Azure Cloud Data Engineer Training In SimpleGuru

Become a Microsoft Certified Azure Data Engineer & Advance Your Career

In today’s data-driven world, organizations rely on Microsoft Azure to store, process, and analyze large-scale data efficiently. SimpleGuru offers an advanced Azure Cloud Data Engineer training program to help professionals gain expertise in Azure data solutions, data integration, real-time analytics, and big data processing.

🚀 Learn from industry experts, work on real-world projects, and get certified as an Azure Data Engineer!

Why Choose SimpleGuru for Azure Data Engineer Training?

✅ Learn from Microsoft Certified Experts
Get trained by experienced professionals with 15+ years of expertise in cloud computing and data engineering.

✅ Hands-on Learning with Real-world Projects
Work on live projects using Azure Data Factory, Synapse Analytics, Databricks, Azure SQL, and Power BI.

✅ Industry-Standard Curriculum
Our training aligns with the Microsoft Azure Data Engineer Certification (DP-203), ensuring you acquire job-ready skills.

✅ Flexible Learning Options
Choose from weekend, weekday, or fast-track batches based on your convenience.

✅ 100% Placement Assistance
Get resume-building support, mock interviews, and job referrals from our hiring partners.

Azure Cloud Data Engineer Course Overview

This Microsoft Azure Data Engineering course will help you:

✔ Understand Azure data storage, processing, and analytics services
✔ Design, build, and manage scalable data pipelines for ETL workflows
✔ Optimize Azure-based data solutions for high performance and cost efficiency
✔ Prepare for the DP-203 Microsoft Certified Azure Data Engineer Associate exam

📌 Duration: 60+ hours of live training
📌 Mode: Online / Classroom / Corporate Training
📌 Certification: Microsoft Certified – Azure Data Engineer Associate

Who Should Enroll?

This course is ideal for:
✔ Data Engineers looking to enhance their Azure skills
✔ Cloud Professionals wanting to specialize in Azure data solutions
✔ Software Engineers & Developers working on cloud-based applications
✔ Database Administrators & Analysts exploring cloud data engineering
✔ Aspiring Microsoft Certified Azure Data Engineers

Azure Data Engineer Certification Benefits

📌 High Demand for Azure Data Engineers – Organizations worldwide are adopting Azure for cloud-based data solutions, increasing demand for certified professionals.

📌 Higher Salary Potential – Certified Azure Data Engineers earn 30-40% higher salaries than non-certified professionals.

📌 Global Recognition – The Microsoft Certified Azure Data Engineer Associate certification (DP-203) is widely recognized by top companies.

📌 Opportunities in Leading Companies – Certified professionals are in demand at Microsoft, Accenture, Deloitte, TCS, Infosys, Capgemini, and more.

📌 Future-Proof Career – The Azure cloud ecosystem is growing rapidly, ensuring long-term career growth in data engineering.

Real-Time Projects You Will Work On

🔥 Project 1: Building a Data Pipeline with Azure Data Factory

• Extract, transform, and load data from various sources
• Automate workflows using Azure Data Factory & Azure SQL

🔥 Project 2: Real-time Event Processing with Azure Stream Analytics

• Process IoT sensor data streams in real-time
• Use Azure Event Hubs, Stream Analytics, and Power BI

🔥 Project 3: Big Data Processing with Azure Databricks

• Analyze structured and unstructured data
• Implement Spark-based ETL pipelines using Databricks

🔥 Project 4: AI-powered Predictive Analytics with Synapse Analytics

• Build predictive models using Azure ML & Synapse Analytics
• Visualize results with Power BI & Azure Data Explorer

Why Azure Data Engineering is a Great Career Choice?

🌍 The Cloud Data Market is Growing Rapidly!

• 80% of enterprises are moving to cloud-based data platforms.
• Microsoft Azure is the second-largest cloud provider, with a 22% market share.
• Companies need Azure Data Engineers to store, process, and analyze data efficiently.

📈 Career Growth & Salary Insights

• Entry-level Data Engineer:₹8-12 LPA
• Mid-level Data Engineer:₹12-20 LPA
• Senior Data Engineer:₹20+ LPA
• Azure Certified Data Engineers earn 30-40% more than non-certified professionals.

Introduction to Azure

• Create an Azure account
• Overview of Azure portal.
• Overview and practical implementation of below services

1. 1. Subscription
   2. Resource Group
   3. Blob Storage, Data Lake Storage
   4. Azure SQL Server, Database
   5. Azure Data Factory
   6. Azure Databricks
   7. Azure Key Vaults
   8. Azure Logic Apps
   9. GitHub Repository

Data Lake Storage(ADLS)

• Create a Storage account
• Types of Storage accounts
• Create a ADLS account
• Configure Access to ADLS
• Load data to ADLS
• Read and write Data to ADLS
• Configure Backup and Disaster Recovery

Azure Data Factory(ADF)

• Introduction to Azure Data Factory
• Azure Data Factory UI Walkthrough
• Components of Azure Data Factory
• Integration Runtime (IR)

1. 1. Azure Auto Integration Runtime
   2. Selfhosted Integration Runtime

• Create Linked Service for

1. 1. BLOB, Azure Data Lake Storage
   2. Azure SQL
   3. On-premises Server

• Create Datasets from

1. 1. CSV, Parquet, Excel, Avro, Json etc.
   2. Azure and On-premises SQL Tables

• Pipelines

1. 1. Create a new pipeline
   2. Execute other Pipelines via REST API
   3. Debug pipeline
   4. Publish Pipeline

• Activities

1. 1. Copy Data
   2. Delete, Stored Procedures
   3. Get-Meta Data, Lookup
   4. For Each, IF Condition, Switch, Until
   5. Wait, Fail, Data Flow
   6. Set Variable, Append Variable
   7. Databricks Notebook
   8. Execute Pipelines

• Triggers

1. 1. Schedule Trigger
   2. Tumbling Window Trigger
   3. Storage Events

• Transformations

1. 1. Create Dataflow, Debug Dataflow
   2. Filter, Select, Sort, Aggregate, GroupBy
   3. Join, Lookup, Exists, Union, Alterrow
   4. Rank, Pivot, UnPivot
   5. Use Flowlet to avoid reduce steps.

• Parameters

1. 1. How to use parameters to dynamically manage multiple ADLS and SQL Servers, datasets, pipelines, Triggers
   2. Use parameters while pipeline execution
   3. Create Global Parameters

• Monitor Jobs
• Expression Language usage in ADF
• Send Failure notifications using Logic Apps
• Manage credentials using Azure KeyVault
• Repository, Change Management

1. 1. Create GitHub Repository
   2. Migrate ARM templates using Git
   3. ARM Templates – Export/Import Manually

Azure Databricks

• Introduction to Spark

1. 1. Overview of Spark Architecture
   2. RDD Vs DataFlow Vs Dataset
   3. Transformations & Actions

• Introduction to DataBricks

1. 1. Create Databricks Workspace
   2. Create Clusters
   3. Create Databricks Notebooks
   4. Azure KeyVault Integration

• Databricks File System (DBFS)

1. 1. Create, Copy, Move files within DBFS
   2. Handle multiple files and folders
   3. Archive files in DBFS

• Databricks Utilities (dbUtils)

1. 1. File system
   2. Secrets
   3. Notebook
   4. Widgets

• Integrate Databricks with External resources

1. 1. Create Mount point with ADLS, storage accounts, Azure SQL, Synapse etc..
   2. Read and write data from ADLS, SQL

• Delta Lakes

1. 1. DeltaLake Overview, Architecture
   2. Diff between DataLake & DeltaLake
   3. How to create DeltaLake Tables
   4. DML operations using Delta Tables
   5. How to manage SCD Type 1 and Type 2
   6. History Logs and Restore the Tables

• Optimization

1. 1. Cost optimization Techniques overview
   2. Catalyst optimizer, Cache, Persist

PySpark Programming

• Introduction to Python
• Variables, Datatypes, Operators
• Introduction to PySpark
• Read and write data from CSV, Json, Parquet, Azure SQL, DBFS, ADLS etc.
• Transformations using PySpark

1. 1. Cast, Select, Filter, Sort, Aggregations
   2. Join, Union, Remove Duplicates
   3. Calculated Columns, Rename columns
   4. Window Functions
   5. String Functions
   6. Date Functions
   7. Conditional Statements
   8. Loops
   9. User Defined Functions
   10. Expression Language

• Run SQL queries in DataBricks using Spark SQL

Azure SQL

• Create Azure SQL Server and Database
• Configure Elastic pools
• Configure Compute resources
• Configure Access and Security
• Configure Azure SQL Connection to Data Factory and Databricks.

Azure Synapse

• Introduction to Azure Synapse

1. 1. Overview of Synapse Architecture
   2. Create Azure Synapse Account
   3. Configure access to Azure Synapse

• Overview of Pools in Synapse

1. 1. Serverless SQL Pool
   2. Dedicated SQL Pool
   3. Apache Spark Pool
   4. Data Explorer Pool

• Integration with Data Lake Storage

1. 1. Load data to ADLS via Synapse UI
   2. Query data using SQL scripts
   3. Create a External tables from CSV and parquet file in ADLS

• Connect to External Resources

1. 1. Create External File Format
   2. Create External Data source
   3. Create External Table
   4. Create Views

• Use multiple languages in Synapse notebook using magic commands
• Overview of Mssparkutils
• Introduction to Spark in Synapse

1. 1. Create a notebook in Synapse
   2. Configure Cluster, Autoscaling
   3. Create Mount point to connect ADLS, storage accounts, Azure SQL and other external databases
   4. Transformations using Synapse

• Integration with Delta Lakes

1. 1. Create Tables from Delta Tables
   2. Create views from Delta Tables

• Monitor and Logging

1. 1. Monitor the pipelines
   2. Notify the failure message using Logic Apps

• Integrate credentials using Azure Key Vault
• Use parameters to integrate multiple Pipelines, datasets, triggers, Linked service, notebooks etc.

Unity Catalog

• Unity Catalog Overview & Architecture.
• Databricks Workspace Catalog vs. Unity Catalog
• Setting Up Unity Catalog in Azure Databricks
• Managing Data Governance with Unity Catalog
• Creating & Managing Catalogs, Schemas, Tables & Views.
• Access Control and Role-Based Permissions in Unity Catalog
• Managing External Tables and Storage Credentials
• Secure Data Sharing Across Workspaces (Delta Sharing with Unity Catalog)
• Best Practices for Data Security and Compliance

Faculty Name: Anand

Experience: 15+ Years of work and teaching in Data Engineering Technologies

1. What is the Azure Data Engineer course about?
This course is designed to help learners build expertise in data integration, transformation, and data storage solutions using Microsoft Azure services like Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and more.

2. Who should take this course?
This course is ideal for aspiring data engineers, ETL developers, data architects, or professionals looking to transition into a cloud-based data engineering role.

3. Are there any prerequisites for enrolling in this course?
Basic knowledge of SQL and data concepts is recommended. Familiarity with cloud computing or any programming language (like Python) is helpful but not mandatory.

4. What tools and services will I learn in this course?
You will gain hands-on experience with Azure Data Factory, Azure Synapse Analytics, Azure Data Lake, Azure SQL Database, Azure Databricks, and more.

5. Will this course prepare me for Microsoft certification?
Yes, this course aligns with the Microsoft Certified: Azure Data Engineer Associate certification (Exam DP-203), and will help you prepare for it.

6. Is there any hands-on project or practical work included?
Yes, the course includes real-time industry-based projects and assignments to give you practical experience.

7. How long does it take to complete the course?
Typically, the course duration is 6–8 weeks, depending on your pace and the schedule of live or recorded sessions.

8. Do I get a certificate after completing the course?
Yes, upon successful completion, you will receive a course completion certificate from us. Additionally, guidance will be provided for the official Microsoft certification.

9. What are the career opportunities after this course?
You can apply for roles such as Azure Data Engineer, Data Engineer, Cloud Data Engineer, Big Data Engineer, or Data Platform Engineer.

10. Will I get placement assistance or interview preparation?
We provide resume reviews, mock interviews, and job referrals to help you land your dream data engineering role