MICROSOFT AZURE DATA ENGINE COURSE

Course Overview:

Our Microsoft Azure Data Engineering Course is crafted to help aspiring data engineers gain expertise in building, managing, and optimizing big data solutions on the Microsoft Azure platform. This course covers everything from data storage, processing, and security to deploying scalable data solutions in the cloud. You’ll gain hands-on experience with Azure services like Azure Data Lake, Azure Synapse Analytics, Azure Data Factory, and more, preparing you for a rewarding career as an Azure-certified data engineer.

Why Choose This Course?

  • Industry-Relevant Skills: Learn the latest Azure data engineering practices used by top organizations globally.
  • Hands-On Projects: Work on real-world data engineering projects that simulate business use cases.
  • Azure Certification Preparation: Prepares you for Microsoft’s official Azure Data Engineering certification exams.
  • Expert Instructors: Get trained by industry professionals with years of experience in cloud data engineering.

What Will You Learn?

This course is divided into several modules that comprehensively cover all aspects of data engineering on Azure.

Module 1: Introduction to Data Engineering and Azure Cloud

  1. Data Engineering Overview
    • Role of Data Engineers in Modern Data Architectures
    • Overview of Data Engineering Tools and Processes
    • Introduction to Azure Cloud Services for Data Engineering
  2. Azure Fundamentals
    • Understanding Azure Subscriptions, Resource Groups, and Regions
    • Azure Portal Navigation
    • Azure CLI, Azure PowerShell, and ARM Templates

Module 2: Azure Storage Solutions

  1. Azure Storage Overview
    • Types of Storage Solutions in Azure: Blob Storage, Azure Data Lake Storage (ADLS), Azure Files
    • Comparison of ADLS Gen1 and Gen2
  2. Azure Blob Storage
    • Introduction to Blob Storage and Use Cases
    • Working with Blob Containers, Blobs, and Access Policies
    • Managing Storage Access with SAS Tokens and Azure Active Directory (AAD)
  3. Azure Data Lake Storage (ADLS)
    • Creating and Managing Data Lakes in Azure
    • Organizing and Securing Data in ADLS
    • Working with Big Data using ADLS

Module 3: Azure SQL Database and Cosmos DB

  1. Azure SQL Database
    • Introduction to Azure SQL and Use Cases
    • Creating and Managing SQL Databases on Azure
    • SQL Database Security, Backup, and Monitoring
  2. Azure Cosmos DB
    • Introduction to Cosmos DB and Its Use Cases
    • Understanding Multi-Model Databases: Document, Key-Value, Graph, and Column-Family
    • Partitioning, Replication, and Consistency Models in Cosmos DB
    • Querying Data with SQL API, MongoDB API, Gremlin, and Cassandra API

Module 4: Azure Synapse Analytics (formerly SQL Data Warehouse)

  1. Overview of Azure Synapse Analytics
    • Understanding Synapse Architecture and Capabilities
    • Synapse Workspaces: Integrating Data Warehousing and Big Data Analytics
  2. SQL Pools in Azure Synapse
    • Setting Up Dedicated SQL Pools
    • Writing and Executing SQL Queries in Synapse
    • Data Loading, Partitioning, and Indexing in Synapse
  3. Synapse Studio
    • Overview of Synapse Studio
    • Building and Managing Data Pipelines in Synapse
    • Integrating Azure Data Lake and Synapse for Big Data Analytics

Module 5: Azure Data Factory (ADF)

  1. Introduction to Azure Data Factory
    • Overview of ETL and ELT in Azure
    • Components of Data Factory: Pipelines, Activities, Triggers, and Datasets
    • Data Ingestion with Data Factory
  2. Building Data Pipelines
    • Creating and Orchestrating Data Pipelines
    • Using Data Flows for Data Transformation
    • Integration with On-Premises Data Using Self-Hosted Integration Runtime
  3. Monitoring and Troubleshooting Pipelines
    • Monitoring Data Factory Pipelines and Activities
    • Handling Errors and Retries in ADF Pipelines

Module 6: Azure Databricks

  1. Introduction to Azure Databricks
    • Overview of Apache Spark and Azure Databricks
    • Architecture of Azure Databricks and Use Cases
    • Setting Up Databricks Workspaces and Clusters
  2. Working with Apache Spark
    • Spark DataFrames and Datasets
    • Writing Spark Jobs for Batch and Streaming Data
    • Integrating Azure Databricks with ADLS, Blob Storage, and SQL Databases
  3. Advanced Analytics with Databricks
    • Performing Machine Learning and Data Science with Databricks
    • Using MLlib for Machine Learning Pipelines
    • Visualization with Databricks Notebooks and Power BI

Module 7: Stream Analytics in Azure

  1. Introduction to Real-Time Data Processing
    • Understanding Streaming Data and Real-Time Analytics
    • Azure Stream Analytics and Use Cases
  2. Azure Stream Analytics
    • Building Real-Time Analytics Pipelines
    • Querying Streaming Data Using Stream Analytics Query Language (SAQL)
    • Integrating with Event Hub, IoT Hub, and Blob Storage
  3. Monitoring and Scaling Stream Analytics Jobs
    • Monitoring Streaming Jobs and Performance Tuning
    • Scaling Stream Analytics Jobs for High Throughput

Module 8: Azure Data Lake Analytics

  1. Overview of Azure Data Lake Analytics
    • Introduction to Data Lake Analytics and Use Cases
    • Architecture and Features of Data Lake Analytics
  2. Processing Big Data with U-SQL
    • Introduction to U-SQL Query Language
    • Writing U-SQL Jobs for Big Data Processing
    • Integrating Data Lake Analytics with ADLS and ADF

Module 9: Azure Event Hubs and IoT Hub

  1. Azure Event Hubs
    • Introduction to Event Hubs for Big Data Ingestion
    • Building Real-Time Data Ingestion Pipelines with Event Hubs
    • Integrating Event Hubs with Stream Analytics and Data Factory
  2. Azure IoT Hub
    • Introduction to IoT Hub for IoT Data Ingestion
    • Connecting IoT Devices to Azure IoT Hub
    • Processing IoT Data with Stream Analytics and Databricks

Module 10: Data Security and Governance in Azure

  1. Data Security in Azure
    • Understanding Data Encryption at Rest and In Transit
    • Role-Based Access Control (RBAC) and Managed Identities
    • Implementing Network Security Groups and Virtual Networks
  2. Azure Policy and Governance
    • Implementing Data Governance with Azure Policy
    • Using Azure Monitor and Log Analytics for Monitoring and Alerts
    • Azure Blueprints for Compliance and Governance

Module 11: Data Integration and Orchestration

  1. Integration with Power BI
    • Connecting Power BI to Azure Data Sources (ADLS, SQL, Synapse, Databricks)
    • Building Dashboards and Reports in Power BI
    • Real-Time Data Visualization Using Power BI and Azure Stream Analytics
  2. Azure Logic Apps and Functions
    • Introduction to Azure Logic Apps for Workflow Automation
    • Triggering Workflows with Azure Functions
    • Integrating Azure Functions with Data Pipelines

Module 12: Data Migration to Azure

  1. Data Migration Tools in Azure
    • Azure Database Migration Service
    • Migrating SQL Databases and NoSQL Databases to Azure
    • Best Practices for Cloud Data Migration
  2. Hybrid Data Architectures
    • Setting Up Hybrid Data Solutions with On-Premises and Azure Integration
    • Using Azure Arc for Multi-Cloud and On-Premises Data Solutions
    • Synchronizing Data Across Hybrid Environments

Module 13: Performance Optimization and Cost Management

  1. Optimizing Data Workloads in Azure
    • Tuning SQL Databases and Synapse Analytics
    • Optimizing Data Pipelines for Performance
    • Caching and Query Optimization Techniques in Azure
  2. Cost Management in Azure
    • Understanding Azure Pricing Models for Data Services
    • Using Azure Cost Management Tools to Monitor and Optimize Costs
    • Best Practices for Cost-Effective Data Solutions

Module 14: Capstone Project

  1. Building an End-to-End Data Pipeline
    • Designing and Building a Data Pipeline Using Azure Services (Data Factory, Databricks, SQL, Synapse)
    • Ingesting, Transforming, and Visualizing Data
  2. Implementing Real-Time Data Analytics
    • Creating a Real-Time Analytics Pipeline with Event Hub, Stream Analytics, and Power BI
    • Monitoring and Scaling the Pipeline
  3. Final Project Presentation
    • Code Review, Optimization, and Presentation
    • Best Practices for Data Security, Performance, and Cost Optimization

Course Highlights:

  • Duration: 3 to 5 months (depending on learning pace and mode).
  • Learning Mode: Available in both classroom and online formats.
  • Prerequisites: Basic knowledge of cloud computing and data management is recommended but not mandatory.

Who Should Take This Course?

  • Aspiring data engineers looking to build a career in cloud-based data engineering.
  • Data analysts and database administrators seeking to transition into data engineering roles.
  • IT professionals looking to expand their skills to cloud data architecture and management.

Career Opportunities:

Upon completing this course, you will be prepared for roles such as:

  • Azure Data Engineer
  • Data Architect
  • Cloud Data Engineer
  • Big Data Engineer
  • Database Engineer

Certification Path:

This course will also prepare you for the following Microsoft Azure certifications:

  • Microsoft Certified: Azure Data Engineer Associate
  • Microsoft Certified: Azure Solutions Architect Expert