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eMexo Technologies
Azure Data Engineer Certification Training
Curriculum
11 Sections
50 Lessons
10 Weeks
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Introduction to Azure Data Engineering
Gain an overview of Microsoft Azure’s ecosystem and understand the role of a Data Engineer in building modern data solutions.
3
1.1
Fundamentals of Data Engineering
1.2
Azure Data Services Overview
1.3
Role of Azure Data Engineer in Real-world Projects
Azure Storage & Data Ingestion
Learn how to collect and store structured, semi-structured, and unstructured data using Azure storage services.
5
2.1
Azure Blob Storage, Azure Data Lake Gen2
2.2
Structured & Unstructured Data Handling
2.3
Batch vs Real-time Data Ingestion
2.4
Integration with Azure Event Hub & IoT Hub
2.5
Hands-on: Create a Data Lake & upload raw datasets for further processing
Data Transformation with Azure Data Factory (ADF)
Master the use of pipelines and data flows for large-scale data transformation and movement
6
3.1
Data Factory Overview
3.2
Linked Services, Datasets & Pipelines
3.3
Mapping Data Flows & Control Flows
3.4
Integration Runtime & Triggers
3.5
Orchestration of ETL/ELT Workflows
3.6
Hands-on: Build an ETL pipeline to move data from on-premises SQL Server to Azure Data Lake
Real-time Data Processing with Azure Stream Analytics
Process streaming data in real time for analytics and decision-making.
5
4.1
Introduction to Stream Analytics
4.2
Streaming Data Sources (IoT Hub, Event Hub, Kafka)
4.3
Real-time Data Transformation & Windowing Functions
4.4
Output to Power BI, SQL DB, and Storage
4.5
Hands-on: Stream IoT data into Azure SQL Database and visualize it in Power BI
Data Integration & Orchestration
Automate and orchestrate data workflows for seamless integration.
4
5.1
Azure Logic Apps
5.2
Event-driven Workflows
5.3
Hybrid Data Integration with ADF
5.4
Monitoring & Troubleshooting Pipelines
Data Modeling & Warehousing with Azure Synapse Analytics
Learn to build scalable and performant enterprise data warehouses using Azure Synapse
5
6.1
Synapse SQL Pools & Dedicated SQL Pools
6.2
Data Partitioning, Indexing & Performance Tuning
6.3
PolyBase & Data Virtualization
6.4
Star & Snowflake Schema Modeling
6.5
Hands-on: Create a star schema warehouse in Synapse and run analytical queries
Big Data Processing with Azure Databricks
Explore Apache Spark on Azure for big data analytics and machine learning.
5
7.1
Azure Databricks Overview
7.2
Spark SQL, DataFrames & Delta Lake
7.3
Machine Learning with Databricks MLlib
7.4
Integration with Synapse & Power BI
7.5
Hands-on: Perform ETL on large datasets with Databricks and write results to Azure Data Lake
Data Security & Compliance
Implement security, governance, and compliance for enterprise data solutions.
5
8.1
Role-Based Access Control (RBAC)
8.2
Managed Identities & Key Vault Integration
8.3
Data Encryption (At-Rest & In-Transit)
8.4
GDPR, HIPAA & Compliance in Azure
8.5
Hands-on: Secure Azure SQL Database with Transparent Data Encryption (TDE)
Data Visualization & Reporting
Connect processed data to visualization tools for insights and business intelligence.
5
9.1
Introduction to Power BI
9.2
Connecting Power BI with Synapse & Databricks
9.3
Building Interactive Dashboards & Reports
9.4
Real-time Analytics Integration
9.5
Hands-on: Build an interactive dashboard with Power BI using Synapse data
Monitoring & Optimization
Optimize pipelines, workloads, and costs while monitoring data infrastructure.
4
10.1
Azure Monitor & Log Analytics
10.2
Cost Optimization Best Practices
10.3
Performance Tuning in Synapse & Databricks
10.4
Scalability & Reliability Enhancements
Capstone Project
Apply all skills in a real-world project to design an end-to-end data engineering solution.
3
11.1
Design & Implement a Data Lakehouse with Azure Data Factory + Synapse + Power BI
11.2
Real-time Analytics Pipeline with Event Hub, Stream Analytics, and Databricks
11.3
Automate Monitoring & Alerts for Data Pipelines
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