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DP-203: Data Engineering on Microsoft Azure

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DP-203: Data Engineering on Microsoft Azure

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Find out more about DP-203: Data Engineering on Microsoft Azure

The DP-203 course equips learners to design, implement, and manage robust data solutions on Microsoft Azure. Participants gain hands-on experience with data storage, transformation, and integration using Azure services like Azure Data Lake, Azure Synapse, and Azure Databricks. The course covers data pipelines, security, monitoring, and optimization for scalable solutions. Designed for aspiring data engineers, it aligns with the DP-203 certification, preparing professionals to efficiently manage, process, and analyze large-scale structured and unstructured data in the cloud.

Duration

35 hours

Fees

$399.00

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About DP-203: Data Engineering on Microsoft Azure

Learn to design and implement scalable data solutions on Azure.
Explore Azure Data Lake, Synapse Analytics, and Databricks services.
Build, schedule, and monitor robust data pipelines efficiently.
Perform data ingestion, transformation, and integration across multiple sources.
Ensure data security, privacy, and compliance using Azure tools.
Optimize data storage, performance, and query execution strategies.
Implement batch and streaming solutions for real-time analytics.
Monitor, troubleshoot, and maintain production data workflows reliably.
Gain hands-on labs to master Azure data engineering skills.
Prepare thoroughly for Microsoft DP-203 certification and career growth.

Course Contents

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Overview of data engineering roles and responsibilities
Azure ecosystem and architecture for data solutions
Lab: Set up Azure resources for data engineering

Azure Data Lake Storage Gen2 fundamentals
Relational data with Azure SQL Database and Synapse
Non-relational storage with Cosmos DB
Lab: Configure and manage storage accounts

Batch and streaming data ingestion concepts
Using Azure Data Factory and Synapse pipelines
Event Hubs, IoT Hub, and Azure Stream Analytics
Lab: Build and schedule data ingestion pipelines

Azure Databricks and Apache Spark fundamentals
Data wrangling, cleansing, and enrichment workflows
Delta Lake for structured and unstructured data
Lab: Transform data with Spark notebooks in Databricks

Orchestrating ETL/ELT pipelines in Data Factory
Managing dependencies and monitoring workflows
Lab: Design an end-to-end automated pipeline

Data encryption, access control, and role-based security
Implementing data governance with Azure Purview
Lab: Secure and monitor a data solution

Designing star schemas and dimensional models in Synapse
Optimizing queries and using serverless SQL pools
Real-time analytics integration with Power BI
Lab: Query and visualize data with Synapse + Power BI

Performance tuning for pipelines and queries
Monitoring data pipelines and system health
Cost optimization strategies for big data workloads
Lab: Monitor and troubleshoot a pipeline solution

Implementing real-time analytics with Event Hubs and Stream Analytics
Integrating Databricks ML models into pipelines
Hybrid and multi-cloud data solutions
Lab: End-to-end big data pipeline with real-time processing

End-to-end project: ingest, transform, store, and visualize data
Best practices for data engineering on Azure
Exam preparation tips and practice questions
Lab: Deliver and present a complete Azure data engineering solution

Eligibility of the course

Basic knowledge of data concepts: relational, non-relational, structured, and unstructured data.
Familiarity with SQL for querying, data manipulation, and transformation tasks.
Understanding of cloud computing fundamentals, preferably Azure services and architecture.
Experience with data integration, ETL/ELT processes, or data pipelines.
Knowledge of programming languages like Python, .NET, or Scala is helpful.
Optional but recommended: exposure to big data technologies like Spark, Hadoop, or Databricks.

Key takeaways

Comprehensive Study material
Access to Practice tests
Access to LMS course content
Access to course assignments
Industry-relevant case-studies

FAQs

Asked Questions & Answer

It teaches designing, building, and managing scalable data solutions using Azure services.

Ideal for aspiring data engineers, ETL developers, and cloud data professionals.

Yes. Basic SQL, cloud fundamentals, and data integration concepts are recommended.

Data ingestion, transformation, pipeline creation, monitoring, security, and performance optimization.

Yes. It aligns with the Microsoft DP-203 certification exam for Data Engineer Associate.

Through instructor-led training and hands-on Azure labs.

Prepares for roles like Azure Data Engineer, ETL Specialist, and Cloud Developer.

Azure Data Lake, Azure Synapse Analytics, Azure Databricks, and Azure Data Factory.

Typically 3–5 days instructor-led depending on experience.

Yes. Microsoft’s DP-203 certification is widely recognized in data engineering roles worldwide.

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