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[UdemyCourseDownloader] Apache Spark for Java Developers

  • 收录时间:2020-01-02 03:12:09
  • 文件大小:11GB
  • 下载次数:11
  • 最近下载:2020-09-28 04:32:03
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文件列表

  1. 43. Recommender Systems/2. Building the Model.mp4 258MB
  2. 41. Decision Trees/2. Building the Model.mp4 253MB
  3. 40. Logistic Regression/3. Coding a Logistic Regression.mp4 230MB
  4. 38. Pipelines/1. Pipelines.mp4 223MB
  5. 16. SparkSQL Getting Started/1. SparkSQL Getting Started.mp4 199MB
  6. 26. More Aggregations/1. How to use the agg method in Spark.mp4 196MB
  7. 45. Streaming Chapter 2 - Streaming with Apache Kafka/5. Using KafkaUtils to access a DStream.mp4 188MB
  8. 14. RDD Performance/7. Caching and Persistence.mp4 187MB
  9. 02. Getting Started/2. Installing Spark.mp4 168MB
  10. 29. SparkSQL Performance/1. Understand the SparkUI for SparkSQL.mp4 168MB
  11. 09. Keyword Ranking Practical/3. Worked Solution (continued) with Sorting.mp4 156MB
  12. 46. Streaming Chapter 3- Structured Streaming/1. Structured Streaming Overview.mp4 153MB
  13. 45. Streaming Chapter 2 - Streaming with Apache Kafka/3. Using a Kafka Event Simulator.mp4 153MB
  14. 39. Case Study/2. Case Study - Walkthrough Part 1.mp4 152MB
  15. 24. DataFrames API/1. SQL vs DataFrames.mp4 147MB
  16. 23. Ordering/1. Ordering.mp4 146MB
  17. 39. Case Study/3. Case Study - Walkthrough Part 2.mp4 140MB
  18. 19. In Memory Data/1. In Memory Data.mp4 140MB
  19. 17. Datasets/4. Filters using Columns.mp4 139MB
  20. 22. Multiple Groupings/1. Multiple Groupings.mp4 139MB
  21. 08. Reading from Disk/1. Reading from Disk.mp4 138MB
  22. 24. DataFrames API/2. DataFrame Grouping.mp4 137MB
  23. 09. Keyword Ranking Practical/2. Worked Solution.mp4 136MB
  24. 14. RDD Performance/4. Shuffles.mp4 134MB
  25. 46. Streaming Chapter 3- Structured Streaming/2. Data Sinks.mp4 132MB
  26. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/4. Starting a Streaming Job.mp4 128MB
  27. 46. Streaming Chapter 3- Structured Streaming/5. What is the Batch Size in Structured Streaming.mp4 126MB
  28. 37. Non-Numeric Data/2. Understanding Vectors.mp4 125MB
  29. 46. Streaming Chapter 3- Structured Streaming/4. Windows and Watermarks.mp4 123MB
  30. 18. The Full SQL Syntax/1. Using a Spark Temporary View for SQL.mp4 121MB
  31. 45. Streaming Chapter 2 - Streaming with Apache Kafka/8. Adding a Slide Interval.mp4 121MB
  32. 36. Feature Selection/3. Identifying and Eliminating Duplicated Features.mp4 119MB
  33. 37. Non-Numeric Data/1. Using OneHotEncoding.mp4 116MB
  34. 35. Model Fitting Parameters/1. Setting Linear Regression Parameters.mp4 113MB
  35. 30. HashAggregation/3. How can I force Spark to use HashAggregation.mp4 111MB
  36. 10. Sorts and Coalesce/2. Why Coalesce is the Wrong Solution.mp4 110MB
  37. 30. HashAggregation/2. How does HashAggregation work.mp4 109MB
  38. 09. Keyword Ranking Practical/1. Practical Requirements.mp4 106MB
  39. 14. RDD Performance/1. Transformations and Actions.mp4 106MB
  40. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/5. Streaming Transformations.mp4 98MB
  41. 20. Groupings and Aggregations/1. Groupings and Aggregations.mp4 97MB
  42. 11. Deploying to AWS EMR (Optional)/2. Packing a Spark Jar for EMR.mp4 95MB
  43. 28. User Defined Functions/3. Using a UDF in Spark SQL.mp4 94MB
  44. 30. HashAggregation/1. Explaining Execution Plans.mp4 94MB
  45. 45. Streaming Chapter 2 - Streaming with Apache Kafka/6. Writing a Kafka Aggegration.mp4 94MB
  46. 36. Feature Selection/1. Describing the Features.mp4 94MB
  47. 41. Decision Trees/1. Overview of Decision Trees.mp4 91MB
  48. 29. SparkSQL Performance/3. Update - Setting spark.sql.shuffle.partitions.mp4 90MB
  49. 28. User Defined Functions/1. How to use a Lambda to write a UDF in Spark.mp4 88MB
  50. 03. Reduces on RDDs/1. Reduces on RDDs.mp4 88MB
  51. 10. Sorts and Coalesce/1. Why do sorts not work with foreach in Spark.mp4 88MB
  52. 12. Joins/2. Left Outer Joins and Optionals.mp4 88MB
  53. 01. Introduction/4. Spark Architecture and RDDs.mp4 86MB
  54. 11. Deploying to AWS EMR (Optional)/1. How to start an EMR Spark Cluster.mp4 85MB
  55. 33. Linear Regression Models/3. Assembling a Vector of Features.mp4 82MB
  56. 45. Streaming Chapter 2 - Streaming with Apache Kafka/2. Installing Kafka.mp4 81MB
  57. 27. Practical Exercise/1. Building a Pivot Table with Multiple Aggregations.mp4 81MB
  58. 01. Introduction/3.1 Practicals.zip.zip 79MB
  59. 06. PairRDDs/3. Coding a ReduceByKey.mp4 79MB
  60. 06. PairRDDs/2. Building a PairRDD.mp4 78MB
  61. 31. SparkSQL Performance vs RDDs/1. SparkSQL Performance vs RDDs.mp4 75MB
  62. 17. Datasets/1. Dataset Basics.mp4 72MB
  63. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/8. Windowing Batches.mp4 72MB
  64. 05. Tuples/2. Tuples and RDDs.mp4 72MB
  65. 07. FlatMaps and Filters/1. FlatMaps.mp4 69MB
  66. 17. Datasets/2. Filters using Expressions.mp4 69MB
  67. 13. Big Data Big Exercise/2. Warmup.mp4 68MB
  68. 34. Training Data/5. Assessing Model Accuracy with R2 and RMSE.mp4 67MB
  69. 32. Module 3 - SparkML for Machine Learning/5. The Model Building Process.mp4 65MB
  70. 14. RDD Performance/3. Narrow vs Wide Transformations.mp4 65MB
  71. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/6. Streaming Aggregations.vtt 65MB
  72. 41. Decision Trees/4. Random Forests.mp4 64MB
  73. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/6. Streaming Aggregations.mp4 64MB
  74. 33. Linear Regression Models/4. Model Fitting.mp4 63MB
  75. 06. PairRDDs/4. Using the Fluent API.mp4 63MB
  76. 36. Feature Selection/2. Correlation of Fetures.mp4 62MB
  77. 29. SparkSQL Performance/2. How does SQL and DataFrame performance compare.mp4 61MB
  78. 11. Deploying to AWS EMR (Optional)/3. Running a Spark Job on EMR.mp4 61MB
  79. 21. Date Formatting/1. Date Formatting.mp4 61MB
  80. 13. Big Data Big Exercise/11. Walkthrough - Step 9, adding titles and using the Big Data file.mp4 59MB
  81. 05. Tuples/1. RDDs of Objects.mp4 59MB
  82. 01. Introduction/3. Module 1 - Introduction.mp4 58MB
  83. 45. Streaming Chapter 2 - Streaming with Apache Kafka/4. Integrating Kafka with Spark.mp4 57MB
  84. 06. PairRDDs/1. Overview of PairRDDs.mp4 57MB
  85. 33. Linear Regression Models/2. Beginning Coding Linear Regressions.mp4 56MB
  86. 39. Case Study/1. Requirements.mp4 56MB
  87. 06. PairRDDs/5. Grouping By Key.mp4 53MB
  88. 12. Joins/1. Inner Joins.mp4 52MB
  89. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/2. Streaming Chapter 1 - Introduction to Streaming.mp4 52MB
  90. 01. Introduction/1. Welcome.mp4 51MB
  91. 41. Decision Trees/3. Interpreting a Decision Tree.mp4 51MB
  92. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/3. DStreams.mp4 50MB
  93. 34. Training Data/2. Using data from Kaggle.mp4 49MB
  94. 04. Mapping and Outputting/4. If you've had a NotSerializableException in Spark.mp4 49MB
  95. 04. Mapping and Outputting/3. Counting Big Data Items.mp4 47MB
  96. 45. Streaming Chapter 2 - Streaming with Apache Kafka/1. Overview of Kafka.mp4 47MB
  97. 34. Training Data/3. Practical Walkthrough.mp4 47MB
  98. 13. Big Data Big Exercise/5. Walkthrough - Step 3.mp4 47MB
  99. 34. Training Data/4. Splitting Training Data with Random Splits.mp4 46MB
  100. 04. Mapping and Outputting/1. Mapping Operations.mp4 45MB
  101. 28. User Defined Functions/2. Using more than one input parameter in Spark UDF.mp4 45MB
  102. 14. RDD Performance/2. The DAG and SparkUI.mp4 43MB
  103. 14. RDD Performance/5. Dealing with Key Skews.mp4 43MB
  104. 07. FlatMaps and Filters/2. Filters.mp4 42MB
  105. 04. Mapping and Outputting/2. Outputting Results to the Console.mp4 42MB
  106. 32. Module 3 - SparkML for Machine Learning/4. Supervised vs Unsupervised Learning.mp4 40MB
  107. 25. Pivot Tables/1. How does a Pivot Table work.mp4 39MB
  108. 11. Deploying to AWS EMR (Optional)/5. Calculating EMR costs and Terminating the cluster.mp4 38MB
  109. 17. Datasets/3. Filters using Lambdas.mp4 37MB
  110. 11. Deploying to AWS EMR (Optional)/4. Understanding the Job Progress Output.mp4 35MB
  111. 43. Recommender Systems/1. Overview and Matrix Factorisation.mp4 35MB
  112. 13. Big Data Big Exercise/3. Main Exercise Requirments.mp4 35MB
  113. 46. Streaming Chapter 3- Structured Streaming/6. Kafka Structured Streaming Pipelines.mp4 35MB
  114. 14. RDD Performance/6. Avoiding groupByKey and using map-side-reduces instead.mp4 34MB
  115. 13. Big Data Big Exercise/4. Walkthrough - Step 2.mp4 34MB
  116. 10. Sorts and Coalesce/3. What is Coalesce used for in Spark.mp4 34MB
  117. 15. Module 2 - Chapter 1 SparkSQL Introduction/2. Introducing SparkSQL.mp4 34MB
  118. 40. Logistic Regression/2. TrueFalse Negatives and Postives.mp4 33MB
  119. 12. Joins/2. Left Outer Joins and Optionals.vtt 30MB
  120. 12. Joins/3. Right Outer Joins.mp4 30MB
  121. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/7. SparkUI for Streaming Jobs.mp4 27MB
  122. 33. Linear Regression Models/1. Introducing Linear Regression.mp4 27MB
  123. 32. Module 3 - SparkML for Machine Learning/2. What is Machine Learning.mp4 27MB
  124. 15. Module 2 - Chapter 1 SparkSQL Introduction/1.1 biglog.txt.txt 24MB
  125. 13. Big Data Big Exercise/8. Walkthrough - Step 6.mp4 23MB
  126. 34. Training Data/1. Training vs Test and Holdout Data.mp4 23MB
  127. 32. Module 3 - SparkML for Machine Learning/3. Coming up in this Module - and introducing Kaggle.mp4 23MB
  128. 15. Module 2 - Chapter 1 SparkSQL Introduction/1.2 Code.zip.zip 22MB
  129. 12. Joins/4. Full Joins and Cartesians.mp4 21MB
  130. 13. Big Data Big Exercise/9. Walkthrough - Step 7.mp4 21MB
  131. 13. Big Data Big Exercise/10. Walkthrough - Step 8.mp4 20MB
  132. 45. Streaming Chapter 2 - Streaming with Apache Kafka/7. Adding a Window.mp4 20MB
  133. 13. Big Data Big Exercise/7. Walkthrough - Step 5.mp4 19MB
  134. 13. Big Data Big Exercise/1. Introducing the Requirements.mp4 17MB
  135. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/1.1 Code.zip.zip 15MB
  136. 13. Big Data Big Exercise/6. Walkthrough - Step 4.mp4 14MB
  137. 30. HashAggregation/4. SQL vs DataFrames Performance Results.mp4 12MB
  138. 36. Feature Selection/4. Data Preparation.mp4 10MB
  139. 32. Module 3 - SparkML for Machine Learning/1.1 MLCode.zip.zip 6MB
  140. 45. Streaming Chapter 2 - Streaming with Apache Kafka/3.1 viewing-figures-generation.zip.zip 4MB
  141. 40. Logistic Regression/1.1 MLCodeChapters9-12.zip.zip 3MB
  142. 13. Big Data Big Exercise/1.1 Practical Guide.pdf.pdf 651KB
  143. 43. Recommender Systems/2. Building the Model.vtt 29KB
  144. 41. Decision Trees/2. Building the Model.vtt 25KB
  145. 42. K Means Clustering/1. K Means Clustering.vtt 24KB
  146. 14. RDD Performance/7. Caching and Persistence.vtt 24KB
  147. 02. Getting Started/2. Installing Spark.vtt 23KB
  148. 38. Pipelines/1. Pipelines.vtt 23KB
  149. 40. Logistic Regression/3. Coding a Logistic Regression.vtt 22KB
  150. 16. SparkSQL Getting Started/1. SparkSQL Getting Started.vtt 22KB
  151. 26. More Aggregations/1. How to use the agg method in Spark.vtt 20KB
  152. 29. SparkSQL Performance/1. Understand the SparkUI for SparkSQL.vtt 20KB
  153. 45. Streaming Chapter 2 - Streaming with Apache Kafka/5. Using KafkaUtils to access a DStream.vtt 19KB
  154. 14. RDD Performance/4. Shuffles.vtt 19KB
  155. 45. Streaming Chapter 2 - Streaming with Apache Kafka/3. Using a Kafka Event Simulator.vtt 18KB
  156. 23. Ordering/1. Ordering.vtt 18KB
  157. 09. Keyword Ranking Practical/2. Worked Solution.vtt 17KB
  158. 36. Feature Selection/1. Describing the Features.vtt 17KB
  159. 30. HashAggregation/2. How does HashAggregation work.vtt 16KB
  160. 25. Pivot Tables/2. Coding a Pivot Table in Spark.vtt 16KB
  161. 37. Non-Numeric Data/1. Using OneHotEncoding.vtt 16KB
  162. 19. In Memory Data/1. In Memory Data.vtt 16KB
  163. 41. Decision Trees/1. Overview of Decision Trees.vtt 16KB
  164. 24. DataFrames API/1. SQL vs DataFrames.vtt 16KB
  165. 35. Model Fitting Parameters/2. Training, Test and Holdout Data.vtt 16KB
  166. 09. Keyword Ranking Practical/3. Worked Solution (continued) with Sorting.vtt 15KB
  167. 22. Multiple Groupings/1. Multiple Groupings.vtt 15KB
  168. 03. Reduces on RDDs/1. Reduces on RDDs.vtt 15KB
  169. 10. Sorts and Coalesce/2. Why Coalesce is the Wrong Solution.vtt 15KB
  170. 46. Streaming Chapter 3- Structured Streaming/1. Structured Streaming Overview.vtt 15KB
  171. 30. HashAggregation/3. How can I force Spark to use HashAggregation.vtt 14KB
  172. 18. The Full SQL Syntax/1. Using a Spark Temporary View for SQL.vtt 14KB
  173. 24. DataFrames API/2. DataFrame Grouping.vtt 14KB
  174. 20. Groupings and Aggregations/1. Groupings and Aggregations.vtt 14KB
  175. 35. Model Fitting Parameters/1. Setting Linear Regression Parameters.vtt 14KB
  176. 36. Feature Selection/3. Identifying and Eliminating Duplicated Features.vtt 14KB
  177. 39. Case Study/1. Requirements.vtt 14KB
  178. 08. Reading from Disk/1. Reading from Disk.vtt 14KB
  179. 39. Case Study/2. Case Study - Walkthrough Part 1.vtt 14KB
  180. 46. Streaming Chapter 3- Structured Streaming/2. Data Sinks.vtt 14KB
  181. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/4. Starting a Streaming Job.vtt 13KB
  182. 37. Non-Numeric Data/2. Understanding Vectors.vtt 13KB
  183. 46. Streaming Chapter 3- Structured Streaming/3. Structured Streaming Output Modes.vtt 13KB
  184. 46. Streaming Chapter 3- Structured Streaming/5. What is the Batch Size in Structured Streaming.vtt 13KB
  185. 39. Case Study/3. Case Study - Walkthrough Part 2.vtt 13KB
  186. 06. PairRDDs/3. Coding a ReduceByKey.vtt 13KB
  187. 09. Keyword Ranking Practical/1. Practical Requirements.vtt 13KB
  188. 45. Streaming Chapter 2 - Streaming with Apache Kafka/1. Overview of Kafka.vtt 12KB
  189. 01. Introduction/4. Spark Architecture and RDDs.vtt 12KB
  190. 11. Deploying to AWS EMR (Optional)/2. Packing a Spark Jar for EMR.vtt 12KB
  191. 46. Streaming Chapter 3- Structured Streaming/4. Windows and Watermarks.vtt 12KB
  192. 14. RDD Performance/1. Transformations and Actions.vtt 12KB
  193. 11. Deploying to AWS EMR (Optional)/1. How to start an EMR Spark Cluster.vtt 12KB
  194. 14. RDD Performance/3. Narrow vs Wide Transformations.vtt 11KB
  195. 33. Linear Regression Models/3. Assembling a Vector of Features.vtt 11KB
  196. 32. Module 3 - SparkML for Machine Learning/4. Supervised vs Unsupervised Learning.vtt 11KB
  197. 17. Datasets/4. Filters using Columns.vtt 11KB
  198. 28. User Defined Functions/1. How to use a Lambda to write a UDF in Spark.vtt 11KB
  199. 45. Streaming Chapter 2 - Streaming with Apache Kafka/8. Adding a Slide Interval.vtt 11KB
  200. 10. Sorts and Coalesce/1. Why do sorts not work with foreach in Spark.vtt 11KB
  201. 06. PairRDDs/2. Building a PairRDD.vtt 10KB
  202. 05. Tuples/2. Tuples and RDDs.vtt 10KB
  203. 07. FlatMaps and Filters/1. FlatMaps.vtt 10KB
  204. 28. User Defined Functions/3. Using a UDF in Spark SQL.vtt 10KB
  205. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/5. Streaming Transformations.vtt 10KB
  206. 45. Streaming Chapter 2 - Streaming with Apache Kafka/2. Installing Kafka.vtt 10KB
  207. 06. PairRDDs/1. Overview of PairRDDs.vtt 10KB
  208. 40. Logistic Regression/2. TrueFalse Negatives and Postives.vtt 10KB
  209. 32. Module 3 - SparkML for Machine Learning/5. The Model Building Process.vtt 10KB
  210. 29. SparkSQL Performance/3. Update - Setting spark.sql.shuffle.partitions.vtt 9KB
  211. 45. Streaming Chapter 2 - Streaming with Apache Kafka/6. Writing a Kafka Aggegration.vtt 9KB
  212. 27. Practical Exercise/1. Building a Pivot Table with Multiple Aggregations.vtt 9KB
  213. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/8. Windowing Batches.vtt 9KB
  214. 13. Big Data Big Exercise/2. Warmup.vtt 9KB
  215. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/2. Streaming Chapter 1 - Introduction to Streaming.vtt 9KB
  216. 11. Deploying to AWS EMR (Optional)/3. Running a Spark Job on EMR.vtt 9KB
  217. 12. Joins/1. Inner Joins.vtt 9KB
  218. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/3. DStreams.vtt 9KB
  219. 30. HashAggregation/1. Explaining Execution Plans.vtt 8KB
  220. 34. Training Data/5. Assessing Model Accuracy with R2 and RMSE.vtt 8KB
  221. 05. Tuples/1. RDDs of Objects.vtt 8KB
  222. 33. Linear Regression Models/4. Model Fitting.vtt 8KB
  223. 46. Streaming Chapter 3- Structured Streaming/6. Kafka Structured Streaming Pipelines.vtt 8KB
  224. 31. SparkSQL Performance vs RDDs/1. SparkSQL Performance vs RDDs.vtt 8KB
  225. 13. Big Data Big Exercise/3. Main Exercise Requirments.vtt 8KB
  226. 14. RDD Performance/5. Dealing with Key Skews.vtt 8KB
  227. 29. SparkSQL Performance/2. How does SQL and DataFrame performance compare.vtt 8KB
  228. 33. Linear Regression Models/2. Beginning Coding Linear Regressions.vtt 7KB
  229. 04. Mapping and Outputting/1. Mapping Operations.vtt 7KB
  230. 33. Linear Regression Models/1. Introducing Linear Regression.vtt 7KB
  231. 06. PairRDDs/4. Using the Fluent API.vtt 7KB
  232. 14. RDD Performance/2. The DAG and SparkUI.vtt 7KB
  233. 36. Feature Selection/2. Correlation of Fetures.vtt 7KB
  234. 17. Datasets/1. Dataset Basics.vtt 7KB
  235. 14. RDD Performance/6. Avoiding groupByKey and using map-side-reduces instead.vtt 7KB
  236. 17. Datasets/2. Filters using Expressions.vtt 7KB
  237. 25. Pivot Tables/1. How does a Pivot Table work.vtt 7KB
  238. 21. Date Formatting/1. Date Formatting.vtt 7KB
  239. 41. Decision Trees/4. Random Forests.vtt 7KB
  240. 13. Big Data Big Exercise/5. Walkthrough - Step 3.vtt 7KB
  241. 04. Mapping and Outputting/3. Counting Big Data Items.vtt 7KB
  242. 15. Module 2 - Chapter 1 SparkSQL Introduction/2. Introducing SparkSQL.vtt 7KB
  243. 41. Decision Trees/3. Interpreting a Decision Tree.vtt 7KB
  244. 04. Mapping and Outputting/4. If you've had a NotSerializableException in Spark.vtt 7KB
  245. 43. Recommender Systems/1. Overview and Matrix Factorisation.vtt 6KB
  246. 13. Big Data Big Exercise/11. Walkthrough - Step 9, adding titles and using the Big Data file.vtt 6KB
  247. 34. Training Data/2. Using data from Kaggle.vtt 6KB
  248. 11. Deploying to AWS EMR (Optional)/4. Understanding the Job Progress Output.vtt 6KB
  249. 34. Training Data/4. Splitting Training Data with Random Splits.vtt 6KB
  250. 34. Training Data/1. Training vs Test and Holdout Data.vtt 6KB
  251. 28. User Defined Functions/2. Using more than one input parameter in Spark UDF.vtt 6KB
  252. 04. Mapping and Outputting/2. Outputting Results to the Console.vtt 5KB
  253. 06. PairRDDs/5. Grouping By Key.vtt 5KB
  254. 45. Streaming Chapter 2 - Streaming with Apache Kafka/4. Integrating Kafka with Spark.vtt 5KB
  255. 13. Big Data Big Exercise/1. Introducing the Requirements.vtt 5KB
  256. 07. FlatMaps and Filters/2. Filters.vtt 5KB
  257. 01. Introduction/3. Module 1 - Introduction.vtt 5KB
  258. 11. Deploying to AWS EMR (Optional)/5. Calculating EMR costs and Terminating the cluster.vtt 5KB
  259. 10. Sorts and Coalesce/3. What is Coalesce used for in Spark.vtt 5KB
  260. 13. Big Data Big Exercise/4. Walkthrough - Step 2.vtt 5KB
  261. 36. Feature Selection/4. Data Preparation.vtt 5KB
  262. 34. Training Data/3. Practical Walkthrough.vtt 5KB
  263. 32. Module 3 - SparkML for Machine Learning/2. What is Machine Learning.vtt 5KB
  264. 13. Big Data Big Exercise/8. Walkthrough - Step 6.vtt 4KB
  265. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/7. SparkUI for Streaming Jobs.vtt 4KB
  266. 12. Joins/4. Full Joins and Cartesians.vtt 4KB
  267. 17. Datasets/3. Filters using Lambdas.vtt 4KB
  268. 12. Joins/3. Right Outer Joins.vtt 4KB
  269. 01. Introduction/1. Welcome.vtt 4KB
  270. 32. Module 3 - SparkML for Machine Learning/3. Coming up in this Module - and introducing Kaggle.vtt 3KB
  271. 13. Big Data Big Exercise/9. Walkthrough - Step 7.vtt 3KB
  272. 13. Big Data Big Exercise/7. Walkthrough - Step 5.vtt 3KB
  273. 30. HashAggregation/4. SQL vs DataFrames Performance Results.vtt 3KB
  274. 13. Big Data Big Exercise/10. Walkthrough - Step 8.vtt 3KB
  275. 13. Big Data Big Exercise/6. Walkthrough - Step 4.vtt 2KB
  276. 45. Streaming Chapter 2 - Streaming with Apache Kafka/7. Adding a Window.vtt 2KB
  277. 02. Getting Started/1. Warning - Java 91011 is not supported by Spark.html 1KB
  278. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/3.1 LoggingServer.zip.zip 560B
  279. 01. Introduction/2. Downloading the Code.html 447B
  280. 15. Module 2 - Chapter 1 SparkSQL Introduction/1. Code for SQLDataFrames Section.html 446B
  281. 44. Module 4 -Spark Streaming and Structured Streaming with Kafka/1. Welcome to Module 4 - Spark Streaming.html 345B
  282. 32. Module 3 - SparkML for Machine Learning/1. Welcome to Module 3.html 282B
  283. 40. Logistic Regression/1. Code for chapters 9-12.html 188B
  284. udemycoursedownloader.com.url 132B
  285. Udemy Course downloader.txt 94B