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Azure Synapse Analytics: The Complete Data Engineering Guide with SQL, Spark, Pipelines & Power BI (Hands-On Projects + DP-203 Prep
Paperback
Series: Data Engineering Domain for Professional, Book 21
Publisher: Independently Published
Published: Apr 5 2026
Pages: 140
Weight: 0.43
Height: 0.30 Width: 6.00 Depth: 9.00
Language: English
Struggling to learn Azure Synapse Analytics and land a high-paying data engineering job?
This complete hands-on guide is your step-by-step roadmap to mastering Azure Synapse Analytics, from fundamentals to advanced real-world applications.
Whether you are a beginner, QA engineer, data analyst, or aspiring data engineer, this book will help you build job-ready skills using practical examples, real projects, and industry best practices.
Inside this book, you will learn:
- Azure Synapse Workspace and Architecture
- Data Integration and Pipelines (ETL and ELT)
- SQL Analytics (Dedicated and Serverless SQL Pools)
- Big Data Processing with Apache Spark
- Data Visualization using Power BI
- Security, Governance, and DevOps
- Real-World Projects and Case Studies
- Performance Optimization and Cost Management
- DP-203 Certification Preparation Guide
What makes this book different?
Unlike many theoretical guides, this book focuses on real-world implementation, hands-on projects, and interview preparation so you can apply what you learn immediately.
By the end of this book, you will be able to:
- Build end-to-end data pipelines
- Process large-scale data using Spark
- Design scalable data warehouses
- Create professional dashboards
- Optimize performance and reduce costs
- Prepare for Azure Data Engineer interviews
Who is this book for?
- Beginners in Data Engineering
- QA Engineers transitioning to data roles
- Data Analysts and BI Developers
- Students preparing for DP-203 certification
If you want to master Azure Synapse Analytics and unlock high-paying data engineering opportunities, this book provides a complete and practical roadmap.
Start your journey today and become a job-ready Azure Data Engineer.
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