Azure Data Engineering-Master 6 Real-World Projects
Azure Data Engineering-Master 6 Real-World Projects, available at $69.99, has an average rating of 3.45, with 224 lectures, based on 61 reviews, and has 763 subscribers.
You will learn about The basics of Azure data engineering and the services available in Azure for data engineers How to design, implement and manage data pipelines using Azure Data Factory, Azure SQL, Azure Storage Account, and Data Lake Storage How to create dynamic and reusable mapping data flows in Azure Data Factory How to use metadata-driven frameworks in real-time projects in Azure Data Factory How to perform incremental data loading using Azure Data Factory and watermarking techniques Techniques for validating source schema using Azure Functions and Azure SQL Six Real-world use cases and scenarios for data engineering in Azure Master Azure Data Factory Advance Configurations How to design A real-world azure data engineering solution using multiple azure services How to log and audit data pipeline details using Azure Data Factory and Azure SQL How to mount a storage account in Azure Databricks ? Real time use cases of Azure Data Factory And Other Azure data engineering services Common Azure Data Engineering Interview questions and answers How to apply Azure services to real-world data engineering projects and use cases. Best practices for logging and auditing data pipelines in Azure using Azure SQL You will learn To Design Data Engineering Solution Using Azure Databricks, Azure SQL Server How to implement incremental loading using Azure Data Factory and watermarking How To Design Data Engineering Solution Using Azure Data lake storage Gen 2 Azure Data Factory Metadata Driven Frameworks Concepts You will learn how to secure your credentials using Azure Key vault You will learn how to create and store secret token Azure Data Engineering Concepts Azure Data Factory Dynamic Pipelines Tracking Azure Data Factory Pipelines Runs Azure Data Factory Metadata Driven Framework Logging Azure Data Factory Pipeline Audit Data Using Stored Procedure Build an end to end Azure Data Engineering project using Azure Services Create And Use Azure Synapse Analytics For Big Data Processing This course is ideal for individuals who are To Gain Hands-on Real-Time Project Experience in Azure Data Engineering Services as a Data Engineer or This course is designed for data engineers, data scientists, and developers who want to take their skills to the next level. or This course is also suitable for those who are new to Azure and want to learn how to use Azure services for data engineering. or This course is also a good fit for those who are experienced in data engineering but want to learn how to apply Azure to their data engineering projects. or This course also good for professionals who are looking to take their data engineering skills to the next level by learning how to build and manage data pipelines using Azure services. or If You Are Looking For A Real World Data engineering Uses Cases, then this course is for you or Any student who is planning to learn azure data factory , azure data bricks or Azure Data Engineering or For all the database developer who wants to learn azure data engineering or For business analyst and data analyst who wants to learn azure data engineering It is particularly useful for To Gain Hands-on Real-Time Project Experience in Azure Data Engineering Services as a Data Engineer or This course is designed for data engineers, data scientists, and developers who want to take their skills to the next level. or This course is also suitable for those who are new to Azure and want to learn how to use Azure services for data engineering. or This course is also a good fit for those who are experienced in data engineering but want to learn how to apply Azure to their data engineering projects. or This course also good for professionals who are looking to take their data engineering skills to the next level by learning how to build and manage data pipelines using Azure services. or If You Are Looking For A Real World Data engineering Uses Cases, then this course is for you or Any student who is planning to learn azure data factory , azure data bricks or Azure Data Engineering or For all the database developer who wants to learn azure data engineering or For business analyst and data analyst who wants to learn azure data engineering.
Enroll now: Azure Data Engineering-Master 6 Real-World Projects
Summary
Title: Azure Data Engineering-Master 6 Real-World Projects
Price: $69.99
Average Rating: 3.45
Number of Lectures: 224
Number of Published Lectures: 134
Number of Curriculum Items: 224
Number of Published Curriculum Objects: 134
Original Price: $49.99
Quality Status: approved
Status: Live
What You Will Learn
- The basics of Azure data engineering and the services available in Azure for data engineers
- How to design, implement and manage data pipelines using Azure Data Factory, Azure SQL, Azure Storage Account, and Data Lake Storage
- How to create dynamic and reusable mapping data flows in Azure Data Factory
- How to use metadata-driven frameworks in real-time projects in Azure Data Factory
- How to perform incremental data loading using Azure Data Factory and watermarking techniques
- Techniques for validating source schema using Azure Functions and Azure SQL
- Six Real-world use cases and scenarios for data engineering in Azure
- Master Azure Data Factory Advance Configurations
- How to design A real-world azure data engineering solution using multiple azure services
- How to log and audit data pipeline details using Azure Data Factory and Azure SQL
- How to mount a storage account in Azure Databricks ?
- Real time use cases of Azure Data Factory And Other Azure data engineering services
- Common Azure Data Engineering Interview questions and answers
- How to apply Azure services to real-world data engineering projects and use cases.
- Best practices for logging and auditing data pipelines in Azure using Azure SQL
- You will learn To Design Data Engineering Solution Using Azure Databricks, Azure SQL Server
- How to implement incremental loading using Azure Data Factory and watermarking
- How To Design Data Engineering Solution Using Azure Data lake storage Gen 2
- Azure Data Factory Metadata Driven Frameworks Concepts
- You will learn how to secure your credentials using Azure Key vault
- You will learn how to create and store secret token
- Azure Data Engineering Concepts
- Azure Data Factory Dynamic Pipelines
- Tracking Azure Data Factory Pipelines Runs
- Azure Data Factory Metadata Driven Framework
- Logging Azure Data Factory Pipeline Audit Data Using Stored Procedure
- Build an end to end Azure Data Engineering project using Azure Services
- Create And Use Azure Synapse Analytics For Big Data Processing
Who Should Attend
- To Gain Hands-on Real-Time Project Experience in Azure Data Engineering Services as a Data Engineer
- This course is designed for data engineers, data scientists, and developers who want to take their skills to the next level.
- This course is also suitable for those who are new to Azure and want to learn how to use Azure services for data engineering.
- This course is also a good fit for those who are experienced in data engineering but want to learn how to apply Azure to their data engineering projects.
- This course also good for professionals who are looking to take their data engineering skills to the next level by learning how to build and manage data pipelines using Azure services.
- If You Are Looking For A Real World Data engineering Uses Cases, then this course is for you
- Any student who is planning to learn azure data factory , azure data bricks or Azure Data Engineering
- For all the database developer who wants to learn azure data engineering
- For business analyst and data analyst who wants to learn azure data engineering
Target Audiences
- To Gain Hands-on Real-Time Project Experience in Azure Data Engineering Services as a Data Engineer
- This course is designed for data engineers, data scientists, and developers who want to take their skills to the next level.
- This course is also suitable for those who are new to Azure and want to learn how to use Azure services for data engineering.
- This course is also a good fit for those who are experienced in data engineering but want to learn how to apply Azure to their data engineering projects.
- This course also good for professionals who are looking to take their data engineering skills to the next level by learning how to build and manage data pipelines using Azure services.
- If You Are Looking For A Real World Data engineering Uses Cases, then this course is for you
- Any student who is planning to learn azure data factory , azure data bricks or Azure Data Engineering
- For all the database developer who wants to learn azure data engineering
- For business analyst and data analyst who wants to learn azure data engineering
Hello,
“Learn to tackle real-world data engineering challenges with Azure by building hands-on projects in this comprehensive course. Dive into Azure’s data engineering services such as Data Factory, Azure SQL, Azure Storage Account, and Data Lake Storage to design, implement, and manage data pipelines. This course is tailored for data engineers, data scientists, and developers looking to enhance their skills and apply them in real-world scenarios.
No previous experience with Azure is required, but some background in data engineering and a general understanding of Azure will be beneficial. The course includes five practical projects that cover a range of use cases and scenarios for data engineering in Azure. By the end of this course, you will have the ability to design, construct, and manage data pipelines using Azure services.
This course, Azure for Data Engineering: Real-world Projects, focuses on five practical projects that address everyday data engineering issues using Azure technologies. With an emphasis on real-world scenarios, this course aims to provide you with the skills and knowledge to apply Azure to your own data engineering projects. Whether you are new to Azure or have some experience, this course is designed to help you take your data engineering skills to the next level.”
Is Azure good for data engineers?
Azure is a great choice for data engineers because it offers a comprehensive set of tools and services that make it easy to design, implement, and manage data pipelines. The Azure Data Factory, Azure SQL, Azure Storage Account, and Data Lake Storage are just a few of the services available to data engineers, making it easy to work with data no matter where it is stored.
One of the biggest advantages of using Azure for data engineering is the ability to easily integrate with other Azure services such as Azure Databricks, Azure Cosmos DB, and Power BI. This allows data engineers to build end-to-end solutions for data processing and analytics. Additionally, Azure provides options for data governance and security, which is a critical concern for data engineers.
In addition, Azure offers advanced features such as Azure Machine Learning and Azure Stream Analytics that can be used to optimize and scale data pipelines, allowing data engineers to quickly and easily process and analyze large amounts of data.
Overall, Azure provides a powerful and flexible platform for data engineers to work with, making it a great option for data engineering projects and real-world scenarios.
Project One:Simplifying Data Processing in Azure Cloud with Data Factory, Functions, and SQL
This course is designed for professionals and data enthusiasts who want to learn how to effectively use Azure cloud services to simplify data processing. The course covers the use of Azure Data Factory, Azure Functions, and Azure SQL to create a powerful and efficient data pipeline.
You will learn how to use Azure Data Factory to extract data from various online storage systems and then use Azure Functions to validate the data. Once the data is validated, you will learn how to use Azure SQL to store and process the data. Along the way, you will also learn best practices and case studies to help you build your own real-world projects.
This project is designed for professionals who want to learn how to use Azure Data Factory for efficient data processing in the cloud. The project covers the use of Azure functions and Azure SQL database for validation of source schema in Azure Data Factory.
The course starts with an introduction to Azure Data Factory and its features.
You will learn how to create and configure an Azure Data Factory pipeline and how to use Azure functions to validate source schema.
You will also learn how to use the Azure SQL database to store and retrieve the schema validation details.
Throughout the project, you will work on hands-on exercises and real-world scenarios to gain hands-on experience in implementing Azure Data Factory for data processing. You will learn how to use Azure functions to validate the source schema and how to use the Azure SQL database to store and retrieve the schema validation details.
By the end of this course, you will have a solid understanding of Azure Data Factory and its capabilities, and you will be able to use it to validate source schema using Azure functions and Azure SQL database. This will enable you to design and implement efficient data processing solutions in the cloud using Azure Data Factory, Azure functions, and Azure SQL database.”
This project is suitable for anyone with a basic understanding of data processing, who wants to learn how to use Azure cloud services to simplify data processing.
Project Two:Create dynamic mapping data flow in Azure data factory
In this project, you will learn how to use the powerful data flow feature in Azure Data Factory to create dynamic, flexible data pipelines. We will start by learning the basics of mapping data flows and how they differ from traditional data flows. From there, we will delve into the various components that make up a mapping data flow, including source, transformations, and sink. We will then explore how to use expressions and variables to create dynamic mappings and how to troubleshoot common issues. By the end of this course, you will have the knowledge and skills to create dynamic mapping data flows in Azure Data Factory to meet the specific needs of your organization. This course is ideal for data engineers and developers who are new to Azure Data Factory and want to learn how to build dynamic data pipelines.”
The project will cover the following topics:
-
Introduction to dynamic mapping data flow and its benefits
-
Understanding the concepts of mapping data flow and how it differs from traditional data flow
-
Hands-on exercises to create and configure dynamic mapping data flow in Azure Data Factory
-
Best practices for designing and implementing dynamic mapping data flow
-
Case studies and real-world examples of dynamic mapping data flow in action
-
Techniques for troubleshooting and optimizing dynamic mapping data flow
-
How to process multiple files with different schema.
These projects cover how you could reuse your mapping data flow, to process multiple files with different schema. It is very easy to design your mapping data flow and process files with the same schema. In this course, we will learn how you could create dynamic mapping data flow so that you could reuse your entire complicated transformations to transform your files and tables with different schema.
Project three: Real-time Project using Metadata Driven Framework in Azure Data Factory
Implement a Metadata driven framework to load multiple source tables from your source system to your Azure Storage account. In this project, we will take our azure data processing approach one step further by making ADF data pipelines metadata-driven. In a metadata-driven approach, you can process multiple tables and apply different transformations and processing tasks without redesigning your entire data flows.
This Project is designed to provide hands-on experience to the participants in implementing a real-time project using a metadata-driven framework in Azure Data Factory. The course will cover the concepts of a metadata-driven framework and its implementation in ADF. after this project, you will learn how to design and implement a metadata-driven ETL pipeline using ADF and how to use ADF’s built-in features to optimize and troubleshoot the pipeline.
By the end of the project, you will have a strong understanding of the Metadata Driven Framework in Azure Data Factory and how to use it in real-time projects. You will be able to design and implement data pipelines using the framework and will have the skills to optimize and troubleshoot them.
This project is perfect for data engineers, data architects, and anyone interested in learning more about the Metadata Driven Framework in Azure Data Factory.
Project Outline:
-
Introduction to Metadata Driven Framework in ADF
-
Setting up the Metadata Repository
-
Designing the Metadata-Driven Pipeline
-
Implementing the Metadata-Driven Pipeline
-
Optimizing and Troubleshooting the Pipeline
-
Real-time Project Implementation using Metadata Driven Framework
-
Case Studies and Best Practices
Prerequisites:
-
Basic knowledge of Azure Data Factory
-
Basic understanding of ETL concepts
-
Familiarity with SQL scripting.
Target Audience:
-
Data Engineers
-
ETL Developers
-
Data Architects
Project four: Incremental Data Loading in the Cloud: A Hands-on Approach with Azure Data Factory and Watermarking
In this project, you will learn how to implement incremental load using Azure Data Factory and a watermark table. This is a powerful technique that allows you to only load new or updated data into your destination, rather than loading the entire dataset every time. This can save a significant amount of time and resources.
You will learn how to set up a watermark table to track the last time a load was run and how to use this information in your ADF pipeline to filter out only new or updated data. You will also learn about the different types of incremental loads and when to use them. Additionally, you will learn about the benefits and best practices for using this technique in real-world scenarios. By the end of this course, you will have the knowledge and skills to implement incremental load in your own projects
This course will guide you through the process of how to efficiently load and process large amounts of data in a cost-effective and timely manner, while maintaining data integrity and consistency. The course will cover the theory and best practices of incremental loading, as well as provide hands-on experience through practical exercises and real-world scenarios. By the end of the course, you will have a solid understanding of how to implement incremental loading for multiple tables using Azure Data Factory and watermarking, and be able to apply this knowledge to your own projects
Project Five:Auditing and Logging Data Pipelines in Azure: A Hands-on Approach
In this project, you will learn how to implement a robust auditing and logging system for your Azure Data Factory pipelines using Azure SQL and stored procedures. You will gain a deep understanding of how to capture and store pipeline execution details, including start and end times, status, and error messages.
You will also learn how to use stored procedures to query and analyze your pipeline logs to identify patterns and trends. Throughout the project, you will work on real-world examples and use cases to solidify your knowledge and skills. By the end of this project, you will have the knowledge and skills needed to implement an efficient and effective auditing and logging system for your Azure Data Factory pipelines.
In this project, we will learn how to log audit details.
-
Using system variables.
-
Using the output of exciting activities.
-
Using the current item from your for each loop.
-
Using dynamic expressions.
By the end of the project, participants will have a thorough understanding of how to implement an advanced monitoring and auditing system for their Azure Data Factory pipelines and be able to analyze and troubleshoot pipeline performance issues more effectively.”
Project Six:Introductions To Azure Synapse Analytics And Azure Data Engineering
We are excited to announce the release of a new module in our Azure Data Engineering course, dedicated to explaining Azure Synapse Analytics. This module covers everything you need to know about Azure Synapse Analytics, from what it is and how to create it, to the different components and how to access data from an Azure Data Lake Gen2.
If you are not familiar with Azure Synapse Analytics, it is a limitless analytics service that brings together big data and data warehousing. It provides a unified experience for data ingestion, big data processing, and data warehousing, and allows you to query both structured and unstructured data using the same familiar tools and languages.
In our new module, we cover all the essential topics related to Azure Synapse Analytics, including:
-
What is Azure Synapse Analytics, and why should you use it?
-
How to create an Azure Synapse Analytics workspace
-
The different components of Azure Synapse Analytics, such as SQL pools, Spark pools, and Pipelines
-
How to access data from an Azure Data Lake Gen2 using Azure Synapse Analytics
We have designed this module to be easy to follow, with step-by-step instructions and real-world examples to help you understand how Azure Synapse Analytics works and how it can be used in your own projects.
By the end of this module, you will have gained a deep understanding of Azure Synapse Analytics and how it can be used to solve big data and data warehousing challenges. You will also have the skills necessary to create an Azure Synapse Analytics workspace and access data from an Azure Data Lake Gen2.
So, whether you are a data engineer, data scientist, or data analyst, our new module on Azure Synapse Analytics is the perfect way to deepen your knowledge of this powerful analytics service. Enroll in our Azure Data Engineering course today to access this new module and start learning!
Please Note: This course covers advanced topics in Azure Data Factory, and while prior knowledge of the platform is beneficial, it is not required as we will be covering all necessary details from the ground up. So, whether you’re new to Azure Data Factory or looking to expand your existing knowledge, this course has something to offer everyone
Please Note: This course comes with a 30-day money-back guarantee. If you are not satisfied with the course within 30 days of purchase, Udemy will refund your money, (Note: Udemy refund conditions are applied)
Course Curriculum
Chapter 1: Azure Data Engineering Project (Data Ingestions-IPL Data)
Lecture 1: Introductions To Azure Data Engineering Project -Process IPL Data
Lecture 2: Introductions To First Part of This Project
Lecture 3: Create Azure Account
Lecture 4: Save Raw Data In GitHub
Lecture 5: Create Azure Data Lake Storage Gen 2 Account (ADLS)
Lecture 6: How To Create Azure Data Factory Account
Lecture 7: How To Create Linked Services ADF?
Lecture 8: How To Create Data Set In ADF?
Lecture 9: How To Create A Pipeline In ADF and Configure Copy Activity?
Lecture 10: Create New Data Set and Copy Second files
Lecture 11: How To Reuse Data Set With The Help Of Parameter
Lecture 12: Use Lookup Activity To List Our Source Files
Lecture 13: Copy 16 Files Using Single Copy Activity
Lecture 14: Conclusions
Chapter 2: Azure Data Engineering Project (Data Transformations)
Lecture 1: Setting Up Azure Databricks Workspace-done
Lecture 2: Creating Our First Cluster
Lecture 3: Creating our first notebook
Lecture 4: Mount Our Azure Data lake Storage -Account Key & SAS Token
Lecture 5: Reading CSV And Appley Schema To It
Lecture 6: Reading A JSON File And Appley Schema to our data
Lecture 7: Learn Common transformations-Select
Lecture 8: Learn Common transformations-Filter
Lecture 9: Learn Common transformations GroupBy and Aggregation
Lecture 10: More videos are comings
Chapter 3: Project 2- Part 1: Mastering ADF Dynamic Pipelines
Lecture 1: Introductions To Project Requirements
Lecture 2: Understand Data and Data Transformations Requirements
Lecture 3: Design Target Table For First Data Set
Lecture 4: Create Data Set: Azure Data Lake and Azure SQL Data set
Lecture 5: Create Data Flow And Add Multiple Source ( ADLS File & Azure SQL Table)
Lecture 6: Make Our Data Flow Using Parameters
Lecture 7: How To Derive New Columns From Existing Columns And Parameters.
Lecture 8: How To Use Exist To Validate Source And Target Data
Lecture 9: Calculate New Surrogate Key And Max Surrogate Key
Lecture 10: Join Max Surrogate Key With New (Or Updated) Data Set
Lecture 11: Derive Additional Columns: Active Status ,and Current Dates
Lecture 12: Select Relevant Column Using Select Activiti-Role Based Mapping
Lecture 13: Process Updated Data Using New Branch Activity
Lecture 14: Select Proper Columns Using Role Based Mappin(Different Expression)
Lecture 15: Define Insert Set Data And Update Set Data
Lecture 16: Merge Insert And Update Data Sets
Lecture 17: Add Sink And Execute Our Pipeline
Lecture 18: Unit Testing: Validate Pipeline Execution Step 1
Lecture 19: Unit Testing : Validate Pipeline Executions Step 2
Chapter 4: Project 2-Part 2: Mastering ADF Dynamic Pipelines
Lecture 1: Introduction To New Data Set
Lecture 2: Make Our Data Set Dynamic Using Parameters
Lecture 3: Make Our Pipeline Dynamic
Lecture 4: Execute Our Pipeline With New Data Set
Chapter 5: Project 2-Part 3: Mastering ADF Dynamic Pipelines
Lecture 1: Introductions To Final Requirements
Lecture 2: Defining Table To Store Structure Of The Table
Lecture 3: How Define A Dynamic Stored Procedure To Read File Structure
Lecture 4: How To Validate File Structure of Two files
Lecture 5: How To Store Structure Details In SQL Table
Lecture 6: Validate Structure Using Azure SQL table and Stored Procedures
Lecture 7: Execute New Pipeline After validations
Lecture 8: Test All the Scenarios (Same Structure , different Structures ) End To END Unit
Chapter 6: Project 3: Introductions To Azure Databricks & Mount Azure Data Lake
Lecture 1: Create Azure Data Lake Gen 2 And Azure Databricks
Lecture 2: Register an application with Azure AD and create a service principal
Lecture 3: Assign Roles To The Application To Provide The Service Principal Permissions
Lecture 4: Add application secret to the Azure Key Vault
Lecture 5: Create a Secret Scope in Azure Databricks
Lecture 6: Create Containers ( bronze/ Raw, silver / Processed , and gold/Final)
Lecture 7: Create Your First Cluster in Databricks
Lecture 8: Create A Notebook
Lecture 9: Mount Azure Data Lake without Key Vault
Lecture 10: Read CSV file from Data Lake
Lecture 11: Mount Data lake using Azure Key Vault
Chapter 7: Project 4: Introductions To Azure Data Factory Metadata Driven Framework
Lecture 1: Introductions To Azure Data Factory Metadata Driven Framework
Lecture 2: Create Azure SQL Data Base (Source System)
Lecture 3: Create New Schema For Metadata Tables
Lecture 4: Create Linked Services To Access Source and Sink
Lecture 5: Create Source Data Set Using Linked Services
Lecture 6: Read Our Metadata From ConfigDB Using Lookup Activity
Lecture 7: Configure For Each Activity To Process Each Metadata Entries
Lecture 8: Add Copy Activity And Configure Sink Settings
Lecture 9: Configure Source Settings As Dynamic SQL Query
Lecture 10: Configure Sink To Save into Correct Folder
Lecture 11: Configure Metadata Table Load Multiple Source Tables Into Sink
Lecture 12: Configure Metadata To Load Tables With Relevant Columns and Proper Name (Rename)
Chapter 8: Project 5: Log Azure Data Factory Pipeline Executions Azure SQL&Stored Procedure
Lecture 1: Logging Audit Data Requirements
Lecture 2: Create New Metadata Table For Saving Log Details
Lecture 3: Create Stored Procedure For Saving Log Details Into A Azure SQL Table
Lecture 4: Configure Stored Procedure Activity To Log Error Details From System Variables
Lecture 5: Configure Stored Procedure Activity To Log Error Details From Activity Output
Lecture 6: Configure Stored Procedure Activity To Log Error Details Using Expressions
Lecture 7: Execute Our Pipeline And Verify Our Log Details
Chapter 9: Incremental Load: Delta Data Loading From Database By Using A Watermark Table
Lecture 1: Create New Tables And Insert Data
Lecture 2: Create Watermark Table And Insert Data
Lecture 3: Create Stored Procedure To Update Watermark Table
Lecture 4: Create Pipeline And Configure Lookup Activity: Existing WatermarkTable,Parameter
Lecture 5: Configure Lookup Activity To Get New Watermark Value
Lecture 6: Configure Copy Activity
Instructors
-
Step2C Education
100000+ Enrollments | 4+ Rated | 190+ Countries
Rating Distribution
- 1 stars: 6 votes
- 2 stars: 2 votes
- 3 stars: 6 votes
- 4 stars: 18 votes
- 5 stars: 29 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
You may also like
- Digital Marketing Foundation Course
- Google Shopping Ads Digital Marketing Course
- Multi Cloud Infrastructure for beginners
- Master Lead Generation: Grow Subscribers & Sales with Popups
- Complete Copywriting System : write to sell with ease
- Product Positioning Masterclass: Unlock Market Traction
- How to Promote Your Webinar and Get More Attendees?
- Digital Marketing Courses
- Create music with Artificial Intelligence in this new market
- Create CONVERTING UGC Content So Brands Will Pay You More
- Podcast: The top 8 ways to monetize by Podcasting
- TikTok Marketing Mastery: Learn to Grow & Go Viral
- Free Digital Marketing Basics Course in Hindi
- MailChimp Free Mailing Lists: MailChimp Email Marketing
- Automate Digital Marketing & Social Media with Generative AI
- Google Ads MasterClass – All Advanced Features
- Online Course Creator: Create & Sell Online Courses Today!
- Introduction to SEO – Basic Principles of SEO
- Affiliate Marketing For Beginners: Go From Novice To Pro
- Effective Website Planning Made Simple