Databricks Fundamentals: Clusters, Notebooks, and Machine Learning

Duration: Hours

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    Training Mode: Online

    Description

    Introduction of Databricks Fundamentals

    Welcome to Databricks Fundamentals : Clusters, Notebooks, and Machine Learning! This course provides  a powerful platform for data analytics and machine learning. Participants will learn how to set up their Databricks environment, manage clusters, and utilize notebooks for collaborative data analysis. The curriculum covers essential topics, including data import/export, working with DataFrames, and integrating machine learning libraries. By the end of the course, learners will be equipped with the skills to effectively use Databricks for their data projects. This training is ideal for data professionals seeking to enhance their analytical capabilities in a cloud-based environment.Participants will explore the power of Databricks Lakehouse, PySpark, and SQL to solve real-world data challenges

    Prerequisites

    • Basic knowledge of Python and SQL.
    • Familiarity with data analysis concepts.
    • Understanding of cloud computing fundamentals.
    • Access to a computer with internet connectivity for hands-on practice.

    TABLE OF CONTENT

    1 : Introduction of Databricks Fundamentals
    1.1 Creating an Account
    1.2 Setting up a Workspace

    2 : Workspace and Notebooks in Databricks Fundamentals

    2.1 Overview of Databricks Workspace
    2.2 Using Notebooks
    2.3 Collaborating in Notebooks

    3 : Clusters

    3.1 Creating and Managing Clusters
    3.2 Cluster Configuration
    3.3 Autoscaling

    4 : Data Import and Export

    4.1 Loading Data into Databricks
    4.2 Data Import from External Sources
    4.4 Exporting Data from Databricks

    5 : DataFrames and Datasets:

    5.1 Introduction to Apache Spark DataFrames
    5.2 Data Manipulation with Spark SQL
    5.3 Structured Streaming(Ref: DB2 Fundamentals Course: Building a Strong Foundation in Database Systems)

    6 : Libraries

    6.1 Installing and Managing Libraries
    6.2 Using Third-party Libraries

    7 : Machine Learning:

    7.1 MLlib for Machine Learning
    7.2 MLflow Integration
    7.3 Model Deployment

    8 : Jobs and Automation:

    8.1 Creating and Managing Jobs
    8.2 Automation with Jobs and Cron Scheduling

    9 : Security in Databricks Fundamentals

    9.1 Access Control and Permissions
    9.2 Data Encryption
    9.3 Compliance and Auditing

    Conclusion

    This Databricks Fundamentals course prepares learners to expertly leverage Databricks for advanced analytics, real-time insights, and seamless data integration. Participants will finish with the skills to design and optimize end-to-end data workflows.

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