Azure Data Engineering Real-Time Projects-Dp 203 Exam Prep

Step-by-step guide to building and managing cloud data pipelines-Create, clean, and transform data pipelines using Azure

What you'll learn

Connecting and extracting data from APIs using ADF

Cleaning and transforming data using PySpark in Databricks

Automating data workflows with Azure Data Factory

Loading data into Azure Synapse for analysis

Power BI reporting and dashboard creation

Requirements

Internet connection

PC/Laptop/Mobile Phone

Azure account (if students want to practice the demo)

A willingness to learn new tools and frameworks

Basic understanding of cloud computing and data processing

Some exposure to SQL and Python

Familiarity with Azure (helpful, but not mandatory)

Description

Course Description:In today's data-driven world, businesses rely heavily on robust and scalable data pipelines to handle the growing volume and complexity of their data. The ability to design and implement these pipelines is an invaluable skill for data professionals. "Azure Data Engineering Projects-Real Time Azure Data Project" is designed to provide you with hands-on experience in building end-to-end data pipelines using the powerful Azure ecosystem. This course will take you through the process of extracting, cleaning, transforming, and visualizing data, using tools like Azure Data Factory (ADF), Azure Data Lake Storage (ADLS), Azure Databricks, and Azure Synapse Analytics, with the final output delivered through Power BI dashboards.This course is perfect for anyone looking to enhance their skills in cloud-based data engineering, whether you're new to the field or seeking to solidify your expertise in Azure technologies. By the end of this course, you will not only understand the theory behind data pipelines but will also have practical knowledge of designing, developing, and deploying a fully functional data pipeline for real-world data.We will start by understanding the architecture and components of an end-to-end data pipeline. You’ll learn how to connect to APIs as data sources, load raw data into Azure Data Lake Storage (ADLS), and use Azure Data Factory to orchestrate data workflows. With hands-on exercises, you’ll perform initial data cleaning in Azure Databricks using PySpark, and then proceed to apply more complex transformations that will convert raw data into valuable insights. From there, you’ll store your processed data in Azure Synapse Analytics, ready for analysis and visualization in Power BI.We will guide you through every step, ensuring you understand the purpose of each tool, and how they work together in the Azure environment to manage the full lifecycle of data. Whether you're working with structured, semi-structured, or unstructured data, this course covers the tools and techniques necessary to manage any type of data efficiently.Course Structure Overview:The course is divided into six comprehensive sections, each focusing on a crucial stage of building data pipelines:Introduction to Data Pipelines and Azure ToolsWe’ll start with an introduction to data pipelines, focusing on their importance and use in modern data architecture. You will learn about the tools we will use throughout the course: Azure Data Factory, Azure Data Lake Storage, Azure Databricks, Azure Synapse, and Power BI. We'll also cover how these tools work together to build an efficient, scalable, and reliable data pipeline in Azure. By the end of this section, you'll have a clear understanding of how Azure facilitates large-scale data processing.Data Ingestion using Azure Data Factory (ADF)In this section, we will focus on extracting data from external sources, particularly APIs. You’ll learn how to create a pipeline in Azure Data Factory to automate the extraction and loading of data into Azure Data Lake Storage (ADLS). We will walk through the process of configuring datasets, linked services, and activities in ADF to pull in data in various formats (JSON, CSV, XML, etc.). This is the crucial first step of our pipeline and serves as the foundation for all subsequent steps.Data Storage and Management in Azure Data Lake Storage (ADLS)Once we have ingested the data, the next step is storing it efficiently in Azure Data Lake Storage (ADLS). This section will teach you how to structure and organize data in ADLS, enabling fast and easy access for further processing. We will explore best practices for partitioning data, handling different file formats, and managing access controls to ensure your data is stored securely and ready for processing.Data Cleaning and Processing with Azure Databricks (PySpark)Raw data often needs to be cleaned before it can be used for analysis. In this section, we’ll take a deep dive into Azure Databricks, using PySpark for initial data cleaning and transformation. You will learn how to remove duplicates, handle missing values, standardize data, and perform data validation. By working with Databricks, you will gain valuable hands-on experience with distributed computing, enabling you to scale your data transformations for large datasets.This section also introduces you to PySpark’s powerful capabilities for data processing, where you'll create transformations such as filtering, aggregating, and joining multiple datasets. We'll also cover the Bronze, Silver, and Gold layers of data transformation, where you’ll take raw data (Bronze) through intermediate processing (Silver) and arrive at a clean, analytics-ready dataset (Gold).Data Transformation and Loading into Azure Synapse AnalyticsAfter the data has been cleaned and transformed in Databricks, the next step is to load it into Azure Synapse Analytics for further analysis and querying. You will learn how to connect Databricks with Azure Synapse and automate the process of moving data from ADLS into Synapse. This section will also cover optimization techniques for storing data in Synapse to ensure that your queries run efficiently. We will walk you through the process of partitioning, indexing, and tuning your Synapse tables to handle large-scale datasets effectively.Course Features:This course is designed to be hands-on, with practical exercises and real-world examples. You will:Work with a real dataset, extracted from an API, cleaned, transformed, and stored in the cloud.Perform data cleaning operations using PySpark and Azure Databricks.Learn how to use ADF for automated data pipeline creation.Practice transforming data into business-ready formats.Gain experience in optimizing data storage and querying in Azure Synapse.Develop interactive reports and dashboards in Power BI.Benefits of Taking this Course:By taking this course, you will gain practical, in-demand skills in cloud-based data engineering. You’ll walk away with the knowledge and experience needed to design and implement scalable data pipelines in Azure. Whether you're a data engineer, data analyst, or a developer looking to build modern data workflows, this course provides you with the technical and strategic skills to succeed in this role.In addition to technical expertise, you will also gain insight into real-world use cases for these tools. Azure Data Factory, Databricks, and Synapse are widely used across industries to manage data workflows, from startups to enterprise-level organizations. After completing this course, you will be equipped to tackle data challenges using Azure’s robust, cloud-native solutions.This course prepares you for a career in data engineering by giving you practical experience in designing and implementing data pipelines. You’ll be able to use your new skills to build efficient, scalable systems that can handle large amounts of data, from ingestion to visualization.After completing this course, you will receive a course completion certificate, which you can download and showcase on your resume. If you encounter any technical issues throughout the course, Udemy’s support team is available to assist you. If you have any suggestions, doubts, or new course requirements, feel free to message me directly or use the Q&A section.Let’s get started on your journey to mastering data pipelines in the cloud!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Overview

Section 2: Environment Setup

Lecture 3 Create Azure Resource For Module Data Ingestions

Lecture 4 Create Azure Resource For Module Data Transformations

Section 3: Data Ingestions Using Azure Data Factory

Lecture 5 Save Raw Data Into Source Systems

Lecture 6 Create Linked Services To Access Our storage Services

Lecture 7 Copy Single File Using Azure Data Factory Pipelines And Data Set

Lecture 8 Copy Multiple Files Using Dynamic Data Set And Loops

Lecture 9 Secure Our Access To ADLS Using Azure Key Vault

Section 4: Data Transformations Using Azure DataBricks

Lecture 10 Introductions To Azure Databricks

Lecture 11 Create A Notebook

Lecture 12 Create Cluster

Lecture 13 Access ADLS From ADB Using Service Principle Without Using KV

Lecture 14 Access ADLS From ADB Using Service Principle With Key Vault

Lecture 15 Connect To New ADLS Using Same Configurations

Lecture 16 Data Transformation's Demo -Part 1

Lecture 17 Data Transformation's Demo -Part 2

Lecture 18 Data Transformation's Demo -Part 3

Lecture 19 Data Transformation's Demos -Part 4

Lecture 20 Data Clean: Process Product Details

Lecture 21 Data Clean: Process Customer Data

Lecture 22 Data Clean: Process Multiple Files Using Single Notebook

Lecture 23 Data Transformations: Create Fact And Dimensions

Section 5: Execute Azure Data Bricks From Azure Data Factory

Lecture 24 Configure Azure Data Factory To Access Azure Databricks

Lecture 25 Execute Azure Data Bricks Notebooks from ADF

Section 6: Data Loading

Lecture 26 Create Azure Synapse Workspace

Lecture 27 Access Our data using Azure Synapse

Lecture 28 Create Our First Pipeline In Synapse And Validate The Executions Details

Section 7: Azure Data Engineering DP-203 Certification Preparations

Lecture 29 Practice Test 1

Lecture 30 Azure Data Factory Related Questions And Answers

Lecture 31 Azure Storage Account Tiers Related Questions And Answers

Lecture 32 Soft Delete And point in Time Restore Q/A

Data professionals working with cloud-based tools,Students or professionals looking to showcase a real-time Azure Data Engineering project.,Data analysts wanting to expand their knowledge of cloud data pipelines.,Aspiring data engineers seeking hands-on experience with Azure tools and frameworks.,Data scientists aiming to improve their understanding of data processing in the cloud.,Database developers interested in learning how to integrate cloud technologies into their workflows.,Azure developers eager to learn how to build scalable data pipelines using Azure services.,IT professionals looking to switch or enhance their skills in data engineering.,Cloud enthusiasts who want to explore Azure data services for handling large-scale data.,Database administrators who want to manage and optimize cloud-based data storage.