Udemy - Apache Spark for Java Developers

Creation Time	Dec. 31, 2024, 1:17 p.m.
Last Access Time	Jan. 8, 2025, 9:07 p.m.
File Size	11.4 GB
Keywords	Java for - Udemy Apache Spark Developers
Total Requests	6
Total Files	285
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

43. Recommender Systems/2. Building the Model.mp4 257.7 MB
41. Decision Trees/2. Building the Model.mp4 253.3 MB
40. Logistic Regression/3. Coding a Logistic Regression.mp4 229.9 MB
38. Pipelines/1. Pipelines.mp4 223.1 MB
42. K Means Clustering/1. K Means Clustering.mp4 211.5 MB
16. SparkSQL Getting Started/1. SparkSQL Getting Started.mp4 199.0 MB
26. More Aggregations/1. How to use the agg method in Spark.mp4 196.0 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/5. Using KafkaUtils to access a DStream.mp4 188.2 MB
14. RDD Performance/7. Caching and Persistence.mp4 187.1 MB
2. Getting Started/2. Installing Spark.mp4 168.5 MB
29. SparkSQL Performance/1. Understand the SparkUI for SparkSQL.mp4 168.3 MB
25. Pivot Tables/2. Coding a Pivot Table in Spark.mp4 165.6 MB
9. Keyword Ranking Practical/3. Worked Solution (continued) with Sorting.mp4 156.3 MB
46. Streaming Chapter 3- Structured Streaming/1. Structured Streaming Overview.mp4 152.9 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/3. Using a Kafka Event Simulator.mp4 152.7 MB
39. Case Study/2. Case Study - Walkthrough Part 1.mp4 151.6 MB
24. DataFrames API/1. SQL vs DataFrames.mp4 147.2 MB
23. Ordering/1. Ordering.mp4 146.4 MB
39. Case Study/3. Case Study - Walkthrough Part 2.mp4 140.1 MB
19. In Memory Data/1. In Memory Data.mp4 139.7 MB
17. Datasets/4. Filters using Columns.mp4 139.0 MB
22. Multiple Groupings/1. Multiple Groupings.mp4 138.8 MB
8. Reading from Disk/1. Reading from Disk.mp4 138.0 MB
24. DataFrames API/2. DataFrame Grouping.mp4 137.0 MB
9. Keyword Ranking Practical/2. Worked Solution.mp4 136.0 MB
14. RDD Performance/4. Shuffles.mp4 133.7 MB
46. Streaming Chapter 3- Structured Streaming/2. Data Sinks.mp4 132.3 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/4. Starting a Streaming Job.mp4 127.9 MB
46. Streaming Chapter 3- Structured Streaming/5. What is the Batch Size in Structured Streaming.mp4 126.1 MB
37. Non-Numeric Data/2. Understanding Vectors.mp4 125.3 MB
46. Streaming Chapter 3- Structured Streaming/4. Windows and Watermarks.mp4 123.2 MB
35. Model Fitting Parameters/2. Training, Test and Holdout Data.mp4 123.1 MB
18. The Full SQL Syntax/1. Using a Spark Temporary View for SQL.mp4 120.6 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/8. Adding a Slide Interval.mp4 120.6 MB
36. Feature Selection/3. Identifying and Eliminating Duplicated Features.mp4 119.1 MB
37. Non-Numeric Data/1. Using OneHotEncoding.mp4 116.3 MB
35. Model Fitting Parameters/1. Setting Linear Regression Parameters.mp4 112.9 MB
30. HashAggregation/3. How can I force Spark to use HashAggregation.mp4 110.6 MB
10. Sorts and Coalesce/2. Why Coalesce is the Wrong Solution.mp4 109.5 MB
30. HashAggregation/2. How does HashAggregation work.mp4 109.0 MB
46. Streaming Chapter 3- Structured Streaming/3. Structured Streaming Output Modes.mp4 108.9 MB
9. Keyword Ranking Practical/1. Practical Requirements.mp4 106.4 MB
14. RDD Performance/1. Transformations and Actions.mp4 105.7 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/5. Streaming Transformations.mp4 98.0 MB
20. Groupings and Aggregations/1. Groupings and Aggregations.mp4 97.1 MB
11. Deploying to AWS EMR (Optional)/2. Packing a Spark Jar for EMR.mp4 94.7 MB
28. User Defined Functions/3. Using a UDF in Spark SQL.mp4 94.1 MB
30. HashAggregation/1. Explaining Execution Plans.mp4 93.9 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/6. Writing a Kafka Aggegration.mp4 93.7 MB
36. Feature Selection/1. Describing the Features.mp4 93.6 MB
41. Decision Trees/1. Overview of Decision Trees.mp4 91.3 MB
29. SparkSQL Performance/3. Update - Setting spark.sql.shuffle.partitions.mp4 90.5 MB
28. User Defined Functions/1. How to use a Lambda to write a UDF in Spark.mp4 88.4 MB
3. Reduces on RDDs/1. Reduces on RDDs.mp4 88.3 MB
10. Sorts and Coalesce/1. Why do sorts not work with foreach in Spark.mp4 88.2 MB
12. Joins/2. Left Outer Joins and Optionals.mp4 87.7 MB
11. Deploying to AWS EMR (Optional)/1. How to start an EMR Spark Cluster.mp4 84.5 MB
33. Linear Regression Models/3. Assembling a Vector of Features.mp4 81.5 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/2. Installing Kafka.mp4 81.3 MB
27. Practical Exercise/1. Building a Pivot Table with Multiple Aggregations.mp4 80.7 MB
1. Introduction/3.1 Practicals.zip.zip 79.2 MB
6. PairRDDs/3. Coding a ReduceByKey.mp4 78.5 MB
6. PairRDDs/2. Building a PairRDD.mp4 77.6 MB
31. SparkSQL Performance vs RDDs/1. SparkSQL Performance vs RDDs.mp4 75.4 MB
17. Datasets/1. Dataset Basics.mp4 72.5 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/8. Windowing Batches.mp4 72.1 MB
5. Tuples/2. Tuples and RDDs.mp4 71.9 MB
7. FlatMaps and Filters/1. FlatMaps.mp4 69.0 MB
17. Datasets/2. Filters using Expressions.mp4 68.7 MB
13. Big Data Big Exercise/2. Warmup.mp4 68.1 MB
34. Training Data/5. Assessing Model Accuracy with R2 and RMSE.mp4 67.1 MB
32. Module 3 - SparkML for Machine Learning/5. The Model Building Process.mp4 65.5 MB
14. RDD Performance/3. Narrow vs Wide Transformations.mp4 65.5 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/6. Streaming Aggregations.vtt 65.5 MB
41. Decision Trees/4. Random Forests.mp4 64.1 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/6. Streaming Aggregations.mp4 63.9 MB
33. Linear Regression Models/4. Model Fitting.mp4 63.2 MB
6. PairRDDs/4. Using the Fluent API.mp4 62.8 MB
36. Feature Selection/2. Correlation of Fetures.mp4 62.0 MB
29. SparkSQL Performance/2. How does SQL and DataFrame performance compare.mp4 61.4 MB
11. Deploying to AWS EMR (Optional)/3. Running a Spark Job on EMR.mp4 61.4 MB
21. Date Formatting/1. Date Formatting.mp4 60.6 MB
13. Big Data Big Exercise/11. Walkthrough - Step 9, adding titles and using the Big Data file.mp4 59.2 MB
5. Tuples/1. RDDs of Objects.mp4 59.0 MB
1. Introduction/3. Module 1 - Introduction.mp4 57.9 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/4. Integrating Kafka with Spark.mp4 57.3 MB
6. PairRDDs/1. Overview of PairRDDs.mp4 57.0 MB
33. Linear Regression Models/2. Beginning Coding Linear Regressions.mp4 56.0 MB
39. Case Study/1. Requirements.mp4 55.7 MB
6. PairRDDs/5. Grouping By Key.mp4 52.5 MB
12. Joins/1. Inner Joins.mp4 51.9 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/2. Streaming Chapter 1 - Introduction to Streaming.mp4 51.7 MB
1. Introduction/1. Welcome.mp4 51.5 MB
41. Decision Trees/3. Interpreting a Decision Tree.mp4 50.7 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/3. DStreams.mp4 49.8 MB
34. Training Data/2. Using data from Kaggle.mp4 48.9 MB
4. Mapping and Outputting/4. If you've had a NotSerializableException in Spark.mp4 48.7 MB
4. Mapping and Outputting/3. Counting Big Data Items.mp4 47.2 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/1. Overview of Kafka.mp4 47.1 MB
34. Training Data/3. Practical Walkthrough.mp4 47.0 MB
13. Big Data Big Exercise/5. Walkthrough - Step 3.mp4 46.5 MB
34. Training Data/4. Splitting Training Data with Random Splits.mp4 46.2 MB
4. Mapping and Outputting/1. Mapping Operations.mp4 45.2 MB
28. User Defined Functions/2. Using more than one input parameter in Spark UDF.mp4 45.1 MB
14. RDD Performance/2. The DAG and SparkUI.mp4 43.3 MB
14. RDD Performance/5. Dealing with Key Skews.mp4 42.9 MB
7. FlatMaps and Filters/2. Filters.mp4 42.1 MB
4. Mapping and Outputting/2. Outputting Results to the Console.mp4 41.7 MB
32. Module 3 - SparkML for Machine Learning/4. Supervised vs Unsupervised Learning.mp4 40.4 MB
25. Pivot Tables/1. How does a Pivot Table work.mp4 39.2 MB
11. Deploying to AWS EMR (Optional)/5. Calculating EMR costs and Terminating the cluster.mp4 37.8 MB
17. Datasets/3. Filters using Lambdas.mp4 36.7 MB
11. Deploying to AWS EMR (Optional)/4. Understanding the Job Progress Output.mp4 35.3 MB
43. Recommender Systems/1. Overview and Matrix Factorisation.mp4 34.9 MB
13. Big Data Big Exercise/3. Main Exercise Requirments.mp4 34.6 MB
46. Streaming Chapter 3- Structured Streaming/6. Kafka Structured Streaming Pipelines.mp4 34.6 MB
14. RDD Performance/6. Avoiding groupByKey and using map-side-reduces instead.mp4 34.1 MB
13. Big Data Big Exercise/4. Walkthrough - Step 2.mp4 33.8 MB
10. Sorts and Coalesce/3. What is Coalesce used for in Spark.mp4 33.6 MB
15. Module 2 - Chapter 1 SparkSQL Introduction/2. Introducing SparkSQL.mp4 33.5 MB
40. Logistic Regression/2. TrueFalse Negatives and Postives.mp4 33.1 MB
12. Joins/2. Left Outer Joins and Optionals.vtt 30.2 MB
12. Joins/3. Right Outer Joins.mp4 30.0 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/7. SparkUI for Streaming Jobs.mp4 27.0 MB
33. Linear Regression Models/1. Introducing Linear Regression.mp4 26.6 MB
32. Module 3 - SparkML for Machine Learning/2. What is Machine Learning.mp4 26.6 MB
15. Module 2 - Chapter 1 SparkSQL Introduction/1.1 biglog.txt.txt 24.3 MB
13. Big Data Big Exercise/8. Walkthrough - Step 6.mp4 23.1 MB
34. Training Data/1. Training vs Test and Holdout Data.mp4 22.8 MB
32. Module 3 - SparkML for Machine Learning/3. Coming up in this Module - and introducing Kaggle.mp4 22.6 MB
15. Module 2 - Chapter 1 SparkSQL Introduction/1.2 Code.zip.zip 22.2 MB
12. Joins/4. Full Joins and Cartesians.mp4 21.3 MB
13. Big Data Big Exercise/9. Walkthrough - Step 7.mp4 21.1 MB
13. Big Data Big Exercise/10. Walkthrough - Step 8.mp4 20.3 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/7. Adding a Window.mp4 20.1 MB
13. Big Data Big Exercise/7. Walkthrough - Step 5.mp4 19.0 MB
13. Big Data Big Exercise/1. Introducing the Requirements.mp4 17.4 MB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/1.1 Code.zip.zip 14.6 MB
13. Big Data Big Exercise/6. Walkthrough - Step 4.mp4 13.8 MB
30. HashAggregation/4. SQL vs DataFrames Performance Results.mp4 11.5 MB
36. Feature Selection/4. Data Preparation.mp4 10.0 MB
32. Module 3 - SparkML for Machine Learning/1.1 MLCode.zip.zip 5.6 MB
45. Streaming Chapter 2 - Streaming with Apache Kafka/3.1 viewing-figures-generation.zip.zip 3.6 MB
40. Logistic Regression/1.1 MLCodeChapters9-12.zip.zip 3.5 MB
13. Big Data Big Exercise/1.1 Practical Guide.pdf.pdf 650.5 KB
43. Recommender Systems/2. Building the Model.vtt 28.7 KB
41. Decision Trees/2. Building the Model.vtt 24.9 KB
42. K Means Clustering/1. K Means Clustering.vtt 24.4 KB
14. RDD Performance/7. Caching and Persistence.vtt 24.0 KB
2. Getting Started/2. Installing Spark.vtt 23.2 KB
38. Pipelines/1. Pipelines.vtt 23.1 KB
40. Logistic Regression/3. Coding a Logistic Regression.vtt 22.4 KB
16. SparkSQL Getting Started/1. SparkSQL Getting Started.vtt 22.0 KB
26. More Aggregations/1. How to use the agg method in Spark.vtt 20.2 KB
29. SparkSQL Performance/1. Understand the SparkUI for SparkSQL.vtt 19.9 KB
45. Streaming Chapter 2 - Streaming with Apache Kafka/5. Using KafkaUtils to access a DStream.vtt 18.8 KB
14. RDD Performance/4. Shuffles.vtt 18.6 KB
45. Streaming Chapter 2 - Streaming with Apache Kafka/3. Using a Kafka Event Simulator.vtt 18.1 KB
23. Ordering/1. Ordering.vtt 17.7 KB
9. Keyword Ranking Practical/2. Worked Solution.vtt 16.9 KB
36. Feature Selection/1. Describing the Features.vtt 16.7 KB
30. HashAggregation/2. How does HashAggregation work.vtt 16.5 KB
25. Pivot Tables/2. Coding a Pivot Table in Spark.vtt 16.5 KB
37. Non-Numeric Data/1. Using OneHotEncoding.vtt 16.1 KB
19. In Memory Data/1. In Memory Data.vtt 16.0 KB
41. Decision Trees/1. Overview of Decision Trees.vtt 15.8 KB
24. DataFrames API/1. SQL vs DataFrames.vtt 15.6 KB
35. Model Fitting Parameters/2. Training, Test and Holdout Data.vtt 15.6 KB
9. Keyword Ranking Practical/3. Worked Solution (continued) with Sorting.vtt 15.3 KB
22. Multiple Groupings/1. Multiple Groupings.vtt 15.1 KB
3. Reduces on RDDs/1. Reduces on RDDs.vtt 14.8 KB
10. Sorts and Coalesce/2. Why Coalesce is the Wrong Solution.vtt 14.7 KB
46. Streaming Chapter 3- Structured Streaming/1. Structured Streaming Overview.vtt 14.7 KB
30. HashAggregation/3. How can I force Spark to use HashAggregation.vtt 14.5 KB
18. The Full SQL Syntax/1. Using a Spark Temporary View for SQL.vtt 14.5 KB
24. DataFrames API/2. DataFrame Grouping.vtt 14.1 KB
20. Groupings and Aggregations/1. Groupings and Aggregations.vtt 14.1 KB
35. Model Fitting Parameters/1. Setting Linear Regression Parameters.vtt 14.1 KB
36. Feature Selection/3. Identifying and Eliminating Duplicated Features.vtt 14.1 KB
39. Case Study/1. Requirements.vtt 14.0 KB
8. Reading from Disk/1. Reading from Disk.vtt 14.0 KB
39. Case Study/2. Case Study - Walkthrough Part 1.vtt 13.7 KB
46. Streaming Chapter 3- Structured Streaming/2. Data Sinks.vtt 13.5 KB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/4. Starting a Streaming Job.vtt 13.4 KB
37. Non-Numeric Data/2. Understanding Vectors.vtt 13.2 KB
46. Streaming Chapter 3- Structured Streaming/3. Structured Streaming Output Modes.vtt 13.2 KB
46. Streaming Chapter 3- Structured Streaming/5. What is the Batch Size in Structured Streaming.vtt 12.9 KB
39. Case Study/3. Case Study - Walkthrough Part 2.vtt 12.8 KB
6. PairRDDs/3. Coding a ReduceByKey.vtt 12.7 KB
9. Keyword Ranking Practical/1. Practical Requirements.vtt 12.6 KB
45. Streaming Chapter 2 - Streaming with Apache Kafka/1. Overview of Kafka.vtt 12.4 KB
11. Deploying to AWS EMR (Optional)/2. Packing a Spark Jar for EMR.vtt 12.2 KB
46. Streaming Chapter 3- Structured Streaming/4. Windows and Watermarks.vtt 12.1 KB
14. RDD Performance/1. Transformations and Actions.vtt 11.8 KB
11. Deploying to AWS EMR (Optional)/1. How to start an EMR Spark Cluster.vtt 11.8 KB
14. RDD Performance/3. Narrow vs Wide Transformations.vtt 11.5 KB
33. Linear Regression Models/3. Assembling a Vector of Features.vtt 11.3 KB
32. Module 3 - SparkML for Machine Learning/4. Supervised vs Unsupervised Learning.vtt 11.1 KB
17. Datasets/4. Filters using Columns.vtt 11.1 KB
28. User Defined Functions/1. How to use a Lambda to write a UDF in Spark.vtt 10.9 KB
45. Streaming Chapter 2 - Streaming with Apache Kafka/8. Adding a Slide Interval.vtt 10.7 KB
10. Sorts and Coalesce/1. Why do sorts not work with foreach in Spark.vtt 10.7 KB
6. PairRDDs/2. Building a PairRDD.vtt 10.4 KB
5. Tuples/2. Tuples and RDDs.vtt 10.4 KB
7. FlatMaps and Filters/1. FlatMaps.vtt 10.1 KB
28. User Defined Functions/3. Using a UDF in Spark SQL.vtt 9.8 KB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/5. Streaming Transformations.vtt 9.8 KB
45. Streaming Chapter 2 - Streaming with Apache Kafka/2. Installing Kafka.vtt 9.8 KB
6. PairRDDs/1. Overview of PairRDDs.vtt 9.7 KB
40. Logistic Regression/2. TrueFalse Negatives and Postives.vtt 9.7 KB
32. Module 3 - SparkML for Machine Learning/5. The Model Building Process.vtt 9.6 KB
29. SparkSQL Performance/3. Update - Setting spark.sql.shuffle.partitions.vtt 9.5 KB
45. Streaming Chapter 2 - Streaming with Apache Kafka/6. Writing a Kafka Aggegration.vtt 9.4 KB
27. Practical Exercise/1. Building a Pivot Table with Multiple Aggregations.vtt 9.4 KB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/8. Windowing Batches.vtt 9.4 KB
13. Big Data Big Exercise/2. Warmup.vtt 9.3 KB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/2. Streaming Chapter 1 - Introduction to Streaming.vtt 9.3 KB
11. Deploying to AWS EMR (Optional)/3. Running a Spark Job on EMR.vtt 8.8 KB
12. Joins/1. Inner Joins.vtt 8.6 KB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/3. DStreams.vtt 8.5 KB
30. HashAggregation/1. Explaining Execution Plans.vtt 8.5 KB
34. Training Data/5. Assessing Model Accuracy with R2 and RMSE.vtt 8.5 KB
5. Tuples/1. RDDs of Objects.vtt 8.4 KB
33. Linear Regression Models/4. Model Fitting.vtt 8.2 KB
46. Streaming Chapter 3- Structured Streaming/6. Kafka Structured Streaming Pipelines.vtt 8.0 KB
31. SparkSQL Performance vs RDDs/1. SparkSQL Performance vs RDDs.vtt 7.8 KB
13. Big Data Big Exercise/3. Main Exercise Requirments.vtt 7.8 KB
14. RDD Performance/5. Dealing with Key Skews.vtt 7.7 KB
29. SparkSQL Performance/2. How does SQL and DataFrame performance compare.vtt 7.7 KB
33. Linear Regression Models/2. Beginning Coding Linear Regressions.vtt 7.5 KB
4. Mapping and Outputting/1. Mapping Operations.vtt 7.4 KB
33. Linear Regression Models/1. Introducing Linear Regression.vtt 7.3 KB
6. PairRDDs/4. Using the Fluent API.vtt 7.3 KB
14. RDD Performance/2. The DAG and SparkUI.vtt 7.3 KB
36. Feature Selection/2. Correlation of Fetures.vtt 7.2 KB
17. Datasets/1. Dataset Basics.vtt 7.2 KB
14. RDD Performance/6. Avoiding groupByKey and using map-side-reduces instead.vtt 7.2 KB
17. Datasets/2. Filters using Expressions.vtt 7.1 KB
25. Pivot Tables/1. How does a Pivot Table work.vtt 7.0 KB
21. Date Formatting/1. Date Formatting.vtt 7.0 KB
41. Decision Trees/4. Random Forests.vtt 6.8 KB
13. Big Data Big Exercise/5. Walkthrough - Step 3.vtt 6.8 KB
4. Mapping and Outputting/3. Counting Big Data Items.vtt 6.7 KB
15. Module 2 - Chapter 1 SparkSQL Introduction/2. Introducing SparkSQL.vtt 6.6 KB
41. Decision Trees/3. Interpreting a Decision Tree.vtt 6.6 KB
4. Mapping and Outputting/4. If you've had a NotSerializableException in Spark.vtt 6.5 KB
43. Recommender Systems/1. Overview and Matrix Factorisation.vtt 6.4 KB
13. Big Data Big Exercise/11. Walkthrough - Step 9, adding titles and using the Big Data file.vtt 6.2 KB
34. Training Data/2. Using data from Kaggle.vtt 6.1 KB
11. Deploying to AWS EMR (Optional)/4. Understanding the Job Progress Output.vtt 5.9 KB
34. Training Data/4. Splitting Training Data with Random Splits.vtt 5.8 KB
34. Training Data/1. Training vs Test and Holdout Data.vtt 5.8 KB
28. User Defined Functions/2. Using more than one input parameter in Spark UDF.vtt 5.5 KB
4. Mapping and Outputting/2. Outputting Results to the Console.vtt 5.5 KB
6. PairRDDs/5. Grouping By Key.vtt 5.5 KB
45. Streaming Chapter 2 - Streaming with Apache Kafka/4. Integrating Kafka with Spark.vtt 5.4 KB
13. Big Data Big Exercise/1. Introducing the Requirements.vtt 5.4 KB
7. FlatMaps and Filters/2. Filters.vtt 5.3 KB
1. Introduction/3. Module 1 - Introduction.vtt 5.3 KB
11. Deploying to AWS EMR (Optional)/5. Calculating EMR costs and Terminating the cluster.vtt 5.2 KB
10. Sorts and Coalesce/3. What is Coalesce used for in Spark.vtt 5.1 KB
13. Big Data Big Exercise/4. Walkthrough - Step 2.vtt 5.0 KB
36. Feature Selection/4. Data Preparation.vtt 4.8 KB
34. Training Data/3. Practical Walkthrough.vtt 4.8 KB
32. Module 3 - SparkML for Machine Learning/2. What is Machine Learning.vtt 4.6 KB
13. Big Data Big Exercise/8. Walkthrough - Step 6.vtt 4.2 KB
44. Module 4 -Spark Streaming and Structured Streaming with Kafka/7. SparkUI for Streaming Jobs.vtt 4.1 KB
12. Joins/4. Full Joins and Cartesians.vtt 4.1 KB
17. Datasets/3. Filters using Lambdas.vtt 4.0 KB
12. Joins/3. Right Outer Joins.vtt 4.0 KB
1. Introduction/1. Welcome.vtt 3.9 KB
32. Module 3 - SparkML for Machine Learning/3. Coming up in this Module - and introducing Kaggle.vtt 3.4 KB
13. Big Data Big Exercise/9. Walkthrough - Step 7.vtt 2.9 KB
13. Big Data Big Exercise/7. Walkthrough - Step 5.vtt 2.9 KB
30. HashAggregation/4. SQL vs DataFrames Performance Results.vtt 2.6 KB
13. Big Data Big Exercise/10. Walkthrough - Step 8.vtt 2.6 KB
13. Big Data Big Exercise/6. Walkthrough - Step 4.vtt 2.1 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

Udemy - Apache Spark for Java Developers

File List

Download Torrent

Related Resources

Copyright Infringement