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种子哈希：536c359c7abd62fee7f59569b87a978aec9e147b
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最近下载：2025-05-16
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文档列表

43. Recommender Systems/2. Building the Model.mp4 270.2 MB
41. Decision Trees/2. Building the Model.mp4 265.6 MB
40. Logistic Regression/3. Coding a Logistic Regression.mp4 241.1 MB
38. Pipelines/1. Pipelines.mp4 234.0 MB
42. K Means Clustering/1. K Means Clustering.mp4 221.8 MB
16. SparkSQL Getting Started/1. SparkSQL Getting Started.mp4 208.6 MB
26. More Aggregations/1. How to use the agg method in Spark.mp4 205.5 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/5. Using KafkaUtils to access a DStream.mp4 197.3 MB
14. RDD Performance/7. Caching and Persistence.mp4 196.2 MB
2. Getting Started/2. Installing Spark.mp4 176.6 MB
29. SparkSQL Performance/1. Understand the SparkUI for SparkSQL.mp4 176.4 MB
25. Pivot Tables/2. Coding a Pivot Table in Spark.mp4 173.6 MB
9. Keyword Ranking Practical/3. Worked Solution (continued) with Sorting.mp4 163.8 MB
46. Streaming Chapter 3- Structured Streaming/1. Structured Streaming Overview.mp4 160.4 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/3. Using a Kafka Event Simulator.mp4 160.1 MB
39. Case Study/2. Case Study - Walkthrough Part 1.mp4 158.9 MB
24. DataFrames API/1. SQL vs DataFrames.mp4 154.3 MB
23. Ordering/1. Ordering.mp4 153.5 MB
39. Case Study/3. Case Study - Walkthrough Part 2.mp4 146.9 MB
19. In Memory Data/1. In Memory Data.mp4 146.5 MB
17. Datasets/4. Filters using Columns.mp4 145.7 MB
22. Multiple Groupings/1. Multiple Groupings.mp4 145.6 MB
8. Reading from Disk/1. Reading from Disk.mp4 144.7 MB
24. DataFrames API/2. DataFrame Grouping.mp4 143.6 MB
9. Keyword Ranking Practical/2. Worked Solution.mp4 142.6 MB
14. RDD Performance/4. Shuffles.mp4 140.2 MB
46. Streaming Chapter 3- Structured Streaming/2. Data Sinks.mp4 138.7 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/4. Starting a Streaming Job.mp4 134.1 MB
46. Streaming Chapter 3- Structured Streaming/5. What is the Batch Size in Structured Streaming.mp4 132.2 MB
37. Non-Numeric Data/2. Understanding Vectors.mp4 131.4 MB
46. Streaming Chapter 3- Structured Streaming/4. Windows and Watermarks.mp4 129.2 MB
35. Model Fitting Parameters/2. Training, Test and Holdout Data.mp4 129.0 MB
18. The Full SQL Syntax/1. Using a Spark Temporary View for SQL.mp4 126.5 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/8. Adding a Slide Interval.mp4 126.4 MB
36. Feature Selection/3. Identifying and Eliminating Duplicated Features.mp4 124.9 MB
37. Non-Numeric Data/1. Using OneHotEncoding.mp4 122.0 MB
35. Model Fitting Parameters/1. Setting Linear Regression Parameters.mp4 118.4 MB
30. HashAggregation/3. How can I force Spark to use HashAggregation.mp4 116.0 MB
10. Sorts and Coalesce/2. Why Coalesce is the Wrong Solution.mp4 114.8 MB
30. HashAggregation/2. How does HashAggregation work.mp4 114.3 MB
46. Streaming Chapter 3- Structured Streaming/3. Structured Streaming Output Modes.mp4 114.2 MB
9. Keyword Ranking Practical/1. Practical Requirements.mp4 111.6 MB
14. RDD Performance/1. Transformations and Actions.mp4 110.8 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/5. Streaming Transformations.mp4 102.8 MB
20. Groupings and Aggregations/1. Groupings and Aggregations.mp4 101.8 MB
11. Deploying to AWS EMR (Optional)/2. Packing a Spark Jar for EMR.mp4 99.3 MB
28. User Defined Functions/3. Using a UDF in Spark SQL.mp4 98.7 MB
30. HashAggregation/1. Explaining Execution Plans.mp4 98.5 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/6. Writing a Kafka Aggegration.mp4 98.3 MB
36. Feature Selection/1. Describing the Features.mp4 98.2 MB
41. Decision Trees/1. Overview of Decision Trees.mp4 95.7 MB
29. SparkSQL Performance/3. Update - Setting spark.sql.shuffle.partitions.mp4 94.9 MB
28. User Defined Functions/1. How to use a Lambda to write a UDF in Spark.mp4 92.7 MB
3. Reduces on RDDs/1. Reduces on RDDs.mp4 92.6 MB
10. Sorts and Coalesce/1. Why do sorts not work with foreach in Spark.mp4 92.5 MB
12. Joins/2. Left Outer Joins and Optionals.mp4 91.9 MB
11. Deploying to AWS EMR (Optional)/1. How to start an EMR Spark Cluster.mp4 88.7 MB
33. Linear Regression Models/3. Assembling a Vector of Features.mp4 85.5 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/2. Installing Kafka.mp4 85.3 MB
27. Practical Exercise/1. Building a Pivot Table with Multiple Aggregations.mp4 84.6 MB
1. Introduction/3.1 Practicals.zip.zip 83.1 MB
6. PairRDDs/3. Coding a ReduceByKey.mp4 82.3 MB
6. PairRDDs/2. Building a PairRDD.mp4 81.4 MB
31. SparkSQL Performance vs RDDs/1. SparkSQL Performance vs RDDs.mp4 79.1 MB
17. Datasets/1. Dataset Basics.mp4 76.0 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/8. Windowing Batches.mp4 75.6 MB
5. Tuples/2. Tuples and RDDs.mp4 75.4 MB
7. FlatMaps and Filters/1. FlatMaps.mp4 72.4 MB
17. Datasets/2. Filters using Expressions.mp4 72.0 MB
13. Big Data Big Exercise/2. Warmup.mp4 71.5 MB
34. Training Data/5. Assessing Model Accuracy with R2 and RMSE.mp4 70.4 MB
32. Module 3 - SparkML for Machine Learning/5. The Model Building Process.mp4 68.7 MB
14. RDD Performance/3. Narrow vs Wide Transformations.mp4 68.7 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/6. Streaming Aggregations.vtt 68.7 MB
41. Decision Trees/4. Random Forests.mp4 67.2 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/6. Streaming Aggregations.mp4 67.1 MB
33. Linear Regression Models/4. Model Fitting.mp4 66.3 MB
6. PairRDDs/4. Using the Fluent API.mp4 65.9 MB
36. Feature Selection/2. Correlation of Fetures.mp4 65.0 MB
29. SparkSQL Performance/2. How does SQL and DataFrame performance compare.mp4 64.4 MB
11. Deploying to AWS EMR (Optional)/3. Running a Spark Job on EMR.mp4 64.4 MB
21. Date Formatting/1. Date Formatting.mp4 63.6 MB
13. Big Data Big Exercise/11. Walkthrough - Step 9, adding titles and using the Big Data file.mp4 62.1 MB
5. Tuples/1. RDDs of Objects.mp4 61.8 MB
1. Introduction/3. Module 1 - Introduction.mp4 60.7 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/4. Integrating Kafka with Spark.mp4 60.1 MB
6. PairRDDs/1. Overview of PairRDDs.mp4 59.8 MB
33. Linear Regression Models/2. Beginning Coding Linear Regressions.mp4 58.7 MB
39. Case Study/1. Requirements.mp4 58.4 MB
6. PairRDDs/5. Grouping By Key.mp4 55.1 MB
12. Joins/1. Inner Joins.mp4 54.4 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/2. Streaming Chapter 1 - Introduction to Streaming.mp4 54.2 MB
1. Introduction/1. Welcome.mp4 54.0 MB
41. Decision Trees/3. Interpreting a Decision Tree.mp4 53.2 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/3. DStreams.mp4 52.2 MB
34. Training Data/2. Using data from Kaggle.mp4 51.3 MB
4. Mapping and Outputting/4. If you've had a NotSerializableException in Spark.mp4 51.0 MB
4. Mapping and Outputting/3. Counting Big Data Items.mp4 49.4 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/1. Overview of Kafka.mp4 49.4 MB
34. Training Data/3. Practical Walkthrough.mp4 49.3 MB
13. Big Data Big Exercise/5. Walkthrough - Step 3.mp4 48.8 MB
34. Training Data/4. Splitting Training Data with Random Splits.mp4 48.4 MB
4. Mapping and Outputting/1. Mapping Operations.mp4 47.4 MB
28. User Defined Functions/2. Using more than one input parameter in Spark UDF.mp4 47.2 MB
14. RDD Performance/2. The DAG and SparkUI.mp4 45.4 MB
14. RDD Performance/5. Dealing with Key Skews.mp4 45.0 MB
7. FlatMaps and Filters/2. Filters.mp4 44.1 MB
4. Mapping and Outputting/2. Outputting Results to the Console.mp4 43.8 MB
32. Module 3 - SparkML for Machine Learning/4. Supervised vs Unsupervised Learning.mp4 42.3 MB
25. Pivot Tables/1. How does a Pivot Table work.mp4 41.1 MB
11. Deploying to AWS EMR (Optional)/5. Calculating EMR costs and Terminating the cluster.mp4 39.7 MB
17. Datasets/3. Filters using Lambdas.mp4 38.5 MB
11. Deploying to AWS EMR (Optional)/4. Understanding the Job Progress Output.mp4 37.0 MB
43. Recommender Systems/1. Overview and Matrix Factorisation.mp4 36.6 MB
13. Big Data Big Exercise/3. Main Exercise Requirments.mp4 36.3 MB
46. Streaming Chapter 3- Structured Streaming/6. Kafka Structured Streaming Pipelines.mp4 36.3 MB
14. RDD Performance/6. Avoiding groupByKey and using map-side-reduces instead.mp4 35.7 MB
13. Big Data Big Exercise/4. Walkthrough - Step 2.mp4 35.5 MB
10. Sorts and Coalesce/3. What is Coalesce used for in Spark.mp4 35.3 MB
15. Module 2 - Chapter 1 SparkSQL Introduction/2. Introducing SparkSQL.mp4 35.2 MB
40. Logistic Regression/2. TrueFalse Negatives and Postives.mp4 34.7 MB
12. Joins/2. Left Outer Joins and Optionals.vtt 31.7 MB
12. Joins/3. Right Outer Joins.mp4 31.4 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/7. SparkUI for Streaming Jobs.mp4 28.3 MB
33. Linear Regression Models/1. Introducing Linear Regression.mp4 27.9 MB
32. Module 3 - SparkML for Machine Learning/2. What is Machine Learning.mp4 27.9 MB
15. Module 2 - Chapter 1 SparkSQL Introduction/1.1 biglog.txt.txt 25.5 MB
13. Big Data Big Exercise/8. Walkthrough - Step 6.mp4 24.2 MB
34. Training Data/1. Training vs Test and Holdout Data.mp4 23.9 MB
32. Module 3 - SparkML for Machine Learning/3. Coming up in this Module - and introducing Kaggle.mp4 23.7 MB
15. Module 2 - Chapter 1 SparkSQL Introduction/1.2 Code.zip.zip 23.2 MB
12. Joins/4. Full Joins and Cartesians.mp4 22.4 MB
13. Big Data Big Exercise/9. Walkthrough - Step 7.mp4 22.2 MB
13. Big Data Big Exercise/10. Walkthrough - Step 8.mp4 21.3 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/7. Adding a Window.mp4 21.1 MB
13. Big Data Big Exercise/7. Walkthrough - Step 5.mp4 20.0 MB
13. Big Data Big Exercise/1. Introducing the Requirements.mp4 18.3 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/1.1 Code.zip.zip 15.3 MB
13. Big Data Big Exercise/6. Walkthrough - Step 4.mp4 14.5 MB
30. HashAggregation/4. SQL vs DataFrames Performance Results.mp4 12.1 MB
36. Feature Selection/4. Data Preparation.mp4 10.5 MB
32. Module 3 - SparkML for Machine Learning/1.1 MLCode.zip.zip 5.8 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/3.1 viewing-figures-generation.zip.zip 3.8 MB
40. Logistic Regression/1.1 MLCodeChapters9-12.zip.zip 3.6 MB
13. Big Data Big Exercise/1.1 Practical Guide.pdf.pdf 666.1 kB
43. Recommender Systems/2. Building the Model.vtt 29.4 kB
41. Decision Trees/2. Building the Model.vtt 25.5 kB
42. K Means Clustering/1. K Means Clustering.vtt 25.0 kB
14. RDD Performance/7. Caching and Persistence.vtt 24.6 kB
2. Getting Started/2. Installing Spark.vtt 23.8 kB
38. Pipelines/1. Pipelines.vtt 23.6 kB
40. Logistic Regression/3. Coding a Logistic Regression.vtt 22.9 kB
16. SparkSQL Getting Started/1. SparkSQL Getting Started.vtt 22.5 kB
26. More Aggregations/1. How to use the agg method in Spark.vtt 20.7 kB
29. SparkSQL Performance/1. Understand the SparkUI for SparkSQL.vtt 20.4 kB
45. Streaming Chapter 2 - Streaming with Apache Kafka/5. Using KafkaUtils to access a DStream.vtt 19.3 kB
14. RDD Performance/4. Shuffles.vtt 19.0 kB
45. Streaming Chapter 2 - Streaming with Apache Kafka/3. Using a Kafka Event Simulator.vtt 18.5 kB
23. Ordering/1. Ordering.vtt 18.1 kB
9. Keyword Ranking Practical/2. Worked Solution.vtt 17.3 kB
36. Feature Selection/1. Describing the Features.vtt 17.1 kB
30. HashAggregation/2. How does HashAggregation work.vtt 16.9 kB
25. Pivot Tables/2. Coding a Pivot Table in Spark.vtt 16.8 kB
37. Non-Numeric Data/1. Using OneHotEncoding.vtt 16.5 kB
19. In Memory Data/1. In Memory Data.vtt 16.4 kB
41. Decision Trees/1. Overview of Decision Trees.vtt 16.2 kB
24. DataFrames API/1. SQL vs DataFrames.vtt 16.0 kB
35. Model Fitting Parameters/2. Training, Test and Holdout Data.vtt 15.9 kB
9. Keyword Ranking Practical/3. Worked Solution (continued) with Sorting.vtt 15.7 kB
22. Multiple Groupings/1. Multiple Groupings.vtt 15.4 kB
3. Reduces on RDDs/1. Reduces on RDDs.vtt 15.1 kB
10. Sorts and Coalesce/2. Why Coalesce is the Wrong Solution.vtt 15.1 kB
46. Streaming Chapter 3- Structured Streaming/1. Structured Streaming Overview.vtt 15.1 kB
30. HashAggregation/3. How can I force Spark to use HashAggregation.vtt 14.8 kB
18. The Full SQL Syntax/1. Using a Spark Temporary View for SQL.vtt 14.8 kB
24. DataFrames API/2. DataFrame Grouping.vtt 14.5 kB
20. Groupings and Aggregations/1. Groupings and Aggregations.vtt 14.5 kB
35. Model Fitting Parameters/1. Setting Linear Regression Parameters.vtt 14.4 kB
36. Feature Selection/3. Identifying and Eliminating Duplicated Features.vtt 14.4 kB
39. Case Study/1. Requirements.vtt 14.3 kB
8. Reading from Disk/1. Reading from Disk.vtt 14.3 kB
39. Case Study/2. Case Study - Walkthrough Part 1.vtt 14.0 kB
46. Streaming Chapter 3- Structured Streaming/2. Data Sinks.vtt 13.8 kB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/4. Starting a Streaming Job.vtt 13.7 kB
37. Non-Numeric Data/2. Understanding Vectors.vtt 13.5 kB
46. Streaming Chapter 3- Structured Streaming/3. Structured Streaming Output Modes.vtt 13.5 kB
46. Streaming Chapter 3- Structured Streaming/5. What is the Batch Size in Structured Streaming.vtt 13.2 kB
39. Case Study/3. Case Study - Walkthrough Part 2.vtt 13.1 kB
6. PairRDDs/3. Coding a ReduceByKey.vtt 13.0 kB
9. Keyword Ranking Practical/1. Practical Requirements.vtt 12.9 kB
45. Streaming Chapter 2 - Streaming with Apache Kafka/1. Overview of Kafka.vtt 12.7 kB
11. Deploying to AWS EMR (Optional)/2. Packing a Spark Jar for EMR.vtt 12.5 kB
46. Streaming Chapter 3- Structured Streaming/4. Windows and Watermarks.vtt 12.4 kB
14. RDD Performance/1. Transformations and Actions.vtt 12.1 kB
11. Deploying to AWS EMR (Optional)/1. How to start an EMR Spark Cluster.vtt 12.0 kB
14. RDD Performance/3. Narrow vs Wide Transformations.vtt 11.7 kB
33. Linear Regression Models/3. Assembling a Vector of Features.vtt 11.5 kB
32. Module 3 - SparkML for Machine Learning/4. Supervised vs Unsupervised Learning.vtt 11.4 kB
17. Datasets/4. Filters using Columns.vtt 11.4 kB
28. User Defined Functions/1. How to use a Lambda to write a UDF in Spark.vtt 11.2 kB
45. Streaming Chapter 2 - Streaming with Apache Kafka/8. Adding a Slide Interval.vtt 11.0 kB
10. Sorts and Coalesce/1. Why do sorts not work with foreach in Spark.vtt 10.9 kB
6. PairRDDs/2. Building a PairRDD.vtt 10.7 kB
5. Tuples/2. Tuples and RDDs.vtt 10.6 kB
7. FlatMaps and Filters/1. FlatMaps.vtt 10.3 kB
28. User Defined Functions/3. Using a UDF in Spark SQL.vtt 10.0 kB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/5. Streaming Transformations.vtt 10.0 kB
45. Streaming Chapter 2 - Streaming with Apache Kafka/2. Installing Kafka.vtt 10.0 kB
6. PairRDDs/1. Overview of PairRDDs.vtt 10.0 kB
40. Logistic Regression/2. TrueFalse Negatives and Postives.vtt 9.9 kB
32. Module 3 - SparkML for Machine Learning/5. The Model Building Process.vtt 9.8 kB
29. SparkSQL Performance/3. Update - Setting spark.sql.shuffle.partitions.vtt 9.7 kB
45. Streaming Chapter 2 - Streaming with Apache Kafka/6. Writing a Kafka Aggegration.vtt 9.7 kB
27. Practical Exercise/1. Building a Pivot Table with Multiple Aggregations.vtt 9.6 kB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/8. Windowing Batches.vtt 9.6 kB
13. Big Data Big Exercise/2. Warmup.vtt 9.6 kB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/2. Streaming Chapter 1 - Introduction to Streaming.vtt 9.5 kB
11. Deploying to AWS EMR (Optional)/3. Running a Spark Job on EMR.vtt 9.0 kB
12. Joins/1. Inner Joins.vtt 8.8 kB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/3. DStreams.vtt 8.7 kB
30. HashAggregation/1. Explaining Execution Plans.vtt 8.7 kB
34. Training Data/5. Assessing Model Accuracy with R2 and RMSE.vtt 8.7 kB
5. Tuples/1. RDDs of Objects.vtt 8.6 kB
33. Linear Regression Models/4. Model Fitting.vtt 8.4 kB
46. Streaming Chapter 3- Structured Streaming/6. Kafka Structured Streaming Pipelines.vtt 8.2 kB
31. SparkSQL Performance vs RDDs/1. SparkSQL Performance vs RDDs.vtt 8.0 kB
13. Big Data Big Exercise/3. Main Exercise Requirments.vtt 7.9 kB
14. RDD Performance/5. Dealing with Key Skews.vtt 7.9 kB
29. SparkSQL Performance/2. How does SQL and DataFrame performance compare.vtt 7.9 kB
33. Linear Regression Models/2. Beginning Coding Linear Regressions.vtt 7.7 kB
4. Mapping and Outputting/1. Mapping Operations.vtt 7.6 kB
33. Linear Regression Models/1. Introducing Linear Regression.vtt 7.5 kB
6. PairRDDs/4. Using the Fluent API.vtt 7.5 kB
14. RDD Performance/2. The DAG and SparkUI.vtt 7.4 kB
36. Feature Selection/2. Correlation of Fetures.vtt 7.4 kB
17. Datasets/1. Dataset Basics.vtt 7.4 kB
14. RDD Performance/6. Avoiding groupByKey and using map-side-reduces instead.vtt 7.4 kB
17. Datasets/2. Filters using Expressions.vtt 7.3 kB
25. Pivot Tables/1. How does a Pivot Table work.vtt 7.2 kB
21. Date Formatting/1. Date Formatting.vtt 7.1 kB
41. Decision Trees/4. Random Forests.vtt 7.0 kB
13. Big Data Big Exercise/5. Walkthrough - Step 3.vtt 7.0 kB
4. Mapping and Outputting/3. Counting Big Data Items.vtt 6.9 kB
15. Module 2 - Chapter 1 SparkSQL Introduction/2. Introducing SparkSQL.vtt 6.8 kB
41. Decision Trees/3. Interpreting a Decision Tree.vtt 6.8 kB
4. Mapping and Outputting/4. If you've had a NotSerializableException in Spark.vtt 6.7 kB
43. Recommender Systems/1. Overview and Matrix Factorisation.vtt 6.6 kB
13. Big Data Big Exercise/11. Walkthrough - Step 9, adding titles and using the Big Data file.vtt 6.3 kB
34. Training Data/2. Using data from Kaggle.vtt 6.3 kB
11. Deploying to AWS EMR (Optional)/4. Understanding the Job Progress Output.vtt 6.1 kB
34. Training Data/4. Splitting Training Data with Random Splits.vtt 5.9 kB
34. Training Data/1. Training vs Test and Holdout Data.vtt 5.9 kB
28. User Defined Functions/2. Using more than one input parameter in Spark UDF.vtt 5.7 kB
4. Mapping and Outputting/2. Outputting Results to the Console.vtt 5.6 kB
6. PairRDDs/5. Grouping By Key.vtt 5.6 kB
45. Streaming Chapter 2 - Streaming with Apache Kafka/4. Integrating Kafka with Spark.vtt 5.5 kB
13. Big Data Big Exercise/1. Introducing the Requirements.vtt 5.5 kB
7. FlatMaps and Filters/2. Filters.vtt 5.4 kB
1. Introduction/3. Module 1 - Introduction.vtt 5.4 kB
11. Deploying to AWS EMR (Optional)/5. Calculating EMR costs and Terminating the cluster.vtt 5.3 kB
10. Sorts and Coalesce/3. What is Coalesce used for in Spark.vtt 5.2 kB
13. Big Data Big Exercise/4. Walkthrough - Step 2.vtt 5.1 kB
36. Feature Selection/4. Data Preparation.vtt 4.9 kB
34. Training Data/3. Practical Walkthrough.vtt 4.9 kB
32. Module 3 - SparkML for Machine Learning/2. What is Machine Learning.vtt 4.8 kB
13. Big Data Big Exercise/8. Walkthrough - Step 6.vtt 4.3 kB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/7. SparkUI for Streaming Jobs.vtt 4.2 kB
12. Joins/4. Full Joins and Cartesians.vtt 4.2 kB
17. Datasets/3. Filters using Lambdas.vtt 4.1 kB
12. Joins/3. Right Outer Joins.vtt 4.1 kB
1. Introduction/1. Welcome.vtt 4.0 kB
32. Module 3 - SparkML for Machine Learning/3. Coming up in this Module - and introducing Kaggle.vtt 3.5 kB
13. Big Data Big Exercise/9. Walkthrough - Step 7.vtt 3.0 kB
13. Big Data Big Exercise/7. Walkthrough - Step 5.vtt 3.0 kB
30. HashAggregation/4. SQL vs DataFrames Performance Results.vtt 2.7 kB
13. Big Data Big Exercise/10. Walkthrough - Step 8.vtt 2.6 kB
13. Big Data Big Exercise/6. Walkthrough - Step 4.vtt 2.2 kB
45. Streaming Chapter 2 - Streaming with Apache Kafka/7. Adding a Window.vtt 1.9 kB
2. Getting Started/1. Warning - Java 91011 is not supported by Spark.html 1.3 kB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/3.1 LoggingServer.zip.zip 560 Bytes
1. Introduction/2. Downloading the Code.html 447 Bytes
15. Module 2 - Chapter 1 SparkSQL Introduction/1. Code for SQLDataFrames Section.html 446 Bytes
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/1. Welcome to Module 4 - Spark Streaming.html 345 Bytes
32. Module 3 - SparkML for Machine Learning/1. Welcome to Module 3.html 282 Bytes
40. Logistic Regression/1. Code for chapters 9-12.html 188 Bytes

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