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Ultimate Apache Spark with Java Course
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Introduction
Lecture 1 - Why Spark (7:58)
Lecture 2 - Spark Components (4:04)
Lecture 3 - Creating a Spark Maven Project (9:16)
Import Source Code into Eclipse (5:51)
Lecture 4 - First Spark Application (21:24)
Lecture 5 - Spark Applications on a Cluster (11:46)
Lecture 6 - Ingesting CSV and JSON files (21:42)
Spark Java Dataset API Basics
Lecture 7 - Unioning Dataframes From Different File Formats (21:13)
Lecture 8 - Union And Set Transformations on Dataframes (29:15)
Lecture 9 - Converting between a Datasets and Dataframes (14:31)
Lecture 10 - Map and Reduce Functions (16:35)
Diving Deeper with Datasets, Dataframes, Transformations and the DAG
Lecture 11 - Using Pojos with Datasets and Dataframes (19:14)
Lecture 12 - Using Datasets for Unstructured Data (wordcount) (18:16)
Lecture 13 - Joining and Querying Dataframes (23:29)
Lecture 14 - Join Assignment + aggregation Transformations (14:05)
Lecture 15 - Transformations, Actions and the DAG (17:17)
Running Spark Jobs on the Cloud
Lecture 16 - Running Spark Jobs in EMR (part 1) (26:39)
Lecture 17 - Running Spark Jobs in EMR (part 2) (20:11)
Instructions for Configuring a Spark Stand-alone Cluster
Spark Streaming Applications
Lecture 18 - Spark Streaming a Network Socket (21:40)
Lecture 19 - Spark Streaming Files (6:23)
Lecture 20 - Using Kafka with Spark Streaming (14:19)
Machine Learning with Spark MLlib
Machine Learning Resources
Lecture 21 - Overview of Linear Regression (6:28)
Lecture 22 - Spark Java Linear Regression Example (23:04)
Overview of Logistic Regression
Lecture 23 - Spark Java Logistic Regression (Classification Algorithm) (16:03)
Lecture 24 - Overview of K-Means Clustering (7:46)
Lecture 25 - Spark Java K-Means Clustering Example (10:52)
Lecture 12 - Using Datasets for Unstructured Data (wordcount)
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