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DP-3011 Implementing a Data Analytics Solution with Azure Databricks Course Overview

The DP-3011 Implementing a Data Analytics Solution with Azure Databricks course equips professionals with essential skills for leveraging Azure Databricks in data analytics. Ideal for data engineers, data scientists, and analytics professionals, this course emphasizes real-world applications, enabling participants to effectively analyze and visualize data, driving informed decision-making in their organizations.


Course Introduction

Price:

USD 750

Duration:

8 Hrs.

  • Course Introduction
    • Overview of Azure Databricks
    • Importance of Data Analytics in Business
  • Module 1: Getting Started with Azure Databricks
    • Introduction to Databricks environment
    • Setting up Databricks workspace
    • Navigating the Databricks interface
  • Module 2: Data Ingestion and Preparation
    • Connecting to data sources
    • Ingesting data using Apache Spark
    • Data cleansing and transformation techniques
  • Module 3: Exploring Data with Apache Spark
    • Introduction to Apache Spark concepts
    • Data exploration techniques
    • Using Spark SQL for querying data
  • Module 4: Building Data Models
    • Introduction to data modeling concepts
    • Creating and managing Delta Lake tables
    • Optimizing data storage and performance
  • Module 5: Implementing Machine Learning
    • Overview of machine learning concepts
    • Using MLlib and MLflow for model development
    • Model evaluation and tuning
  • Module 6: Data Visualization and Reporting
    • Creating visualizations with Databricks notebooks
    • Integrating with Power BI
    • Building interactive dashboards
  • Module 7: Data Governance and Security
    • Understanding data governance principles
    • Implementing security measures in Databricks
    • Managing user access and permissions
  • Module 8: Advanced Analytics and Real-Time Processing
    • Introduction to stream processing with Spark Structured Streaming
    • Implementing real-time analytics solutions
    • Case studies and best practices
  • Course Summary and Next Steps
    • Recap of key concepts learned
    • Resources for further learning
    • Certification and career pathways in data analytics

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