Getting started with PySpark
Getting started with PySpark (2 days)
In today’s data-driven world, organizations are handling massive amounts of information generated from various sources. Traditional data processing tools often struggle to manage and analyze such large datasets efficiently. This is where Apache Spark comes into play—a powerful open-source distributed computing framework designed for speed and scalability in big data processing.
PySpark is the Python API for Apache Spark, enabling users to leverage Spark’s powerful distributed computing capabilities using Python, one of the most popular programming languages for data science and analytics. PySpark simplifies big data processing by allowing you to write scalable, parallelized applications that can handle massive datasets with ease.
Over two immersive days, you’ll explore the key concepts of PySpark, from understanding its architecture to performing transformations and analyzing large datasets with ease. Through interactive sessions and practical exercises, you will learn how to set up your PySpark environment, manipulate data using RDDs and DataFrames, and implement Spark SQL for structured data analysis.
Course Outline
- Getting Started with Spark and PySpark
- Create Spark Dataframes using Python Collections and Pandas Dataframes
- Selecting and Renaming the Columns
- Manipulating Columns
- Filtering Data from Spark Dataframes
- Dropping Columns and Rows from Spark Dataframes
- Sorting Data in Spark Dataframes
- Performing Aggregations on Spark Dataframes
- Joining Spark Dataframes
- Reading Data from Spark Dataframes into Files
- Writing Data from Files into Spark Dataframes
- Partitioning Spark Dataframes
- Working with Spark SQL Functions
- Spark Architecture Concepts
Who should attend
Data Scientists, statisticians, and information technology engineers who want to get started with Spark and need to make better use of their data.
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