Data Engineering on Microsoft Azure Training (DP 203)

DP-203 is designed for data professionals who want to build and manage data solutions on Microsoft Azure. This course focuses on designing data storage, developing data processing pipelines, and implementing data security and monitoring.

This course provides in-depth knowledge of data engineering concepts and Azure data services. Participants will gain hands-on experience with Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and data lake solutions.

The course covers real-world scenarios such as batch processing, stream processing, data transformation, and performance optimization.

Module 1: Design and Implement Data Storage

Topics Covered:

• Data storage options in Azure
• Azure Data Lake Storage Gen2
• Partitioning and indexing strategies

Lab:

• Create a Data Lake Storage account
• Organize data into hierarchical structure
• Implement partitioning strategies

Outcome:
Design efficient and scalable data storage solutions.

Module 2: Data Processing with Azure Databricks

Topics Covered:

• Apache Spark fundamentals
• Data transformation using PySpark
• Batch processing

Lab:

• Create a Databricks workspace
• Run Spark jobs
• Transform and clean data

Outcome:
Process large datasets efficiently using Spark.

Module 3: Build Data Pipelines with Azure Data Factory

Topics Covered:

• Data Factory pipelines and activities
• Data movement and orchestration
• Integration runtimes

Lab:

• Build an ETL pipeline
• Move data from on-prem to cloud
• Schedule and monitor pipelines

Outcome:
Create automated data pipelines.

Module 4: Real-Time Data Processing

Topics Covered:

• Stream processing concepts
• Azure Stream Analytics
• Event Hubs

Lab:

• Ingest streaming data
• Process real-time events
• Analyze streaming outputs

Outcome:
Handle real-time data scenarios.

Module 5: Data Warehousing with Azure Synapse Analytics

Topics Covered:

• Synapse architecture
• Data warehousing concepts
• Query optimization

Lab:

• Create Synapse workspace
• Load data into data warehouse
• Optimize queries

Outcome:
Build modern cloud data warehouses.

Module 6: Data Transformation & Integration

Topics Covered:

• Data cleansing techniques
• Data flows in Azure Data Factory
• Data integration patterns

Lab:

• Transform raw data into structured format
• Apply business rules
• Validate data quality

Outcome:
Prepare high-quality data for analytics.

Module 7: Data Security and Governance

Topics Covered:

• Data encryption
• Role-based access control (RBAC)
• Data masking and compliance

Lab:

• Configure security settings
• Apply access controls
• Monitor data access

Outcome:
Secure data platforms effectively.

Module 8: Monitoring and Optimization

Topics Covered:

• Monitoring tools in Azure
• Performance tuning
• Cost optimization

Lab:

• Monitor pipeline execution
• Optimize query performance
• Analyze cost usage

Outcome:
Improve performance and reduce costs.