Azure Databricks Platform Architect Course Overview

Azure Databricks Platform Architect Course Overview

The Azure Databricks Platform Architect course at Koenig Solutions provides a comprehensive understanding of data analytics and engineering using the Databricks platform. Participants will learn key concepts such as data integration, Spark programming, and scalability within Azure. The course’s learning objectives focus on equipping learners with the skills to design and implement robust data architectures, facilitate collaborative data science projects, and optimize performance for big data workflows. Practical applications include developing ETL pipelines, conducting advanced analytics, and leveraging machine learning models. By the end of the course, students will be prepared to architect efficient solutions that can handle the growing demands of data-driven organizations.

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  • Live Training (Duration : 24 Hours)
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  • Classroom Training fee on request

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

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Target Audience for Azure Databricks Platform Architect

The Azure Databricks Platform Architect course equips professionals with essential skills to design and manage scalable data analytics solutions on the Azure Databricks platform.


Target Audience and Job Roles:


  • Data Architect
  • Data Engineer
  • Cloud Architect
  • Business Intelligence Developer
  • Big Data Analyst
  • Machine Learning Engineer
  • DevOps Engineer
  • Data Scientist
  • Database Administrator
  • IT Consultant
  • Software Engineer
  • System Integrator
  • Technical Project Manager
  • Analytics Manager
  • Enterprise Data Architect


Learning Objectives - What you will Learn in this Azure Databricks Platform Architect?

Introduction

The Azure Databricks Platform Architect course equips students with essential skills to design and manage robust data solutions on the Azure Databricks platform, focusing on architecture, data processing, and optimization techniques for big data analytics.

Learning Objectives and Outcomes

  • Understand the architecture and components of Azure Databricks.
  • Design scalable and efficient data pipelines.
  • Implement data engineering and machine learning workflows.
  • Optimize performance for big data processing tasks.
  • Manage and secure data access and governance.
  • Utilize Delta Lake for data reliability and performance.
  • Integrate Azure Databricks with other Azure services.
  • Monitor and troubleshoot Databricks workloads effectively.
  • Apply best practices in data analytics and visualization.
  • Architect solutions for real-time data processing and analytics.

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