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

• Day 1 – Azure Cloud Fundamentals

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  Cloud computing basicsAzure global infrastructure & servicesLab: Navigate Azure Portal and create resource groups

• Day 2 – Role of a Data Engineer in Azure

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  Data engineering lifecycle overviewResponsibilities 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