Course: 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

Course: 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

Course: 9 Minutes

Streaming Analytics

Course: 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

Course: 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

Course: 19 Minutes

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

Practice: Transformations on Dstreams

Course: 6 Minutes

• Exercise: Perform Transformations on DStreams

Machine Learning with MLlib

Course: 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

Course: 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

Course: 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

Course: 10 Minutes

• Exercise: Use MLlib