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!

Who this course is for
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.

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