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Master Data Engineering with Azure Synapse and PySpark Course Overview

Master Data Engineering with Azure Synapse and PySpark Course Overview

Unlock your potential with our Master Data Engineering with Azure Synapse and PySpark course at Koenig Solutions. This comprehensive program is designed to equip you with essential skills in data engineering, focusing on Azure Synapse Analytics and PySpark. You will learn how to efficiently manage, process, and analyze large datasets, enabling informed decision-making in business contexts.

Key learning objectives include understanding data integration techniques, building data pipelines, and leveraging analytics for actionable insights. Through hands-on projects, you will gain practical experience in real-world applications, preparing you for a successful career in data engineering. Elevate your expertise and stay ahead in today's data-driven landscape!

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2,500

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Course Fee 2,500
Total Fees
2,500 (USD)
  • Live Training (Duration : 40 Hours)
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  • Classroom Training fee on request
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  • Live Training (Duration : 40 Hours)
  • Per Participant
  • Classroom Training fee on request
Koeing Learning Stack

Koenig Learning Stack

Free Pre-requisite Training

Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

Post Training Reports

Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

Class Recordings

Get access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.

Free Lab Extensions

Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

Free Revision Classes

Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

Request More Information

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Target Audience for Master Data Engineering with Azure Synapse and PySpark

Master Data Engineering with Azure Synapse and PySpark equips professionals with the skills to efficiently manage big data using advanced analytics and cloud services.


  • Data Engineers
  • Data Analysts
  • Business Intelligence Developers
  • Cloud Engineers
  • Data Scientists
  • SQL Developers
  • IT Professionals seeking specialization in big data
  • Database Administrators
  • Software Developers interested in data solutions
  • Project Managers in data-centric projects
  • Graduates aiming for a career in data engineering
  • Technical Architects focusing on data infrastructure


Learning Objectives - What you will Learn in this Master Data Engineering with Azure Synapse and PySpark?

Introduction:
The Master Data Engineering with Azure Synapse and PySpark course equips learners with essential skills in data engineering, focusing on Azure Synapse and PySpark to handle and process big data efficiently.

Learning Objectives and Outcomes:

  • Understand the architecture and capabilities of Azure Synapse Analytics.
  • Work with PySpark for scalable data processing and transformations.
  • Implement data ingestion techniques for large datasets.
  • Design and optimize data workflows and pipelines.
  • Utilize data storage solutions, including Azure Data Lake and SQL databases.
  • Analyze and visualize data using integrated tools within Azure.
  • Employ best practices for data governance and security.
  • Conduct performance tuning for data processing tasks.
  • Collaborate on data engineering projects within a team.
  • Prepare for real-world data engineering challenges using Azure tools.

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