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Data Engineering with Azure Synapse
- Categories:
- Data and Office Applications
- Level:
- Intermediate
- Tier type:
- Premium
- Code:
- QADEAS
- Exam:
- Not Applicable
Overview
This course provides practical training in implementing and managing data engineering workloads on Microsoft Azure. Learners will work with Azure Synapse Analytics, Azure Data Lake Storage Gen2, Spark notebooks, Data Flow Gen2, pipelines, and SQL-based data services.
The course focuses on common data engineering tasks, including orchestrating data transfer and transformation pipelines, working with data files in a data lake, analysing and transforming data at scale, and creating and loading relational data warehouses. Learners will also explore how to prepare data for reporting by building a Power BI semantic model from Synapse data.
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.
What's included
Select your preferred way to learn:
What is Virtual?
Live, instructor-led training delivered online
Interactive online sessions led by subject matter experts. Learners join live classes, take part in discussions, and complete practical exercises from any location, making it easy to fit collaborative learning into busy schedules.
If you prefer to connect to a course that is taking place in a physical classroom, you can choose our Remote Access option. .
Best for: Teams and individuals who want expert guidance, real-time collaboration, and flexible access.
What's included?
2 Days instructor led course
Exam: Not Applicable
6 month free access to QA learning platform
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Find out more about the course:
Outline
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.
Learning outcomes
By the end of this course, learners will be able to:
- Describe the architecture and core services used in Azure Synapse Analytics
- Configure Synapse workspaces, storage accounts, linked services, and access controls
- Work with data in Azure Data Lake Storage Gen2 and organise data into appropriate layers
- Analyse and transform structured and semi-structured data using Spark, PySpark, serverless SQL, and Data Flow Gen2
- Design and load relational data warehouses using dedicated SQL pools and star schema principles
- Build and manage data pipelines to orchestrate data transfer and transformation workloads
- Connect Synapse data to Power BI and create an efficient semantic model for reporting
Good to know
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