PySpark training is designed to provide participants with a deep understanding of how to process large datasets using the Apache Spark framework with Python. It covers the fundamentals of distributed computing, Spark’s architecture, and PySpark’s API, making it perfect for data engineers, analysts, and anyone looking to build scalable data processing pipelines. Participants will learn to leverage PySpark for data wrangling, analysis, and machine learning at scale.

Why should you choose Nisa For PySpark Training?

Nisa Trainings is the best online training platform for conducting one-on-one interactive live sessions with a 1:1 student-teacher ratio. You can gain hands-on experience by working on near-real-time projects under the guidance of our experienced faculty. We support you even after the completion of the course and happy to clarify your doubts anytime. Our teaching style at Nisa Trainings is entirely hands-on. You’ll have access to our desktop screen and will be actively conducting hands-on labs on your desktop.

Job Assistance

If you face any problem while working on PySpark Course, then Nisa Trainings is simply a Call/Text/Email away to assist you. We offer Online Job Support for professionals to assist them and to solve their problems in real-time.

The Process we follow for our Online Job Support Service:

• We receive your inquiry for Online Job
• We will arrange a telephone call with our consultant to grasp your complete requirement and the tools you’re
• If our consultant is 100% confident in taking up your requirement and when you are also comfortable with our consultant, we will only agree to provide service. And then you have to make the payment to get the service from
• We will fix the timing for Online Job Support as mutually agreed by you and our consultant.

Course Information

PySpark Training

Duration: 25 Hours

Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)

Training Method: Instructor Led Online One-on-One Live Interactive

Sessions.

COURSE CONTENT :

Module 1: Introduction to PySpark

• Overview of Big Data and Hadoop ecosystem
• Introduction to Apache Spark and its components
• Setting up Spark and PySpark environment
• Introduction to SparkContext and RDDs (Resilient Distributed Datasets)
• PySpark DataFrame API basics
• Understanding Spark execution model and cluster architecture

Module 2: PySpark DataFrames and SQL

• Introduction to Spark DataFrames
• Creating and manipulating DataFrames
• PySpark SQL for querying DataFrames
• Handling missing data and applying transformations
• Spark SQL for advanced data manipulation
• Optimizing DataFrame performance

Module 3: Data Processing with PySpark

• Reading and writing data from different file formats (CSV, Parquet, JSON, etc.)
• Data cleaning and preprocessing in PySpark
• Filtering, selecting, and grouping data
• Aggregation functions and window functions
• Merging and joining DataFrames

Module 4: Working with Spark RDDs

• Introduction to RDDs and their limitations
• RDD transformations and actions
• Converting between RDDs and DataFrames
• When to use RDDs vs DataFrames

Module 5: PySpark Machine Learning

• Overview of Spark MLlib
• Building and evaluating machine learning models with PySpark
• Classification and regression with PySpark MLlib
• Feature engineering, scaling, and transformations
• Building machine learning pipelines in Spark

Module 6: Advanced PySpark Techniques

• Understanding and optimizing Spark performance
• Spark configurations and tuning Spark jobs
• Caching and persistence in PySpark
• Understanding partitions and parallelism
• Handling skewed data and optimizing data shuffling

Module 7: PySpark Streaming

• Introduction to Spark Streaming
• Setting up and processing real-time data
• Working with DStreams
• Handling windowed computations
• Processing streaming data with PySpark

Module 8: PySpark in Production

• Best practices for deploying PySpark applications
• Running PySpark on cloud platforms (e.g., AWS, Databricks)
• Cluster management with Spark
• Monitoring and debugging Spark jobs