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Apache Spark 2 for Beginners
Spark Fundamentals
The Course Overview (4:30)
An Overview of Apache Hadoop (5:50)
Understanding Apache Spark (5:13)
Installing Spark on Your Machines (13:48)
Spark Programming Model
Functional Programming with Spark and Understanding Spark RDD (8:44)
Data Transformations and Actions with RDDs (5:21)
Monitoring with Spark (4:01)
The Basics of Programming with Spark (20:30)
Creating RDDs from Files and Understanding the Spark Library Stack (6:38)
Spark SQL
Understanding the Structure of Data and the Need of Spark SQL (9:38)
Anatomy of Spark SQL (5:08)
DataFrame Programming (12:00)
Understanding Aggregations and Multi-Datasource Joining with SparkSQL (8:32)
Introducing Datasets and Understanding Data Catalogs (7:53)
Spark Programming with R
The Need for Spark and the Basics of the R Language (8:09)
DataFrames in R and Spark (2:57)
Spark DataFrame Programming with R (4:42)
Understanding Aggregations and Multi- Datasource Joins in SparkR (4:11)
Spark Data Analysis with Python
Charting and Plotting Libraries and Setting Up a Dataset (3:59)
Charts, Plots, and Histograms (5:36)
Bar Chart and Pie Chart (7:45)
Scatter Plot and Line Graph (4:53)
Spark Stream Processing
Data Stream Processing and Micro Batch Data Processing (8:36)
A Log Event Processor (16:22)
Windowed Data Processing and More Processing Options (7:26)
Kafka Stream Processing (10:43)
Spark Streaming Jobs in Production (9:09)
Spark Machine Learning
Understanding Machine Learning and the Need of Spark for it (6:22)
Wine Quality Prediction and Model Persistence (10:43)
Wine Classification (5:57)
Spam Filtering (7:07)
Feature Algorithms and Finding Synonyms (6:54)
Spark Graph Processing
Understanding Graphs with Their Usage (4:35)
The Spark GraphX Library (10:09)
Graph Processing and Graph Structure Processing (9:44)
Tennis Tournament Analysis (5:34)
Applying PageRank Algorithm (3:30)
Connected Component Algorithm (4:39)
Understanding GraphFrames and Its Queries (9:31)
Designing Spark Applications
Lambda Architecture (4:47)
Micro Blogging with Lambda Architecture (7:13)
Implementing Lambda Architecture and Working with Spark Applications (8:19)
Coding Style, Setting Up the Source Code, and Understanding Data Ingestion (9:09)
Generating Purposed Views and Queries (5:53)
Understanding Custom Data Processes (6:12)
Data Stream Processing and Micro Batch Data Processing
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