Databricks on AWS for Professionals Course Overview

Databricks on AWS for Professionals Course Overview

Databricks on AWS for Professionals is a comprehensive course designed to empower IT professionals with the skills needed to leverage Databricks’ powerful analytics platform within the AWS environment. Participants will learn to efficiently manage big data, automate data pipelines, and utilize machine learning tools to derive insights from vast datasets.

By the end of the course, attendees will have a solid understanding of best practices for collaborative data science and will be able to apply concepts like data engineering, notebook workflows, and SQL analytics in real-world scenarios. This hands-on approach ensures that participants are well-prepared to enhance their organizations' data capabilities effectively.

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Course Fee 850
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850 (USD)
  • Live Training (Duration : 20 Hours)
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  • Live Training (Duration : 20 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 Databricks on AWS for Professionals

The "Databricks on AWS for Professionals" course equips IT professionals with the skills to leverage Databricks for big data processing and machine learning on AWS.


  • Data Engineers
  • Data Analysts
  • Data Scientists
  • Machine Learning Engineers
  • Cloud Solutions Architects
  • IT Project Managers
  • Business Intelligence Developers
  • Software Developers
  • DevOps Engineers
  • Database Administrators
  • System Administrators
  • Technical Consultants
  • AWS Certified Professionals


Learning Objectives - What you will Learn in this Databricks on AWS for Professionals?

Introduction

The Databricks on AWS for Professionals course equips learners with the skills to leverage Databricks for big data analytics, machine learning, and data engineering, focusing on effective integration within the AWS ecosystem.

Learning Objectives and Outcomes

  • Understand the architecture and core components of Databricks on AWS.
  • Use Apache Spark for data processing and analysis in Databricks.
  • Implement and manage Delta Lake for reliable data storage.
  • Build and deploy machine learning models using MLflow.
  • Develop interactive notebooks for data analysis and visualization.
  • Integrate Databricks with various AWS services (e.g., S3, Redshift).
  • Optimize Spark jobs for better performance and cost efficiency.
  • Manage clusters and jobs effectively within the Databricks environment.
  • Apply best practices for data governance and security within Databricks.
  • Collaborate with teams using Databricks workspace features and tools.

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