Spark Up Your Skills: Dive into PySpark for Big Data Exploration
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PySpark Module
Module Outline:
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.
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.
Resilient Distributed Datasets (RDDs)
- Understanding RDDs and their role in PySpark.
- Creating RDDs from various data sources.
- Basic RDD transformations and actions.
Data Transformations and Actions in PySpark
- Advanced RDD transformations and actions.
- Working with key-value pairs in RDDs.
- Lazy evaluation and Spark lineage graph.
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.
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.
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.
- 1 Month
- Weekdays : Mon to Fri ( 1hr/day )
- Weekend: 2hrs/day
- Flexible Time
- Free Session Videos
- Course Completion Certificate
- Lifetime Customer Support
- Helping to Get a Job
- Resume Preparation