 206 Azure Databricks Overview - 1

Lecture 207 Azure Databricks Overview - 2

Lecture 208 Data Integration - ADF

Lecture 209 Data Processing - Azure Databricks

Section 13: Projects

Lecture 210 Introduction

Lecture 211 ADF Pipeline

Lecture 212 Project - Databricks

Lecture 213 Project - CI/CD #1

Lecture 214 Project - CI/CD #2

Aspiring Data Engineers: Individuals looking to start a career in data engineering and analytics.,IT Professionals: Current IT professionals seeking to upskill and transition into data engineering roles.,Data Analysts: Data analysts who want to deepen their technical skills and expand their knowledge of data engineering.,Students in Related Fields: University students studying computer science, information technology, or data science.,Business Analysts: Professionals interested in leveraging data engineering to enhance business insights and decision-making.,Career Changers: Individuals from non-technical backgrounds who are motivated to enter the data engineering field.,Anyone Interested in Azure Solutions: Those looking to understand and utilize Azure as a cloud service provider for data solutions.