Spark Up Your Skills: Dive into PySpark for Big Data Exploration

Module Outline:

1. Introduction to Apache Spark and PySpark

   • Overview of Apache Spark and its features.
   • Introduction to PySpark: Python API for Apache Spark.
   • Understanding the Spark ecosystem and its components.

2. Setting Up a PySpark Environment

   • Installation and configuration of Apache Spark and PySpark.
   • Setting up a SparkSession for Python.
   • Using PySpark shell (pyspark) and Jupyter notebooks for interactive development.

3. Resilient Distributed Datasets (RDDs)

   • Understanding RDDs and their role in PySpark.
   • Creating RDDs from various data sources.
   • Basic RDD transformations and actions.

4. Data Transformations and Actions in PySpark

   • Advanced RDD transformations and actions.
   • Working with key-value pairs in RDDs.
   • Lazy evaluation and Spark lineage graph.

5. Introduction to Spark SQL

   • Overview of Spark SQL and DataFrame API.
   • Creating DataFrames from RDDs and external data sources.
   • Performing SQL queries and DataFrame operations.

6. Working with External Data Sources

   • Reading and writing data from/to external sources.
   • Integrating PySpark with big data platforms like Hadoop and Hive.
   • Handling structured and semi-structured data formats.

7. Introduction to PySpark MLlib

   • Overview of MLlib: machine learning library for PySpark.
   • Building machine learning pipelines for classification, regression, and clustering.
   • Training and evaluating machine learning models.