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Big Data

Pyspark

Pysparks offers introduction to programming Spark with Python, equipping you with the skills to harness the full…

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PySpark presents a broad perspective of the Spark stack; the essential topics covered relates to the ability to program Spark using Python. With the help of PySpark, you can get acquainted with the functions of Spark and Python together that are really powerful. This training will arm you with all the necessary tools that can help you be a great PySpark developer.

More specifically, this PySpark course online is ideal for learners who want the freedom of learning on their own schedule and on their own computer. Regardless, whether you are practicing PySpark for the first time or you really want to know about next level, the course provides rich information and experience that really helps to be professional in big data and analytics career.

What Will You Learn?

Spark Architecture and Components: Understand the core architecture of Apache Spark, including its components like Spark SQL, Spark Streaming, and MLlib, and how they interact within the ecosystem.
PySpark DataFrames and RDDs: Gain expertise in working with Spark DataFrames and Resilient Distributed Datasets (RDDs) for handling and processing large-scale data efficiently.
Data Manipulation and Transformation: Learn techniques for manipulating and transforming data using PySpark, including filtering, aggregating, and joining datasets.
Spark SQL and Query Optimization: Master the use of Spark SQL for querying data and learn strategies for optimizing queries to improve performance.
Machine Learning with MLlib: Explore Spark's MLlib library for implementing machine learning algorithms and models, and understand how to scale these processes for large datasets.
Stream Processing: Discover how to handle real-time data streams with Spark Streaming, including processing, analyzing, and managing streaming data efficiently.

Course Curriculum

Module 1: Introduction to PySpark

1.1 What is PySpark?
1.1.1 Overview
:: PySpark vs. Apache Spark
:: Key features and benefits of PySpark
1.1.2 Architecture
:: Spark architecture overview
:: Components: Spark Core, Spark SQL, DataFrames, Datasets, MLlib, Spark Streaming
1.2 Setting Up PySpark
1.2.1 Installation
:: Installing PySpark on local machine (using pip or conda)
:: Setting up a PySpark development environment (Jupyter Notebooks, IDEs)
1.2.2 Configuration
:: Configuring Spark with Hadoop
:: Setting up Spark clusters on local, standalone, and cloud environments
1.3 PySpark Basics
1.3.1 Understanding RDDs (Resilient Distributed Datasets)
:: RDD concepts: creation, transformations, actions
:: Working with RDDs in PySpark
1.3.2 Introduction to DataFrames
:: DataFrame creation and operations
:: Difference between RDDs and DataFrames
1.4 Hands-On Exercise: Getting Started with PySpark
1.4.1 Environment Setup
:: Install PySpark and set up a local environment
1.4.2 Basic Operations
:: Create and manipulate RDDs and DataFrames

Module 2: DataFrames and SQL

2.1 PySpark DataFrames
2.1.1 Creating DataFrames
:: From existing RDDs, CSV files, JSON, Parquet
:: Schema inference and definition
2.1.2 DataFrame Operations
:: Selecting, filtering, and sorting data
:: Aggregations and group-by operations
:: Joins and set operations (union, intersection, except)
2.2 PySpark SQL
2.2.1 Introduction to Spark SQL
:: Using SQL queries with PySpark
:: Registering DataFrames as SQL tables
2.2.2 Advanced SQL Queries
:: Window functions, subqueries
:: User-Defined Functions (UDFs) and their applications
2.3 Performance Optimization
2.3.1 Caching and Persistence
:: Using caching to optimize DataFrame operations
2.3.2 Query Optimization
:: Understanding the Catalyst Optimizer and Tungsten execution engine
2.4 Hands-On Exercise: Working with DataFrames and SQL
2.4.1 Data Operations
:: Perform various DataFrame operations and SQL queries
2.4.2 Optimization
:: Apply caching and performance tuning techniques

Module 3: Advanced Data Processing

3.1 Data Cleaning and Transformation
3.1.1 Data Cleaning Techniques
:: Handling missing values, duplicates, and outliers
3.1.2 Data Transformation
:: Using functions for data transformation (map, flatMap, filter, etc.)
:: String operations and regular expressions
3.2 Data Aggregation and Analysis
3.2.1 Aggregation Techniques
:: Using groupBy and aggregation functions for summarizing data
3.2.2 Advanced Analytics
:: Performing complex aggregations and computations
3.3 Data Integration
3.3.1 Integration with External Data Sources
:: Reading from and writing to various data sources (HDFS, S3, RDBMS)
3.3.2 Handling Streaming Data
:: Introduction to Spark Streaming with PySpark
3.4 Hands-On Exercise: Data Processing and Analysis
3.4.1 Data Cleaning
:: Apply data cleaning techniques to a real-world dataset
3.4.2 Aggregation and Analysis
:: Perform aggregation and analysis tasks

Module 4: Machine Learning with PySpark

4.1 Introduction to MLlib
4.1.1 Overview
:: Introduction to Spark MLlib and its capabilities
4.1.2 Data Preparation
:: Feature extraction, transformation, and selection
4.2 Building Machine Learning Models
4.2.1 Classification and Regression
:: Building and evaluating classification and regression models
4.2.2 Clustering and Recommendation
:: Implementing clustering algorithms (e.g., K-means)
:: Building recommendation systems using collaborative filtering
4.3 Model Tuning and Evaluation
4.3.1 Hyperparameter Tuning
:: Using GridSearch and Cross-validation
4.3.2 Model Evaluation Metrics
:: Understanding precision, recall, F1 score, and AUC-ROC
4.4 Hands-On Exercise: Machine Learning with MLlib
4.4.1 Model Building
:: Build and evaluate machine learning models using PySpark MLlib
4.4.2 Tuning and Evaluation
:: Apply hyperparameter tuning and evaluate model performance

Module 5: Advanced Topics and Best Practices

5.1 Advanced Spark Features
5.1.1 Broadcast Variables
:: Using broadcast variables for efficient data sharing
5.1.2 Accumulators
:: Implementing accumulators for aggregating information
5.2 Performance Tuning
5.2.1 Optimizing Spark Jobs
:: Performance tuning techniques for Spark jobs
5.2.2 Resource Management
:: Configuring and managing Spark resources effectively
5.3 Security and Compliance
5.3.1 Data Security
:: Ensuring data security and privacy in PySpark applications
5.3.2 Compliance
:: Implementing data governance and compliance measures
5.4 Hands-On Exercise: Advanced PySpark Topics
5.4.1 Advanced Features
:: Implement broadcast variables and accumulators
5.4.2 Tuning and Security
:: Optimize PySpark jobs and apply security measures

Module 6: Real-World Projects and Case Studies

6.1 Industry Case Studies
6.1.1 Applications
:: Case studies from various industries (e.g., finance, healthcare, retail)
6.1.2 Lessons Learned
:: Key takeaways and best practices from real-world PySpark implementations
6.2 Project
6.2.1 Project Development
:: Develop a comprehensive PySpark-based solution involving data processing, analysis, and machine learning
6.2.2 Presentation and Review
:: Present the project, receive feedback, and iterate on the solution
6.3 Future Trends and Continued Learning
6.3.1 Emerging Trends
:: Trends in big data, cloud computing, and AI/ML
6.3.2 Additional Resources
:: Recommended resources for advanced learning and certifications

A. Course Resources

A.1 Documentation
:: Official PySpark and Apache Spark documentation
A.2 Code Examples
:: GitHub repository with sample code and project templates
A.3 Tools and Libraries
:: inks to essential tools and libraries used in PySpark
A.4 Community
:: Access to forums, discussion groups, and PySpark community resources

B. Assignments and Evaluation

B.1 Weekly Assignments
:: Practical exercises and tasks for each module
B.2 Hands-On Exercises
:: Real-world scenarios and project work
B.3 Mid-Course Project
:: Develop a small-scale PySpark application or solution
B.4 Final Capstone Project
:: Create a comprehensive PySpark-based solution with data processing, analysis, and machine learning components
B.5 Quizzes and Assessments
:: Regular quizzes and assessments to evaluate understanding

No Data Available in this Section

Pyspark

What Will You Learn?

Course Curriculum

Module 1: Introduction to PySpark

1.1 What is PySpark?

1.1.1 Overview

:: PySpark vs. Apache Spark

:: Key features and benefits of PySpark

1.1.2 Architecture

:: Spark architecture overview

:: Components: Spark Core, Spark SQL, DataFrames, Datasets, MLlib, Spark Streaming

1.2 Setting Up PySpark

1.2.1 Installation

:: Installing PySpark on local machine (using pip or conda)

:: Setting up a PySpark development environment (Jupyter Notebooks, IDEs)

1.2.2 Configuration

:: Configuring Spark with Hadoop

:: Setting up Spark clusters on local, standalone, and cloud environments

1.3 PySpark Basics

1.3.1 Understanding RDDs (Resilient Distributed Datasets)

:: RDD concepts: creation, transformations, actions

:: Working with RDDs in PySpark

1.3.2 Introduction to DataFrames

:: DataFrame creation and operations

:: Difference between RDDs and DataFrames

1.4 Hands-On Exercise: Getting Started with PySpark

1.4.1 Environment Setup

:: Install PySpark and set up a local environment

1.4.2 Basic Operations

:: Create and manipulate RDDs and DataFrames

Module 2: DataFrames and SQL

2.1 PySpark DataFrames

2.1.1 Creating DataFrames

:: From existing RDDs, CSV files, JSON, Parquet

:: Schema inference and definition

2.1.2 DataFrame Operations

:: Selecting, filtering, and sorting data

:: Aggregations and group-by operations

:: Joins and set operations (union, intersection, except)

2.2 PySpark SQL

2.2.1 Introduction to Spark SQL

:: Using SQL queries with PySpark

:: Registering DataFrames as SQL tables

2.2.2 Advanced SQL Queries

:: Window functions, subqueries

:: User-Defined Functions (UDFs) and their applications

2.3 Performance Optimization

2.3.1 Caching and Persistence

:: Using caching to optimize DataFrame operations

2.3.2 Query Optimization

:: Understanding the Catalyst Optimizer and Tungsten execution engine

2.4 Hands-On Exercise: Working with DataFrames and SQL

2.4.1 Data Operations

:: Perform various DataFrame operations and SQL queries

2.4.2 Optimization

:: Apply caching and performance tuning techniques

Module 3: Advanced Data Processing

3.1 Data Cleaning and Transformation

3.1.1 Data Cleaning Techniques

:: Handling missing values, duplicates, and outliers

3.1.2 Data Transformation

:: Using functions for data transformation (map, flatMap, filter, etc.)

:: String operations and regular expressions

3.2 Data Aggregation and Analysis

3.2.1 Aggregation Techniques

:: Using groupBy and aggregation functions for summarizing data

3.2.2 Advanced Analytics

:: Performing complex aggregations and computations

3.3 Data Integration

3.3.1 Integration with External Data Sources

:: Reading from and writing to various data sources (HDFS, S3, RDBMS)

3.3.2 Handling Streaming Data

:: Introduction to Spark Streaming with PySpark

3.4 Hands-On Exercise: Data Processing and Analysis

3.4.1 Data Cleaning

:: Apply data cleaning techniques to a real-world dataset

3.4.2 Aggregation and Analysis

:: Perform aggregation and analysis tasks

Module 4: Machine Learning with PySpark

4.1 Introduction to MLlib