Learn Azure cloud basics, data engineering concepts, and set up your Azure environment.

• Day 1 – Azure Cloud Fundamentals

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  • Cloud computing basics
    
  • Azure global infrastructure & services
    
  • Lab: Navigate Azure Portal and create resource groups

• Day 2 – Role of a Data Engineer in Azure

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  • Data engineering lifecycle overview
    
  • Responsibilities of a data engineer
  • Azure data services (ADLS, Synapse, Databricks, ADF)

• Day 3 – Azure Account & Subscription Setup

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  • Creating a free Azure account
  • Understanding subscriptions, IAM, and cost management
  • Lab: Configure access controls and budgets

• Day 4 – Data Engineering Workflow in Azure

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  • Data pipeline stages: Ingestion → Processing → Storage → Analytics
  •  Lab: Deploy sample Azure services and link them

• Day 5 – Mini Project & Quiz

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  • Design a basic Azure data pipeline architecture
  • Hands-on assessment exercise

Explore Azure storage options, design data models, and manage structured and unstructured data.

• Day 6 – Azure Blob Storage

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  • Blob storage tiers (Hot, Cool, Archive)
  • Lab: Upload and manage files in Blob Storage

• Day 7 – Azure Data Lake Storage (ADLS Gen2)

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  • Hierarchical namespace & access permissions
  • Lab: Create and configure ADLS for analytics

• Day 8 – Azure SQL Database & Managed Instances

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  • SQL DB vs Managed Instance
  • Lab: Deploy Azure SQL and import sample datasets

• Day 9 – Cosmos DB for NoSQL Workloads

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  • Partitioning, consistency models, and scalability
  • Lab: Create a multi-region Cosmos DB instance

• Day 10 – Data Modeling for Azure

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  • Structured vs Semi-Structured vs Unstructured data
  • Schema-on-Read vs Schema-on-Write approaches

• Day 11 – Hands-on Data Loading

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  • Lab: Load raw data into ADLS and Azure SQL
  • Query data using Azure Storage Explorer

• Day 12 – Storage Solution Design Challenge

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  • Design a storage architecture for a sample use case
  • Review and feedback session

Build ETL/ELT pipelines with Azure Data Factory and integrate both batch and streaming data.

• Day 13 – ETL vs ELT in Azure

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  • Batch vs streaming ingestion patterns
  • Azure Data Factory overview

• Day 14 – Building Your First ADF Pipeline

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  • Create linked services and datasets
  • Lab: Build a pipeline to move data from Blob → Azure SQL

• Day 15 – ADF Linked Services, Datasets & Triggers

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  • Orchestrate pipelines using triggers
  • Scheduling and automation techniques

• Day 16 – Data Flows in ADF

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  • Mapping Data Flows vs Wrangling Data Flows
  • Lab: Clean and transform CSV data in ADF

• Day 17 – Event-Driven Ingestion with Event Hubs

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  • Introduction to Azure Event Hubs
  • Designing real-time ingestion patterns

• Day 18 – Streaming Data into ADLS

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  • Lab: Stream data from Event Hub to Azure Data Lake

• Day 19 – Real-Time Processing with Azure Stream Analytics

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  • Aggregating and querying streaming data
  • Lab: Create a real-time dashboard

• Day 20 – Hybrid Pipeline Mini Project

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  • Combine batch and streaming pipelines using ADF + Event Hub

Use Synapse Analytics, Databricks, and serverless tools to clean and transform data for analytics.

• Day 21 – Azure Synapse Analytics Overview

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  • Dedicated vs Serverless SQL Pools
  • Data warehousing concepts and architecture

• Day 22 – Loading & Querying Data in Synapse

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  • Perform analytical SQL queries
  • Lab: Load large datasets into Synapse

• Day 23 – Performance Tuning in Synapse

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  • Partitioning and indexing strategies
  • Materialized views for performance

• Day 24 – Introduction to Azure Databricks & Spark

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  • Basics of Apache Spark
  • Working with Databricks notebooks

• Day 25 – Data Transformation with PySpark

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  • Lab: Clean and aggregate data using PySpark in Databricks

• Day 26 – Delta Lake for ACID Transactions

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  • Implement Delta tables in Databricks
  • Benefits of Delta Lake in data pipelines

• Day 27 – Integrating Databricks with ADLS & Synapse

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  • Lab: Create pipeline from Data Lake → Databricks → Synapse

• Day 28 – Serverless Data Processing with Azure Functions

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  • Automate data transformations with serverless triggers

• Day 29 – Logic Apps for Orchestration

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  • Automate workflows connecting multiple Azure services
  • Lab: Build a Logic App for data movement

• Day 30 – End-to-End Data Flow Exercise

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  • Build a mini project: Blob → Databricks → Synapse pipeline

Secure your data pipelines, apply RBAC, monitor workloads, and optimize performance and costs.

• Day 31 – Azure RBAC & Managed Identities

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  • Role-based access control
  • Lab: Assign roles and permissions to pipelines

• Day 32 – Azure Key Vault Integration

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  • Secure secrets and keys for ADF and Synapse pipelines

• Day 33 – Network Security for Data Services

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  • Virtual Networks, Private Endpoints, and Firewalls

• Day 34 – Azure Monitoring & Log Analytics

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  • Monitor pipelines and data services
  • Configure metrics and alerts

• Day 35 – Pipeline Monitoring Hands-on

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  • Lab: Create alerting for failed ADF pipelines

• Day 36 – Cost Optimization Strategies

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  • Autoscaling, serverless cost management
  • Resource planning and budgeting

Implement an end-to-end Azure data pipeline combining all concepts and services learned.

• Day 37 – Project Planning & Architecture

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  • Define use case and data sources
  • Draw architecture diagram with selected Azure tools

• Day 38 – Setting Up Resources

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  • Provision ADLS, ADF, Event Hub, Databricks, Synapse

• Day 39 – Batch Data Ingestion

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  • Build Azure Data Factory pipelines for raw data ingestion

• Day 40 – Real-Time Data Streaming

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  • Capture live data using Event Hub and Stream Analytics

• Day 41 – Data Transformation with Databricks

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  • Clean, aggregate, and prepare data for analytics

• Day 42 – Loading into Synapse & Reporting

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  • Create analytical queries and connect to Power BI

• Day 43 – Security & Monitoring Implementation

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  • Apply RBAC, integrate Key Vault, configure monitoring

• Day 44 – Optimization & Final Testing

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  • Tune performance and costs, validate pipeline end-to-end

• Day 45 – Final Presentation & Review

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  • Present architecture, live demo, and documentation