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Big Data with Apache Spark 3 and Python: From Zero to Expert

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Complete bootcamp to learn PySpark, Databricks, Spark Machine Learning, Advanced Analytics, Koalas and Spark Streaming


1. Spark Fundamentals
  • 1. How to get the most out of this course.html
  • 2.1 Big Data with Apache Spark 3 and Python From Zero to Expert.pdf
  • 2.2 Entrega.rar
  • 2. Course material.html
  • 3. Spark Fundamentals
  • 4. Apache Spark execution
  • 5. Apache Spark ecosystem and documentation
  • 6. PySpark operation, cluster administration and architecture

  • 2. Installing Apache Spark locally
  • 1. Download Spark, Java and Anaconda
  • 2. Setting environment variables
  • 3. Running Spark in Prompt and Jupyter Notebook
  • 4. Fixing common problems.html

  • 3. Basic Features and RDDs
  • 1.1 PySpark RDDs.pdf
  • 1. PySpark Cheat Sheet.html
  • 2. RDD Fundamentals
  • 3. Initialize PySpark with SparkSession and the SparkContext
  • 4. Transformations in RDDs like map, filter, flatMap and distinct
  • 5. Transformations in RDDs like reduceByKey, groupByKey or sortByKey
  • 6. RDD actions such as count, first, collect or take

  • 4. Spark DataFrames and Apache Spark SQL
  • 1.1 PySpark Sql Basics Cheat Sheet.pdf
  • 1. PySpark Cheatsheet SQL.html
  • 2. Fundamentals and advantages of DataFrames
  • 3. Characteristics of DataFrames and data sources
  • 4. Creating DataFrames in PySpark
  • 5. Operations with PySpark DataFrames
  • 6. Different types of joins in DataFrames
  • 7. SQL queries in PySpark
  • 8. Advanced features for loading and exporting data in PySpark

  • 5. Advanced features in Apache Spark
  • 1. Funciones avanzadas y optimizacion del rendimiento
  • 2. BroadCast Join and caching
  • 3. User Defined Functions (UDF) and advanced SQL functions
  • 4. Handling and imputation of missing values
  • 5. Partitioning and catalog of APIs
  • 6. Practical Exercise Advanced Analytics with Apache Spark.html

  • 6. Advanced Analytics with Apache Spark
  • 1. Introduction to advanced analytics with Spark
  • 2. Data loading and data schema modification
  • 3. Inspect data in PySpark
  • 4. Column transformation in PySpark
  • 5. Advanced missing data imputation in PySpark
  • 6. Data selection with PySpark and PySpark SQL
  • 7. Data visualization and graph generation in PySpark
  • 8. Persist data with PySpark

  • 7. Kolas The Apache Spark Pandas API
  • 1. Spark Koalas Fundamentals
  • 2. Feature Engineering with Koalas
  • 3. Creating DataFrames with Koalas
  • 4. Data manipulation and DataFrames with Koalas
  • 5. Working with missing data in Koalas
  • 6. Data visualization and graph generation with Koalas
  • 7. Importing and exporting data with Koalas
  • 8. Hands-on exercise with Koalas.html

  • 8. Machine Learning with Apache Spark
  • 1. Fundamentals of Machine Learning with Spark
  • 2. Spark Machine Learning Components
  • 3. Stages of developing a Machine Learning model
  • 4. Import data and exploratory data analysis (EDA)
  • 5. Data preprocessing with PySpark
  • 6. Training the machine learning model in PySpark
  • 7. Evaluation of the Machine Learning model

  • 9. Spark Streaming
  • 1. Practical example of counting words with Spark Streaming
  • 2. Spark Streaming Configurations Output Modes and Operation Types
  • 3. Time Window Operations in Spark Streaming
  • 4. Spark Streaming Capabilities
  • 5. Use case Real-time bank fraud detection (Part I)
  • 6. Use case Real-time bank fraud detection (Part II)
  • 7. Spark Streaming Exercise.html

  • 10. Introduction to Databricks
  • 1. Introduction to Databricks
  • 2. Databricks Terminology and Databricks Community
  • 3. Delta Lake
  • 4. Create a free Databricks account

  • 11. Apache Spark on Databricks
  • 1. Introduction to the Databricks environment
  • 2. Getting started with Databricks
  • 3. Creating and saving DataFrames in Databricks
  • 4. Data transformation and visualization in Databricks
  • 5. Use case Population data analytics

  • 12. Machine Learning in Databricks
  • 1. Import and exploratory analysis of the data
  • 2. Variable preprocessing with PySpark and Databricks
  • 3. Definition of the Machine Learning model and development of the Pipeline
  • 4. Model evaluation with PySpark and Databricks
  • 5. Hyperparameter tuning and registration in MLFlow
  • 6. Predictions with new data and visualization of the results

  • 13. Additional material
  • 1.1 PySpark_Cheat_Sheet_Python.pdf
  • 1. Additional Resources Complete Guide to Spark.html
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    شناسه: 2552
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    مدت زمان: 258 دقیقه
    تاریخ انتشار: 28 دی 1401
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