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PySpark
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4.3 ( 1459 Ratings )
PySpark is an open-source, Python-based library and framework for big data processing and analytics. It is part of the Apache Spark project, which is a powerful and fast cluster computing system designed for distributed data processing. It is commonly used in big data analytics, data engineering, and machine learning applications.
Overview
The Syllabus
Curriculum Designed by Experts
Module 1: Introduction to PySpark
What is PySpark?
• PySpark vs. Spark: Understanding the difference
• Spark architecture and components
• Setting up PySpark environment
• Creating RDDs (Resilient Distributed Datasets)
• Transformations and actions in RDDs
• Hands-on exercises
Module 2: PySpark DataFrames
• Introduction to DataFrames
• Creating DataFrames from various data sources (CSV, JSON, Parquet, etc.)
• Basic DataFrame operations (filtering, selecting, aggregating)
• Handling missing data
• DataFrame joins and unions
• Hands-on exercises
Module 3: PySpark SQL
• Introduction to Spark SQL
• Creating temporary views and global temporary views
• Executing SQL queries on DataFrames
• Performance optimization techniques
• Working with user-defined functions (UDFs)
• Hands-on exercises
Module 4: PySpark MLlib (Machine Learning Library)
• Introduction to MLlib
• Data preprocessing and feature engineering
• Building and evaluating regression models
• Classification algorithms and evaluation metrics
• Clustering and collaborative filtering
• Model selection and tuning
• Hands-on exercises with real-world datasets
Module 5: PySpark Streaming
• Introduction to Spark Streaming
• DStream (Discretized Stream) and input sources
• Windowed operations and stateful transformations
• Integration with Kafka for real-time data processing
• Hands-on exercise
Module 6: PySpark and Big Data Ecosystem
• Overview of Hadoop, HDFS, and YARN
• Integrating PySpark with Hadoop and Hive
• PySpark and NoSQL databases (e.g., HBase)
• Spark on Kubernetes
• Hands-on exercises
Module 7: PySpark Optimization and Best Practices
• Understanding Spark’s execution plan
• Performance tuning and optimization techniques
• Broadcast variables and accumulators
• PySpark configuration and memory management
• Coding best practices for PySpark
• Hands-on exercises
Module 8: Advanced PySpark Concepts (Optional)
• Spark GraphX for graph processing
• SparkR: R language integration with PySpark
• Deep learning with Spark using TensorFlow or Keras
• PySpark and SparkML integration
• Hands-on exercises and mini-projects
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Red Hat Linux Course Projects
Infrastructure Provisioning
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Implementing automated infrastructure provisioning and configuration management using Ansible. This may include setting up servers, networking devices, and other infrastructure components using playbooks and roles.
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Automating the deployment and orchestration of applications across development, testing, and production environments. This could involve deploying web servers, databases. middleware, and other application components using Ansible
Continuous Integration
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Integrating Ansible into CI/CD pipelines to automate software. build, test, and deployment processes. This may include automating the creation of build artifacts, running tests, and deploying applications to various environments.