Course Outline

Introduction to Google Colab and Apache Spark

  • Overview of Google Colab
  • Introduction to Apache Spark
  • Setting up Spark in Google Colab

Data Processing with Apache Spark

  • Working with RDDs and DataFrames
  • Loading and processing large datasets
  • Using Spark SQL for querying structured data

Advanced Analytics with Spark

  • Machine learning with Spark MLlib
  • Performing real-time data analysis
  • Distributed computing with Spark

Visualization and Collaboration in Google Colab

  • Integrating Colab with popular visualization libraries
  • Collaborative workflows with Colab notebooks
  • Sharing and exporting results

Optimizing Big Data Workflows

  • Tuning Spark for performance
  • Optimizing memory and storage usage
  • Scaling workflows for large datasets

Big Data in the Cloud

  • Integrating Google Colab with cloud-based tools
  • Using cloud storage for big data
  • Working with Spark in distributed cloud environments

Case Studies and Best Practices

  • Review of real-world big data applications
  • Case studies using Apache Spark and Colab
  • Best practices for big data analytics

Summary and Next Steps

Requirements

  • Basic knowledge of data science concepts
  • Familiarity with Apache Spark
  • Python programming skills

Audience

  • Data scientists
  • Data engineers
  • Researchers working with big data
 14 Hours

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