PySpark Training
Level
IntermediateDuration
32h / 4 daysDate
Individually arrangedPrice
Individually arrangedPySpark Training
PySpark is a library for Apache Spark that enables the creation and execution of distributed tasks on clusters using Python. It provides an API for working with distributed data through Spark and offers access to all Spark functions, such as mapping, aggregation, filtering, and grouping of data. PySpark is widely used in Big Data, data analysis, and machine learning.
What You Will Learn
- Understand the application of Big Data in organizations
- Learn fundamental concepts related to working with data in Apache Spark
- Master Spark Project Core and Spark SQL
- Apply Spark ML in practical scenarios
Who is this training for?
Developers with knowledge of Python-
Individuals who want to learn one of the most popular tools for data processing
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Data analysts with Python experience
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Data scientists
Training Program
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Module 1 – Apache Spark Architecture
- Understanding Spark components and their roles
- Positioning Apache Spark within the Big Data landscape
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Module 2 – RDDs (Resilient Distributed Datasets)
- Core concept for distributed data processing in Apache Spark
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Module 3 – Differences Between Python Syntax and PySpark
- Comparing RDDs and Pandas DataFrames
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Module 4 – Variables, Partitioning, and Core Spark Concepts
- Deep dive into Spark’s foundational elements
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Module 5 – Spark SQL
- Working with DataFrames
- Syntax, schemas, and aggregations
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Module 6 – Spark ML (Machine Learning)
- Introduction to machine learning capabilities in Spark
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Module 7 – Prototyping
- Developing and testing data processing workflows
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Module 8 – Running and Managing Jobs on a Cluster
- Best practices for job execution and cluster management
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Module 9 – Testing Processes
- Ensuring reliability and correctness of data pipelines
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Module 10 – Optimization and Task Configuration
- Techniques for improving performance and resource utilization
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Module 11 – Spark Structured Streaming
- Handling real-time data streams with Apache Spark
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Module 12 – Q&A Session
- Addressing participant questions and clarifications
