Spark in Action, Second Edition
Focused View
15:43:43
54 View
00001 Chapter 1. Introduction to Apache Spark.mp4
08:53
00002 Chapter 1. What Spark brings to the table.mp4
06:52
00003 Chapter 1. Spark components.mp4
06:56
00004 Chapter 1. Spark program flow.mp4
08:57
00005 Chapter 1. Setting up the spark-in-action VM.mp4
06:45
00006 Chapter 2. Spark fundamentals.mp4
07:11
00007 Chapter 2. Using the VM s Hadoop installation.mp4
05:33
00008 Chapter 2. Using Spark shell and writing your first Spark program.mp4
11:06
00009 Chapter 2. Basic RDD actions and transformations.mp4
05:30
00010 Chapter 2. Using the distinct and flatMap transformations.mp4
09:01
00011 Chapter 2. Obtaining RDD s elements with the sample take and takeSample operations.mp4
05:09
00012 Chapter 2. Double RDD functions.mp4
09:12
00013 Chapter 3. Writing Spark applications.mp4
11:21
00014 Chapter 3. Developing the application.mp4
09:49
00015 Chapter 3. Running the application from Eclipse.mp4
11:04
00016 Chapter 3. Broadcast variables.mp4
06:39
00017 Chapter 3. Submitting the application.mp4
09:05
00018 Chapter 3. Using spark-submit.mp4
05:29
00019 Chapter 4. The Spark API in depth.mp4
05:48
00020 Chapter 4. Basic pair RDD functions.mp4
08:18
00021 Chapter 4. Using the flatMapValues transformation to add values to keys.mp4
08:13
00022 Chapter 4. Understanding data partitioning and reducing data shuffling.mp4
07:51
00023 Chapter 4. Understanding and avoiding unnecessary shuffling.mp4
10:21
00024 Chapter 4. Repartitioning RDDs.mp4
08:15
00025 Chapter 4. Joining sorting and grouping data.mp4
11:23
00026 Chapter 4. Joining data.mp4
07:05
00027 Chapter 4. Sorting data.mp4
10:05
00028 Chapter 4. Grouping data.mp4
07:41
00029 Chapter 4. Understanding RDD dependencies.mp4
09:47
00030 Chapter 4. Using accumulators and broadcast variables to communicate with Spark executors.mp4
07:22
00031 Chapter 4. Sending data to executors using broadcast variables.mp4
06:38
00032 Chapter 5. Sparkling queries with Spark SQL.mp4
09:58
00033 Chapter 5. Creating DataFrames from RDDs.mp4
07:47
00034 Chapter 5. Creating a DataFrame from an RDD of tuples.mp4
08:09
00035 Chapter 5. DataFrame API basics.mp4
08:00
00036 Chapter 5. Using SQL functions to perform calculations on data.mp4
11:14
00037 Chapter 5. Working with missing values.mp4
05:43
00038 Chapter 5. Grouping and joining data.mp4
10:17
00039 Chapter 5. Beyond DataFrames - introducing DataSets.mp4
05:19
00040 Chapter 5. Table catalog and Hive metastore.mp4
06:48
00041 Chapter 5. Executing SQL queries.mp4
08:32
00042 Chapter 5. Saving and loading DataFrame data.mp4
05:20
00043 Chapter 5. Saving data.mp4
10:19
00044 Chapter 5. Catalyst optimizer.mp4
11:28
00045 Chapter 6. Ingesting data with Spark Streaming.mp4
09:32
00046 Chapter 6. Creating a discretized stream.mp4
08:55
00047 Chapter 6. Saving the results to a file.mp4
07:19
00048 Chapter 6. Saving the computation state over time.mp4
08:01
00049 Chapter 6. Specifying the checkpointing directory.mp4
06:18
00050 Chapter 6. Using window operations for time-limited calculations.mp4
06:58
00051 Chapter 6. Using external data sources.mp4
05:52
00052 Chapter 6. Changing the streaming application to use Kafka.mp4
09:18
00053 Chapter 6. Performance of Spark Streaming jobs.mp4
10:28
00054 Chapter 6. Structured Streaming.mp4
11:44
00055 Chapter 7. Getting smart with MLlib.mp4
12:11
00056 Chapter 7. Classification of machine-learning algorithms.mp4
10:27
00057 Chapter 7. Linear algebra in Spark.mp4
10:23
00058 Chapter 7. Distributed matrices.mp4
03:44
00059 Chapter 7. Linear regression.mp4
07:00
00060 Chapter 7. Expanding the model to multiple linear regression.mp4
05:18
00061 Chapter 7. Analyzing and preparing the data.mp4
11:20
00062 Chapter 7. Fitting and using a linear regression model.mp4
07:22
00063 Chapter 7. Tweaking the algorithm.mp4
10:51
00064 Chapter 7. Plotting residual plots.mp4
09:43
00065 Chapter 7. Optimizing linear regression.mp4
10:31
00066 Chapter 8. ML - classification and clustering.mp4
09:41
00067 Chapter 8. Logistic regression.mp4
06:59
00068 Chapter 8. Preparing data to use logistic regression in Spark.mp4
08:58
00069 Chapter 8. Training the model.mp4
12:31
00070 Chapter 8. Performing k-fold cross-validation.mp4
07:51
00071 Chapter 8. Decision trees and random forests.mp4
06:51
00072 Chapter 8. Decision trees.mp4
08:06
00073 Chapter 8. Random forests.mp4
03:49
00074 Chapter 8. Using k-means clustering.mp4
03:59
00075 Chapter 8. K-means clustering.mp4
11:02
00076 Chapter 8. Summary.mp4
03:01
00077 Chapter 9. Connecting the dots with GraphX.mp4
09:54
00078 Chapter 9. Transforming graphs.mp4
10:03
00079 Chapter 9. Graph algorithms.mp4
13:16
00080 Chapter 9. Implementing the A search algorithm.mp4
04:59
00081 Chapter 9. Implementing the A algorithm.mp4
11:37
00082 Chapter 9. Summary.mp4
02:47
00083 Chapter 10. Running Spark.mp4
11:42
00084 Chapter 10. Job and resource scheduling.mp4
10:01
00085 Chapter 10. Data-locality considerations.mp4
06:57
00086 Chapter 10. Configuring Spark.mp4
07:49
00087 Chapter 10. Spark web UI.mp4
06:25
00088 Chapter 10. Running Spark on the local machine.mp4
06:29
00089 Chapter 11. Running on a Spark standalone cluster.mp4
05:34
00090 Chapter 11. Starting the standalone cluster.mp4
06:37
00091 Chapter 11. Viewing Spark processes.mp4
05:45
00092 Chapter 11. Standalone cluster web UI.mp4
08:31
00093 Chapter 11. Specifying extra classpath entries and files.mp4
07:09
00094 Chapter 11. Spark History Server and event logging.mp4
08:16
00095 Chapter 11. Creating an EC2 standalone cluster.mp4
07:23
00096 Chapter 11. Using the EC2 cluster.mp4
07:30
00097 Chapter 12. Running on YARN and Mesos.mp4
09:09
00098 Chapter 12. Resource scheduling in YARN.mp4
06:58
00099 Chapter 12. Configuring Spark on YARN.mp4
05:06
00100 Chapter 12. Configuring resources for Spark jobs.mp4
08:15
00101 Chapter 12. Finding logs on YARN.mp4
09:44
00102 Chapter 12. Running Spark on Mesos.mp4
09:44
00103 Chapter 12. Installing and configuring Mesos.mp4
04:52
00104 Chapter 12. Mesos resource scheduling.mp4
09:12
00105 Chapter 12. Running Spark with Docker.mp4
10:52
00106 Chapter 13. Case study - real-time dashboard.mp4
06:30
00107 Chapter 13. Running the application.mp4
08:58
00108 Chapter 13. Starting the application manually.mp4
04:23
00109 Chapter 13. Understanding the source code.mp4
09:24
00110 Chapter 13. The StreamingLogAnalyzer project.mp4
08:52
00111 Chapter 14. Deep learning on Spark with H2O.mp4
07:01
00112 Chapter 14. Using H2O with Spark.mp4
11:46
00113 Chapter 14. Performing regression with H2O s deep learning.mp4
11:11
00114 Chapter 14. Building and evaluating a deep-learning model using the Sparkling Water API.mp4
04:41
00115 Chapter 14. Performing classification with H2O s deep learning.mp4
10:05
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Manning Publications
View courses Manning PublicationsManning Publications is an American publisher specializing in content relating to computers. Manning mainly publishes textbooks but also release videos and projects for professionals within the computing world.
- language english
- Training sessions 115
- duration 15:43:43
- Release Date 2023/11/06
