PYSPARK

Index:

1. Introduction to Apache Spark and PySpark:

• Overview of Apache Spark and its ecosystem.
• Introduction to PySpark as a Python API for Spark.
• Setting up the development environment with PySpark.

2. Getting Started with PySpark:

• Creating SparkContext and SparkSession objects in PySpark.
• Loading and saving data with PySpark DataFrames.
• Exploring PySpark RDDs (Resilient Distributed Datasets) and DataFrames.

3. Data Manipulation and Transformation:

• Performing data manipulation and transformation operations with PySpark DataFrame API.
• Filtering, grouping, aggregating, and joining data in PySpark.
• Working with user-defined functions (UDFs) and built-in PySpark functions.

4. PySpark SQL and Data Analysis:

• Writing SQL queries and expressions with PySpark SQL.
• Performing data analysis and exploratory data analysis (EDA) with PySpark.
• Visualizing data with PySpark and integration with visualization libraries.

5. Machine Learning with PySpark MLlib:

• Introduction to PySpark MLlib for scalable machine learning.
• Building and training machine learning models with PySpark MLlib.
• Evaluating model performance and tuning hyperparameters with PySpark MLlib.

6. Advanced PySpark Topics:

• Working with graph processing algorithms in PySpark GraphFrames.
• Implementing stream processing and real-time analytics with PySpark Structured Streaming.
• Integrating PySpark with external libraries and frameworks (e.g., Pandas, scikit-learn).

7. Building Scalable Data Pipelines:

• Designing and building scalable data pipelines with PySpark.
• Implementing data ingestion, processing, and storage solutions with PySpark.
• Orchestrating data workflows and scheduling jobs with PySpark and Apache Airflow.

8. Performance Optimization and Debugging:

• Optimizing PySpark applications for performance and efficiency.
• Debugging and troubleshooting common issues in PySpark applications.

Monitoring and profiling PySpark jobs for performance tuning