Databricks Course Overview

Databricks Course Overview

The Databricks course is designed to equip learners with the knowledge and skills necessary to work with Apache Spark and Databricks. It's beneficial for those aiming to obtain Databricks certification and gain expertise in big data processing, analytics, and machine learning. The course walks through the essentials of big data, Spark's various programming languages, and the use of Databricks' unified platform, including its architecture and community edition.

Learners will understand how to implement Databricks on Azure and AWS cloud services, integrate into data pipelines, and set up their workspaces and clusters. The course also covers data ingestion, performing queries, data visualization, and the use of Delta Lake for data reliability. By the end of the course, participants will be well-prepared to take Databricks certification courses and apply their knowledge in real-world scenarios, from analytics to machine learning projects.

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  • Live Online Training (Duration : 24 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

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Course Prerequisites

To ensure a successful learning experience in the Databricks course offered by Koenig Solutions, the following minimum prerequisites are recommended:


  • Basic Understanding of Big Data Concepts: Familiarity with what Big Data is and the challenges it presents is essential, as Databricks is a platform designed to handle large data sets.


  • Fundamental Knowledge of Apache Spark: Since Databricks is built on top of Apache Spark, having an introductory understanding of Spark's role in big data processing will be beneficial.


  • Programming Experience: Some experience with at least one of the Spark languages (Scala, Python, R, Java, or SQL) is highly recommended, as these are used for data processing and analysis tasks within Databricks.


  • Conceptual Knowledge of Data Analytics and Machine Learning: Understanding the basics of data analytics and machine learning will help in comprehending the applications and capabilities of Databricks.


  • Familiarity with Cloud Platforms: Basic knowledge of cloud services, particularly Microsoft Azure and/or Amazon Web Services (AWS), since the course covers Databricks implementation on these platforms.


  • Interest in Data Engineering/Science: As Databricks is a tool used predominantly by data engineers and scientists, an interest in these fields will facilitate a more engaging learning experience.


Please note that while these prerequisites are recommended for the best chance at success, Koenig Solutions is committed to helping all students, regardless of their starting skill level. Our courses are designed to be accessible, with expert instructors ready to guide you through each step of your learning journey.


Target Audience for Databricks

The Databricks course by Koenig Solutions covers Big Data analytics, machine learning, and cloud implementations, targeting IT professionals enhancing data skills.


Target Audience for the Databricks Course:


  • Data Scientists
  • Data Engineers
  • Big Data Analysts
  • Machine Learning Engineers
  • Data Architects
  • Cloud Solutions Architects
  • IT Professionals with a focus on data analytics and processing
  • Software Developers interested in Big Data and analytics
  • DevOps Engineers involved in data pipeline integration
  • Database Administrators looking to expand into Big Data platforms
  • Technical Managers overseeing data or analytics teams
  • Business Analysts who require a deeper understanding of Big Data tools and frameworks
  • System Administrators aiming to manage and deploy Databricks environments


Learning Objectives - What you will Learn in this Databricks?

Introduction to Learning Outcomes

In this Databricks course, participants will gain comprehensive knowledge of Apache Spark, Databricks, data analytics, machine learning, and cloud implementations, leading to mastery in data engineering and analysis.

Learning Objectives and Outcomes

  • Understand the concept of Big Data and its challenges.
  • Learn the fundamentals of Apache Spark and its various language interfaces including Scala, Python, R, Java, and SQL.
  • Gain hands-on experience with the Databricks Community Edition and comprehend its architecture.
  • Acquire skills in defining and applying data analytics and machine learning concepts within the Databricks environment.
  • Implement Databricks on cloud platforms such as Azure and AWS for scalable analytics.
  • Integrate Databricks seamlessly into data pipelines for enhanced data processing.
  • Set up and manage a Databricks Workspace and Clusters on Azure, and understand the configuration steps for optimal performance.
  • Master the process of uploading, connecting to Spark data sources, and handling tables and data types.
  • Develop proficiency in using Databricks Notebooks for data manipulation, including writing SQL queries, performing joins, and viewing aggregates.
  • Create insightful visualizations and understand DataFrame operations, including structured streaming and visualizing machine learning outputs.
  • Learn to create, run, and monitor Databricks Jobs, set up alerts, and troubleshoot common issues.
  • Explore Delta Lake's features for reliable data storage, and perform data operations like delete, update, and merge within Delta Tables, alongside an overview of Delta Engine.