Spark RDDs
1 Hour, 13 Minutes

Course Introduction
Review of Spark Stack
Defining Lazy Evaluation
Examining RDD Lineage
Pre-partitioning RDDs
Storing RDDs in Serialized Form
Performing Numeric Operations
Creating Custom Accumulators
Optimizing Broadcasts
Piping to External Applications
Tuning Garbage Collection
Performing Batch Importing
Determining Memory Consumption
Tuning Data Structures
Minimizing Memory Usage of Reduce Tasks
Setting the Levels of Parallelism

Data Frames and Spark SQL
43 Minutes

Creating DataFrames
Interoperating with RDDs
Examining the Load and Save Functions
Reading and Writing Parquet Files
Using JSON Dataset as a DataFrame
Reading and Writing Data in Hive Tables
Reading and Writing Data Using JDBC
Running Thrift JDBC/ODBC Server

Practice: Tuning Spark
9 Minutes

Spark Streaming

Streaming Analytics
54 Minutes

Course Introduction
Examining Discretized Streams
Ingesting TCP Socket Input Streams
Reading File Input Streams
Receiving Akka Actor Input Streams
Consuming Kafka Input Streams
Ingesting Flume Input Streams
Setting Up Kinesis Input Streams
Configuring Twitter Input Streams
Implementing Custom Input Streams
Describing Receiver Reliability

Transformations on DStreams
1 Hour, 19 Minutes

Using UpdateStateByKey Operations
Performing Transform Operations
Performing Window Operations
Performing Join Operations
Using Output Operations on DStreams
Using Data Frames and SQL Operations
Using Learning Algorithms with MLlib
Persisting Stream Data in Memory
Enabling and Configuring Checkpointing
Deploying Applications
Monitoring Applications
Reducing Batch Processing Times

Performance Tuning
19 Minutes

Setting Batch Intervals
Tuning Memory Usage
Examining the Semantics of Fault Tolerance

Practice: Transformations on Dstreams
6 Minutes
Exercise: Perform Transformations on DStreams

MLlib, GraphX, and R

Machine Learning with MLlib
1 Hour, 12 Minutes

Course Introduction
Describing Data Types
Examining Basic Statistics
Exploring Linear SVMs
Performing Logistic Regression
Using Naive Bayes
Creating Decision Trees
Using Collaborative Filtering with ALS
Clustering with K-means
Clustering with Latent Dirichlet Allocation (LDA)
Analyzing with Frequent Pattern Mining

GraphX
57 Minutes

Examining the Property Graph
Exploring the Graph Operators
Performing Analytics with Neighborhood Aggregation
Messaging with Pregel API
Building Graphs
Examining Vertex and Edge RDDs
Optimizing Representation Through Partitioning
Measuring Vertices with PageRank

R and Spark
37 Minutes

Installing SparkR
Running SparkR
Using Existing R Packages
Exposing RDDs as Distributed Lists
Interoperating with DataFrames
Using Parquet Files
Running on a Cluster

Practice: Use MLlib
10 Minutes
Exercise: Use MLlib