Azure Databricks Data Engineering Course Overview

Azure Databricks Data Engineering Course Overview

Unlock the power of data with our Azure Databricks Data Engineering course at Koenig Solutions. This comprehensive program focuses on essential topics like data integration, pipeline creation, and machine learning workflows within the Azure Databricks environment.

By the end of the course, you'll achieve clear learning objectives: mastering the art of data processing, enhancing data analytics skills, and developing robust data pipelines. You'll gain hands-on experience through practical applications, transforming theoretical concepts into real-world solutions. Whether you're a data analyst or aspiring engineer, this course equips you with valuable expertise to excel in today's data-driven landscape. Join us and elevate your career in data engineering!

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1,700

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Course Fee 1,700
Total Fees
1,700 (USD)
  • Live Training (Duration : 40 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
  • Classroom Training fee on request

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  • Live Training (Duration : 40 Hours)
  • Per Participant
  • Classroom Training fee on request

♱ Excluding VAT/GST

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

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Target Audience for Azure Databricks Data Engineering

Azure Databricks Data Engineering course equips professionals with the skills to efficiently process big data and build data pipelines using Databricks, making it ideal for data-driven roles.


  • Data Engineers
  • Data Analysts
  • Business Intelligence Analysts
  • Machine Learning Engineers
  • Data Scientists
  • Database Administrators
  • Cloud Solutions Architects
  • IT Professionals focusing on Data Management
  • Software Developers working with data applications
  • Product Managers in data-centric companies


Learning Objectives - What you will Learn in this Azure Databricks Data Engineering?

Azure Databricks Data Engineering Course Overview

The Azure Databricks Data Engineering course equips students with essential skills in data analytics and engineering, focusing on leveraging the power of Apache Spark and Azure Databricks for efficient big data processing.

Learning Objectives and Outcomes

  • Understand the fundamentals of Azure Databricks and its architecture.
  • Utilize Apache Spark for data processing and analysis.
  • Design and implement data pipelines on Azure Databricks.
  • Optimize Spark jobs for better performance and resource management.
  • Work with structured and semi-structured data formats.
  • Implement data ingestion and transformation using notebooks.
  • Conduct data exploration and visualization within Databricks.
  • Collaborate using Delta Lake for data reliability and performance.
  • Monitor and troubleshoot Spark applications effectively.
  • Secure data and manage access in Azure Databricks environments.

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