Course Description
This course equips learners with skills to design and implement data solutions using Microsoft Azure. It covers data storage, transformation, integration, and analytics using services like Azure Data Factory, Synapse Analytics, and Databricks. Ideal for roles like data engineers, ETL developers, and cloud data professionals.
Why should I join?
Azure Data Engineering course is a powerful way to jumpstart or boost your career in the fast-growing field of cloud data engineering, Azure data solutions are used in healthcare, finance, retail, government, and more. Skills you learn are transferable across multiple sectors.
Gain knowledge of cloud infrastructure along with data engineering. This combined skillset is highly valuable for digital transformation projects.Get ready for the Microsoft Certified: Azure Data Engineer Associate certification. Certification boosts your credibility and opens job opportunities globally.
Course Content
Module 1: Introduction to Tableau
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5 Topics
What is Data Engineering?
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Azure ecosystem overview
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Role of a Data Engineer in cloud projects
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Azure Data Engineer vs. Data Scientist vs. Data Analyst
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Azure certifications overview (focus on DP-203)
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Module 2: Azure Storage Fundamentals
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5 Topics
Azure Blob Storage
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Azure Data Lake Storage Gen2
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Storage account types and performance tiers
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Secure data access with Shared Access Signatures (SAS) and Azure AD
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Lifecycle and tiering policies
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Module 3: Data Ingestion with Azure Data Factory (ADF)
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6 Topics
Introduction to Azure Data Factory
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Creating pipelines and datasets
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Linked services and triggers
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Copy activity and data movement
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Monitoring and debugging pipelines
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Integration Runtime (Self-hosted vs Azure-hosted)
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Module 4: Data Processing with Azure Synapse Analytics
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5 Topics
Overview of Synapse Studio
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Serverless SQL pools vs Dedicated SQL pools
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Writing queries in Synapse SQL
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Integrating ADF pipelines with Synapse
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Data exploration with Synapse notebooks
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Module 5: Data Processing with Azure Databricks
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6 Topics
Introduction to Azure Databricks
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Apache Spark fundamentals
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Notebooks and clusters
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ETL using PySpark
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Delta Lake architecture
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Working with structured streaming
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Module 6: Real-Time Data with Azure Stream Analytics
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4 Topics
Introduction to real-time processing
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Streaming inputs and outputs
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Writing Stream Analytics queries
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Integrating with Event Hubs & IoT Hub
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Module 7: Data Transformation and Orchestration
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4 Topics
Data wrangling and data flows in ADF
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Mapping vs. Wrangling Data Flows
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Parameterization in pipelines
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Custom activities using Azure Functions
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Module 8: Azure SQL & NoSQL Databases
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4 Topics
Azure SQL Database and Managed Instances
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Azure Cosmos DB (Core API, SQL API)
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Performance tuning and indexing
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Data import/export tools
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Module 9: Monitoring, Logging, and Security
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5 Topics
Monitoring data pipelines (ADF & Synapse)
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Azure Monitor, Log Analytics, and Application Insights
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Managing identity with Azure AD
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Role-Based Access Control (RBAC)
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Key Vault for securing credentials
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Module 10: Data Governance & Compliance
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4 Topics
Introduction to Microsoft Preview
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Data cataloging and classification
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Lineage and auditing
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Data masking and encryption options
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Module 11: Capstone Project
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2 Topics
Design and build an end-to-end Azure data pipeline:
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Example: Raw CSV to Azure Data Lake → ETL in Databricks → Load to Synapse → Power BI dashboard.
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Includes
11 Lessons
50 Topics
Admin
