Data Engineering with Azure Synapse

Prerequisites

Learners should have:

• An understanding of cloud computing and core data concepts
• Experience working with data solutions
• Recommended: AZ-900T00 Microsoft Azure Fundamentals and/or DP-900T00 Microsoft Azure Data Fundamentals

Target audience

This course is suited to learners who work with data solutions and want to develop practical data engineering skills using Microsoft Azure. It is particularly relevant to data professionals who need to work with cloud-based data storage, processing, transformation, orchestration, data warehousing, and reporting workflows.

Data Engineering with Azure Synapse Course Content

Introduction to Azure data engineering

• Review the tools and technologies used throughout the course
• Understand the data engineering workflow and course timeline

Setting up Synapse services

• Overview of Azure Synapse Analytics architecture
• Provision Synapse workspaces and associated resources
• Configure storage accounts and linked services
• Manage access control and workspace roles
• Navigate Synapse Studio and its integrated tools
• Introduction to Azure Data Lake Storage Gen2
• Structure data using hierarchical namespaces
• Organise data into raw, curated, and consumption layers
• Manage files, folders, and access permissions
• Integrate data lake storage with Synapse services

Data analysis and transformation using Synapse Spark pools

• Introduction to Apache Spark pools in Synapse
• Query data using Spark SQL and DataFrames
• Perform exploratory data analysis
• Aggregate and filter large datasets
• Visualise outputs within notebooks
• Cleanse and prepare data using PySpark
• Transform structured and semi-structured data
• Work with distributed processing at scale
• Create and manage Parquet and Delta-style outputs
• Optimise Spark jobs for performance and cost

Data analysis and transformation using serverless SQL pools

• Query data directly in the data lake using SQL
• Create external tables and views
• Analyse structured and semi-structured CSV, Parquet, and JSON files
• Optimise query performance in serverless environments
• Explore use cases for on-demand data analysis
• Transform data using T-SQL in a serverless model
• Create reusable views for transformed datasets
• Prepare data for reporting and downstream consumption
• Combine data from multiple sources
• Consider performance and cost when transforming data

Data warehousing using dedicated SQL pools

• Introduction to enterprise data warehousing concepts
• Design star schemas using fact and dimension tables
• Create and load tables in dedicated SQL pools
• Implement extract, load, transform patterns using T-SQL
• Manage performance through indexing and partitioning

Data transformation using Data Flow Gen2

• Introduction to mapping data flows and visual extract, transform, load
• Design transformation pipelines using drag-and-drop tools
• Ingest data from multiple sources
• Apply joins, aggregations, and derived columns
• Monitor and debug data flows

Building a Power BI semantic model from Synapse data

• Connect Power BI to Synapse using Import and DirectQuery modes
• Design efficient data models using star schema principles
• Create measures and calculations using DAX
• Manage relationships and hierarchies
• Implement row-level security
• Optimise performance for large-scale reporting

Exams and assessments

The supplied course content does not specify formal exams, knowledge checks, quizzes, or other assessment methods. Assessment details should therefore be confirmed before publication.

Hands-on learning

The course is focused on practical data engineering activities across Microsoft Azure. Learners work with Synapse Studio, data lake storage, Spark notebooks, SQL pools, data transformation pipelines, and Power BI. Practical activities include querying and transforming datasets, managing data files, creating warehouse tables, designing pipelines, and preparing data for reporting.