Udemy - PySpark - Build DataFrames with Python, Apache Spark and SQL

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PySpark - Build DataFrames with Python, Apache Spark and SQL [TutsNode.com] - PySpark - Build DataFrames with Python, Apache Spark and SQL 3. 02 Introduction to Python (Prerequisite)
  • 15. Classes And Objects.mp4 (223.4 MB)
  • 11. If Statement Examples.srt (34.3 KB)
  • 15. Classes And Objects.srt (53.9 KB)
  • 12. Loops.srt (46.4 KB)
  • 4. Operators.srt (44.4 KB)
  • 6. List Examples.srt (31.0 KB)
  • 2. Variables.srt (30.6 KB)
  • 16. Inheritance Examples.srt (27.4 KB)
  • 13. Functions.srt (27.0 KB)
  • 8. Dictionaries Examples.srt (22.6 KB)
  • 14. Parameters And Return Values Examples.srt (21.8 KB)
  • 17. Static Members Examples.srt (16.5 KB)
  • 3. Type Conversion Examples.srt (16.2 KB)
  • 1. Intro To Course And Python.srt (15.3 KB)
  • 7. Tuples Examples.srt (13.5 KB)
  • 9. Ranges Examples.srt (13.5 KB)
  • 5. Collections.srt (13.2 KB)
  • 10. Conditionals.srt (10.5 KB)
  • 18. Summary And Outro.srt (6.1 KB)
  • 12. Loops.mp4 (167.1 MB)
  • 4. Operators.mp4 (161.4 MB)
  • 16. Inheritance Examples.mp4 (130.7 MB)
  • 11. If Statement Examples.mp4 (118.0 MB)
  • 6. List Examples.mp4 (109.9 MB)
  • 2. Variables.mp4 (106.5 MB)
  • 8. Dictionaries Examples.mp4 (87.3 MB)
  • 13. Functions.mp4 (86.5 MB)
  • 14. Parameters And Return Values Examples.mp4 (78.6 MB)
  • 17. Static Members Examples.mp4 (78.5 MB)
  • 3. Type Conversion Examples.mp4 (59.8 MB)
  • 1. Intro To Course And Python.mp4 (57.3 MB)
  • 9. Ranges Examples.mp4 (53.2 MB)
  • 7. Tuples Examples.mp4 (52.5 MB)
  • 5. Collections.mp4 (41.6 MB)
  • 10. Conditionals.mp4 (38.1 MB)
  • 18. Summary And Outro.mp4 (20.9 MB)
4. 03 Build DataFrames from Spreadsheets with PySpark
  • 8.1 Source Files.zip (59.3 KB)
  • 6. Perform Dataframe Queries.srt (17.8 KB)
  • 7. Build SQL Queries With Spark.srt (8.6 KB)
  • 5. Perform Dataframe Math Operations.srt (8.6 KB)
  • 2. Load Data As A CSV.srt (7.5 KB)
  • 4. Format Dataframe Table.srt (6.2 KB)
  • 3. Perform Basic Dataframe Operations.srt (5.4 KB)
  • 1. Start A Spark Session.srt (4.9 KB)
  • 8. Source Files.html (0.0 KB)
  • 6. Perform Dataframe Queries.mp4 (88.0 MB)
  • 5. Perform Dataframe Math Operations.mp4 (62.8 MB)
  • 7. Build SQL Queries With Spark.mp4 (44.2 MB)
  • 2. Load Data As A CSV.mp4 (41.2 MB)
  • 4. Format Dataframe Table.mp4 (39.0 MB)
  • 3. Perform Basic Dataframe Operations.mp4 (32.3 MB)
  • 1. Start A Spark Session.mp4 (23.6 MB)
1. 00 Course Overview
  • 4. What Is A Dataframe.srt (2.2 KB)
  • 6. Source Files.html (0.0 KB)
  • 3. What Are Resilient Distributed Datasets.srt (1.3 KB)
  • 5. What You'll Need.srt (2.2 KB)
  • 2. What Is Apache Spark.srt (3.2 KB)
  • 1. Project Preview.srt (3.2 KB)
  • 1. Project Preview.mp4 (26.3 MB)
  • 2. What Is Apache Spark.mp4 (13.5 MB)
  • 5. What You'll Need.mp4 (8.6 MB)
  • 4. What Is A Dataframe.mp4 (8.6 MB)
  • 3. What Are Resilient Distributed Datasets.mp4 (7.4 MB)
  • 6.1 Source Files.zip (3.0 MB)
2. 01 Mammoth Interactive Courses Introduction
  • 1. About Mammoth Interactive.srt (1.4 KB)
  • 3. Source Files.html (0.0 KB)
  • 2. How To Learn Online Effectively.srt (19.8 KB)
  • 2. How To Learn Online Effectively.mp4 (74.1 MB)
  • 1. About Mammoth Interactive.mp4 (8.7 MB)
  • 3.1 Source Files.zip (2.1 MB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
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Description


Description

This course covers all the fundamentals about Apache Spark streaming with Python and teaches you everything you need to know about developing Spark streaming applications using PySpark, the Python API for Spark. At the end of this course, you will gain in-depth knowledge about Spark streaming and general big data manipulation skills to help your company to adapt Spark Streaming for building big data processing pipelines and data analytics applications. This course will be absolutely critical to anyone trying to make it in data science today.

Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!

This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we’ve done that we’ll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way, you’ll have exercises and Mock Consulting Projects that put you right into a real-world situation where you need to use your new skills to solve a real problem!

We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume! This course also has a full 30-day money-back guarantee and comes with a LinkedIn Certificate of Completion!

If you’re ready to jump into the world of Python, Spark, and Big Data, this is the course for you!
Who this course is for:

Someone who knows Python and would like to learn how to use it for Big Data
Someone who is very familiar with another programming language and needs to learn Spark
Developers transferring from other languages
Python Developers looking to get better at Data Streaming
Spark Developers eager to expand their skills.

Requirements

No necessary experience needed

Last Updated 2/2021



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Udemy - PySpark - Build DataFrames with Python, Apache Spark and SQL

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Udemy - PySpark - Build DataFrames with Python, Apache Spark and SQL

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