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1977
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The Nude Bomb (1980)
spidey and his amazing friends s01e21
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1977
▲
Udemy - Taming Big Data with Apache Spark 3 and Python – Hands On!
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Udemy - Taming Big Data with Apache Spark 3 and Python – Hands On! (Size: 1.9 GB)
| 1. Introducing Elastic MapReduce.mp4 | 29 MB | ||
| 1. Introducing Elastic MapReduce.srt | 8.8 KB | ||
| 1. Introducing MLLib.mp4 | 32.2 MB | ||
| 1. Introducing MLLib.srt | 13.6 KB | ||
| 1. Introducing SparkSQL.mp4 | 24.4 MB | ||
| 1. Introducing SparkSQL.srt | 10.2 KB | ||
| 1. Introduction to Spark.mp4 | 34 MB | ||
| 1. Introduction to Spark.srt | 16.9 KB | ||
| 1. Introduction.mp4 | 9.1 MB | ||
| 1. Introduction.srt | 4.2 KB | ||
| 1. Learning More about Spark and Data Science.mp4 | 69.8 MB | ||
| 1. Learning More about Spark and Data Science.srt | 8 KB | ||
| 1. [Activity] Find the Most Popular Movie.mp4 | 31.2 MB | ||
| 1. [Activity] Find the Most Popular Movie.srt | 9.1 KB | ||
| 1.1 popular-movies.py.py | 512 B | ||
| 10. [Activity] Improving the Word Count Script with Regular Expressions.mp4 | 23.8 MB | ||
| 10. [Activity] Improving the Word Count Script with Regular Expressions.srt | 7.4 KB | ||
| 10. [Exercise] Improve the Quality of Similar Movies.mp4 | 20.6 MB | ||
| 10. [Exercise] Improve the Quality of Similar Movies.srt | 5.9 KB | ||
| 10.1 word-count-better.py.py | 512 B | ||
| 11. [Activity] Sorting the Word Count Results.mp4 | 32.9 MB | ||
| 11. [Activity] Sorting the Word Count Results.srt | 12.6 KB | ||
| 11.1 word-count-better-sorted.py.py | 716 B | ||
| 12. Tally up amount spent by customer using Spark.html | 102 B | ||
| 13. Sort your results by amount spent per customer.html | 102 B | ||
| 2. Bonus Lecture More courses to explore!.html | 6.8 KB | ||
| 2. Executing SQL commands and SQL-style functions on a DataFrame.mp4 | 31.1 MB | ||
| 2. Executing SQL commands and SQL-style functions on a DataFrame.srt | 14 KB | ||
| 2. How to Use This Course.mp4 | 11.5 MB | ||
| 2. How to Use This Course.srt | 3.1 KB | ||
| 2. The Resilient Distributed Dataset (RDD).mp4 | 36 MB | ||
| 2. The Resilient Distributed Dataset (RDD).srt | 18.8 KB | ||
| 2. [Activity] Setting up your AWS Elastic MapReduce Account and Setting Up PuTTY.mp4 | 65.6 MB | ||
| 2. [Activity] Setting up your AWS Elastic MapReduce Account and Setting Up PuTTY.srt | 16.7 KB | ||
| 2. [Activity] Use Broadcast Variables to Display Movie Names Instead of ID Numbers.mp4 | 38.9 MB | ||
| 2. [Activity] Use Broadcast Variables to Display Movie Names Instead of ID Numbers.srt | 13.3 KB | ||
| 2. [Activity] Using MLLib to Produce Movie Recommendations.mp4 | 16.3 MB | ||
| 2. [Activity] Using MLLib to Produce Movie Recommendations.srt | 4.7 KB | ||
| 2.1 movie-recommendations-als.py.py | 1.4 KB | ||
| 2.1 popular-movies-nicer.py.py | 921 B | ||
| 2.1 spark-sql.py.py | 1.1 KB | ||
| 3. Analyzing the ALS Recommendations Results.mp4 | 35.1 MB | ||
| 3. Analyzing the ALS Recommendations Results.srt | 8.6 KB | ||
| 3. Find the Most Popular Superhero in a Social Graph.mp4 | 25 MB | ||
| 3. Find the Most Popular Superhero in a Social Graph.srt | 7.3 KB | ||
| 3. Partitioning.mp4 | 24.6 MB | ||
| 3. Partitioning.srt | 7 KB | ||
| 3. Ratings Histogram Walkthrough.mp4 | 80 MB | ||
| 3. Ratings Histogram Walkthrough.srt | 22.3 KB | ||
| 3. Udemy 101 Getting the Most From This Course.mp4 | 19.7 MB | ||
| 3. Udemy 101 Getting the Most From This Course.srt | 4 KB | ||
| 3. Using DataFrames instead of RDD's.mp4 | 19.8 MB | ||
| 3. Using DataFrames instead of RDD's.srt | 10 KB | ||
| 3.1 Marvel Graph.txt | 1.6 MB | ||
| 3.1 popular-movies-dataframe.py.py | 1.4 KB | ||
| 3.1 ratings-counter.py.py | 409 B | ||
| 3.2 most-popular-superhero.py.py | 921 B | ||
| 3.3 Marvel Names.txt | 343.6 KB | ||
| 4. Create Similar Movies from One Million Ratings - Part 1.mp4 | 28.8 MB | ||
| 4. Create Similar Movies from One Million Ratings - Part 1.srt | 8.4 KB | ||
| 4. KeyValue RDD's, and the Average Friends by Age Example.mp4 | 61.7 MB | ||
| 4. KeyValue RDD's, and the Average Friends by Age Example.srt | 25.8 KB | ||
| 4. Using DataFrames with MLLib.mp4 | 28.7 MB | ||
| 4. Using DataFrames with MLLib.srt | 12.9 KB | ||
| 4. [Activity] Run the Script - Discover Who the Most Popular Superhero is!.mp4 | 29 MB | ||
| 4. [Activity] Run the Script - Discover Who the Most Popular Superhero is!.srt | 9.2 KB | ||
| 4. [Activity]Getting Set Up Installing Python, a JDK, Spark, and its Dependencies..mp4 | 223.1 MB | ||
| 4. [Activity]Getting Set Up Installing Python, a JDK, Spark, and its Dependencies..srt | 24.9 KB | ||
| 4.1 Apache Spark.html | 102 B | ||
| 4.1 Marvel Graph.txt | 1.6 MB | ||
| 4.1 movie-similarities-1m.py.py | 3.6 KB | ||
| 4.1 spark-linear-regression.py.py | 2 KB | ||
| 4.2 Marvel Names.txt | 343.6 KB | ||
| 4.2 regression.txt.txt | 11.7 KB | ||
| 4.2 winutils.exe.html | 102 B | ||
| 4.3 JDK.html | 102 B | ||
| 4.3 most-popular-superhero.py.py | 921 B | ||
| 5. Spark Streaming.mp4 | 45 MB | ||
| 5. Spark Streaming.srt | 14.3 KB | ||
| 5. Superhero Degrees of Separation Introducing Breadth-First Search.mp4 | 38.2 MB | ||
| 5. Superhero Degrees of Separation Introducing Breadth-First Search.srt | 13.8 KB | ||
| 5. [Activity] Create Similar Movies from One Million Ratings - Part 2.mp4 | 60.1 MB | ||
| 5. [Activity] Create Similar Movies from One Million Ratings - Part 2.srt | 17.5 KB | ||
| 5. [Activity] Installing the MovieLens Movie Rating Dataset.mp4 | 7.9 MB | ||
| 5. [Activity] Installing the MovieLens Movie Rating Dataset.srt | 5.6 KB | ||
| 5. [Activity] Running the Average Friends by Age Example.mp4 | 47.5 MB | ||
| 5. [Activity] Running the Average Friends by Age Example.srt | 9.2 KB | ||
| 5.1 fakefriends.csv.html | 102 B | ||
| 5.2 friends-by-age.py.py | 614 B | ||
| 6. Create Similar Movies from One Million Ratings - Part 3.mp4 | 30.7 MB | ||
| 6. Create Similar Movies from One Million Ratings - Part 3.srt | 6.3 KB | ||
| 6. Filtering RDD's, and the Minimum Temperature by Location Example.mp4 | 30.9 MB | ||
| 6. Filtering RDD's, and the Minimum Temperature by Location Example.srt | 13.1 KB | ||
| 6. Superhero Degrees of Separation Accumulators, and Implementing BFS in Spark.mp4 | 25.9 MB | ||
| 6. Superhero Degrees of Separation Accumulators, and Implementing BFS in Spark.srt | 11.1 KB | ||
| 6. [Activity] Run your first Spark program! Ratings histogram example..mp4 | 66.2 MB | ||
| 6. [Activity] Run your first Spark program! Ratings histogram example..srt | 11.3 KB | ||
| 6. [Activity] Structured Streaming in Python.mp4 | 81 MB | ||
| 6. [Activity] Structured Streaming in Python.srt | 15 KB | ||
| 6.1 min-temperatures.py.py | 716 B | ||
| 6.1 ratings-counter.py.py | 409 B | ||
| 6.1 structured-streaming.py.py | 1.8 KB | ||
| 6.2 1800.csv.html | 102 B | ||
| 6.2 access_log.txt.txt | 10.1 MB | ||
| 7. GraphX.mp4 | 12 MB | ||
| 7. GraphX.srt | 4.1 KB | ||
| 7. Troubleshooting Spark on a Cluster.mp4 | 22.3 MB | ||
| 7. Troubleshooting Spark on a Cluster.srt | 6.3 KB | ||
| 7. [Activity] Superhero Degrees of Separation Review the Code and Run it.mp4 | 92.5 MB | ||
| 7. [Activity] Superhero Degrees of Separation Review the Code and Run it.srt | 16.6 KB | ||
| 7. [Activity]Running the Minimum Temperature Example, and Modifying it for Maximums.mp4 | 55.5 MB | ||
| 7. [Activity]Running the Minimum Temperature Example, and Modifying it for Maximums.srt | 8.7 KB | ||
| 7.1 degrees-of-separation.py.py | 3.6 KB | ||
| 7.1 min-temperatures.py.py | 716 B | ||
| 7.2 1800.csv.html | 102 B | ||
| 8. Item-Based Collaborative Filtering in Spark, cache(), and persist().mp4 | 46.6 MB | ||
| 8. Item-Based Collaborative Filtering in Spark, cache(), and persist().srt | 18 KB | ||
| 8. More Troubleshooting, and Managing Dependencies.mp4 | 29.8 MB | ||
| 8. More Troubleshooting, and Managing Dependencies.srt | 10.3 KB | ||
| 8. [Activity] Running the Maximum Temperature by Location Example.mp4 | 22.1 MB | ||
| 8. [Activity] Running the Maximum Temperature by Location Example.srt | 5.8 KB | ||
| 8.1 max-temperatures.py.py | 716 B | ||
| 9. [Activity] Counting Word Occurrences using flatmap().mp4 | 29.4 MB | ||
| 9. [Activity] Counting Word Occurrences using flatmap().srt | 12.5 KB | ||
| 9. [Activity] Running the Similar Movies Script using Spark's Cluster Manager.mp4 | 57.7 MB | ||
| 9. [Activity] Running the Similar Movies Script using Spark's Cluster Manager.srt | 18.1 KB | ||
| 9.1 movie-similarities.py.py | 3.5 KB | ||
| 9.1 word-count.py.py | 409 B | ||
| 9.2 Book.txt | 258.7 KB | ||
| Read Me.txt | 1 KB | ||
| [FreeAllCourse.Com].URL | 204 B | ||
| ▲ 131 total files | |||
Description
Taming Big Data with Apache Spark 3 and Python – Hands On! 
Dive right in with 15+ hands-on examples of analyzing large data sets with Apache Spark, on your desktop or on Hadoop!
What you’ll learn?
Use DataFrames and Structured Streaming in Spark 3
Frame big data analysis problems as Spark problems
Use Amazon’s Elastic MapReduce service to run your job on a cluster with Hadoop YARN
Install and run Apache Spark on a desktop computer or on a cluster
Use Spark’s Resilient Distributed Datasets to process and analyze large data sets across many CPU’s
Implement iterative algorithms such as breadth-first-search using Spark
Use the MLLib machine learning library to answer common data mining questions
Understand how Spark SQL lets you work with structured data
Created by Sundog Education by Frank Kane, Frank Kane
Last updated 1/2020
English
English, French [Auto-generated]
For More Updated Udemy Courses: FreeAllCourse.Com
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