Big Data Analytics with Google Colab and Apache Spark

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

Audience

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

Big Data Analytics with Google Colab and Apache Spark is a powerful approach to processing and analyzing massive datasets efficiently.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics.

By the end of this training, participants will be able to:

• Set up a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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