In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage.

Course Prerequisites

While there are no mandatory prerequisites, it is recommended that learners have:

• A basic understanding of cloud computing concepts, preferably with prior exposure to Microsoft Azure
• Familiarity with core data concepts such as relational and non-relational data
• Experience with programming languages such as SQL or Python
• An understanding of data processing and analytics

Target Audience

This course is intended for:

• Data professionals, data architects, and business intelligence (BI) professionals seeking to expand their skills in cloud-based data engineering
• Individuals who want to learn how to design and build analytical solutions using Microsoft Azure data platform technologies
• Data analysts and data scientists who work with or support analytical solutions on Azure

Why Choose us

⭢ Live Online Training (Duration : 32 Hours)

⭢ Guaranteed to run classes

⭢ Experienced & certified trainers

⭢ Query Handling session

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Learning Objectives

The DP-203T00 course equips learners with practical skills to design, build, and manage modern data solutions using Azure’s data engineering services.

• Understand the core principles of data engineering on Azure, including data storage options, streaming data, and analytics services
• Utilize Azure Data Lake Storage Gen2 for managing large-scale data storage and performing analytical workloads
• Query and transform data efficiently using Azure Synapse Analytics serverless SQL pools
• Perform advanced data engineering tasks using Azure Synapse Apache Spark Pools and Azure Databricks
• Design, build, and optimize data pipelines using Azure Synapse Analytics
• Implement hybrid transactional and analytical processing (HTAP) using Azure Synapse Link
• Develop and manage real-time analytics and streaming data solutions using Azure Stream Analytics and Event Hubs
• Integrate Azure Databricks for high-performance data exploration, ETL operations, and data science workflows
• Apply robust security and data protection measures within the Azure Synapse Analytics environment
• Leverage machine learning integration and Power BI for advanced reporting and predictive analytics capabilities within Azure