DP-203 – Data Engineering on Microsoft Azure
In this course, the student will learn about the data engineering as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. The students will learn how to interactively explore data […]
- Course Objectives
- Course Outline
- Course Materials
Instructor Profile
Dinesh Priyankara, MVP, MCT
Founder / Principal Architect at dinesQL (Pvt) Ltd
Dinesh is an experienced professional and database enthusiast with skills in database management systems and business intelligence, especially on the Microsoft SQL Server product suite. Possessing over 16 years of experience in data-related technologies, he does training, and consulting, and is a top contributor to the local SQL Server community.
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Audience Profile
After completing this course, you will be able to:
- Explore compute and storage options for data engineering workloads in Azure.
- Run interactive queries using serverless SQL pools.
- Perform data exploration and transformation in Azure Databricks.
- Explore, transform, and load data into the Data Warehouse using Apache Spark.
- Ingest and load data into the Data Warehouse.
- Transform data with Azure Data Factory or Azure Synapse Pipelines.
- Integrate data from notebooks with Azure Data Factory or Azure Synapse Pipelines.
- Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link.
- Perform end-to-end security with Azure Synapse Analytics.
- Perform real-time stream processing with Stream Analytics.
- Create a stream processing solution with Event Hubs and Azure Databricks.
Course Objectives
After completing this course, you will be able to:
- Explore compute and storage options for data engineering workloads in Azure.
- Run interactive queries using serverless SQL pools.
- Perform data exploration and transformation in Azure Databricks.
- Explore, transform, and load data into the Data Warehouse using Apache Spark.
- Ingest and load data into the Data Warehouse.
- Transform data with Azure Data Factory or Azure Synapse Pipelines.
- Integrate data from notebooks with Azure Data Factory or Azure Synapse Pipelines.
- Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link.
- Perform end-to-end security with Azure Synapse Analytics.
- Perform real-time stream processing with Stream Analytics.
- Create a stream processing solution with Event Hubs and Azure Databricks.
Participant Prerequisites
Successful students should start this course with knowledge of cloud computing, core data concepts, and professional experience working with data solutions.
Specifically, students are recommended to have completed:
- AZ-900 – Azure Fundamentals
- DP-900 – Microsoft Azure Data Fundamentals
Course Outline
The following items describe the outline of the course:
- Module 1: Explore compute and storage options for data engineering workloads
- Module 2: Run interactive queries using Azure Synapse Analytics serverless SQL pools
- Module 3: Data exploration and transformation in Azure Databricks
- Module 4: Explore, transform, and load data into the Data Warehouse using Apache Spark
- Module 5: Ingest and load data into the Data Warehouse
- Module 6: Transform data with Azure Data Factory or Azure Synapse Pipelines
- Module 7: Orchestrate data movement and transformation in Azure Synapse Pipelines
- Module 8: End-to-end security with Azure Synapse Analytics
- Module 9: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
- Module 10: Real-time Stream Processing with Stream Analytics
- Module 11: Create a Stream Processing Solution with Event Hubs and Azure Databricks
Course Materials
The following materials are included as part of the course:
- Microsoft Official Digital Curriculum (MOC)
- Lab Setup
Frequently Asked Questions
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