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DP-3027 : Implement a Data Engineering Solution with Azure Databricks Course Overview

DP-3027 : Implement a Data Engineering Solution with Azure Databricks Course Overview

The DP-3027: Implement a Data Engineering Solution with Azure Databricks course equips learners with essential skills to design and implement data engineering solutions using Azure Databricks. This course covers critical topics such as data ingestion, transformation, and optimization, enabling participants to leverage big data technologies effectively.

By the end of the course, students will be able to apply best practices in data management, build scalable data pipelines, and integrate machine learning models. With hands-on exercises and real-world scenarios, attendees will gain practical experience that prepares them for challenges in modern data engineering, making them valuable assets in the field of data analytics.

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Course Fee 575
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575 (USD)
  • Live Training (Duration : 8 Hours)
  • Per Participant
  • Includes Official Coursebook
  • Guaranteed-to-Run (GTR)
  • Classroom Training fee on request
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  • Live Training (Duration : 8 Hours)
  • Per Participant
  • Classroom Training fee on request
  • Includes Official Coursebook
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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

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Target Audience for DP-3027 : Implement a Data Engineering Solution with Azure Databricks

DP-3027: Implement a Data Engineering Solution with Azure Databricks offers professionals the skills to design and implement data solutions, focusing on utilizing Azure Databricks for effective data engineering.


  • Data Engineers
  • Data Architects
  • Cloud Solution Architects
  • Business Intelligence Developers
  • Azure Database Administrators
  • Data Scientists
  • Analytics Professionals
  • IT Professionals transitioning to Data Engineering
  • Software Engineers with a focus on data solutions
  • IT Managers overseeing data projects
  • Students and Graduates interested in data engineering careers


Learning Objectives - What you will Learn in this DP-3027 : Implement a Data Engineering Solution with Azure Databricks?

Course Introduction

The DP-3027: Implement a Data Engineering Solution with Azure Databricks course equips students with essential skills in data engineering using Azure Databricks, focusing on data ingestion, transformation, and analytics to drive data-driven decisions.

Learning Objectives and Outcomes

  • Understand the architecture and components of Azure Databricks.
  • Implement data ingestion techniques using Azure Databricks.
  • Perform data transformation and processing using Apache Spark.
  • Develop ETL (Extract, Transform, Load) workflows within Databricks.
  • Utilize Databricks notebooks for collaborative data analytics.
  • Integrate Azure services with Databricks for enhanced data solutions.
  • Implement data security measures and access controls in Databricks.
  • Optimize performance and monitor workloads in Azure Databricks.
  • Use Delta Lake for managing data reliability and integrity.
  • Deploy machine learning models using Databricks features.

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