Azure Data Engineering Training in Hyderabad with Placement

Before learning data engineering tools, students understand the basics of cloud computing and the Microsoft Azure platform.

Topics Covered:

• Introduction to Cloud Computing
• Public vs Private vs Hybrid Cloud
• Microsoft Azure Architecture Overview
• Azure Resource Manager (ARM)
• Azure Portal Navigation
• Azure Subscriptions and Resource Groups
• Identity Management with Azure Active Directory
• Role-Based Access Control (RBAC)

Outcome:
Students understand Azure platform architecture and how resources are managed in cloud environments.

This module builds the foundation of modern data engineering concepts used in cloud platforms.

Topics Covered:

• What is Data Engineering
• Data Warehousing Concepts
• ETL vs ELT Pipelines
• Structured vs Semi-Structured vs Unstructured Data
• Data Lakes vs Data Warehouses
• Modern Data Architecture
• Data Pipeline Design Concepts
• Batch Processing vs Streaming Data

Outcome:
Students understand how modern organizations design scalable data platforms.

Azure provides multiple storage services for handling large volumes of data.

Topics Covered:

• Azure Storage Account Overview
• Azure Blob Storage
• Azure Data Lake Storage Gen2
• Storage Tiers and Lifecycle Management
• Access Control and Security
• Data Encryption and Security Best Practices
• Data Partitioning Strategies

Outcome:
Students learn how to store and manage large datasets efficiently using Azure storage solutions.

Azure Data Factory is the core service used to build ETL and data integration pipelines.

Topics Covered:

• Introduction to Azure Data Factory
• Data Integration Concepts
• Pipelines and Activities
• Linked Services and Datasets
• Copy Activity for Data Movement
• Data Transformation Techniques
• Triggering Pipelines
• Monitoring Pipelines
• Incremental Data Loads
• Integration Runtime Concepts

Outcome:
Students build automated data pipelines connecting multiple data sources.

Azure Databricks is used for large-scale data processing and analytics.

Topics Covered:

• Introduction to Azure Databricks
• Apache Spark Architecture
• Databricks Workspace Overview
• Spark Clusters and Jobs
• Data Processing with PySpark
• DataFrames and Transformations
• Spark SQL Queries
• Delta Lake Architecture
• Optimizing Spark Jobs

Outcome:
Students process large datasets using distributed computing frameworks.

Azure Synapse combines data warehousing and big data analytics.

Topics Covered:

• Introduction to Azure Synapse Analytics
• Synapse Architecture
• Dedicated SQL Pools
• Serverless SQL Pools
• Data Warehousing Concepts
• Query Optimization Techniques
• Data Integration with Synapse Pipelines
• Monitoring and Performance Tuning

Outcome:
Students build scalable data warehouse solutions.

This module focuses on building efficient and scalable ETL workflows.

Topics Covered:

• Data Cleaning and Transformation
• Data Validation Techniques
• Pipeline Orchestration
• Data Quality Management
• Data Partitioning
• Performance Optimization

Outcome:
Students design enterprise-grade data transformation pipelines.

Modern applications require real-time data streaming and processing.

Topics Covered:

• Introduction to Real-Time Data Processing
• Azure Event Hub
• Azure Stream Analytics
• Streaming Data Pipelines
• Processing IoT Data Streams

Outcome:
Students learn how to process streaming data in real time.

Security and compliance are critical in enterprise data platforms.

Topics Covered:

• Data Security in Azure
• Encryption and Data Protection
• Role-Based Access Control
• Data Governance Concepts
• Azure Purview Overview
• Data Compliance Best Practices

Outcome:
Students learn to build secure and compliant data pipelines.

This module focuses on maintaining efficient data pipelines.

Topics Covered:

• Monitoring Azure Data Pipelines
• Azure Monitor
• Logging and Alerts
• Performance Tuning
• Cost Optimization Strategies

Outcome:
Students learn to manage production-level data engineering systems.

Students work on real-world projects that simulate enterprise data environments.

Projects Include:

Project 1: Data Pipeline Automation
Build ETL pipelines using Azure Data Factory.

Project 2: Big Data Processing
Process large datasets using Azure Databricks and Spark.

Project 3: Data Warehouse Implementation
Design a data warehouse using Azure Synapse Analytics.

Project 4: Real-Time Streaming Pipeline
Process streaming data using Azure Event Hub and Stream Analytics.

Outcome:
Students gain real-time project experience required for industry roles.

The course prepares students for the Microsoft Azure Data Engineer Associate certification.

Topics Covered:

• DP-203 Exam Overview
• Exam Objectives Breakdown
• Practice Tests
• Interview Preparation
• Certification Strategy

Outcome:
Students become ready to clear the Microsoft Azure Data Engineer certification.