Course Orientation-About the Course
Welcome to Cloud Applications, Part 2!
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Syllabus
About the Discussion Forums
Course Orientation-About Your Classmates
Updating Your Profile
Social Media
Module 1: Spark, Hortonworks, HDFS, CAP-Module 1 Information
Module 1 Overview
Module 1: Spark, Hortonworks, HDFS, CAP-Lesson 1.1: Spark
1.1.1 Motivation for Spark
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1.1.2 Apache Spark
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1.1.3 Spark Example: Log Mining
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1.1.4 Spark Example: Logistic Regression
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1.1.5 RDD Fault Tolerance
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1.1.6 Interactive Spark
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1.1.7 Spark Implementation
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Module 1: Spark, Hortonworks, HDFS, CAP-Lesson 1.2: Big Data Distros
1.2.1 Introduction to Distros
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1.2.2 Hortonworks
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1.2.3 Cloudera CDH
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1.2.4 MapR Distro
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Module 1: Spark, Hortonworks, HDFS, CAP-Lesson 1.3: HDFS
1.3.1 HDFS Introduction
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1.3.2 YARN and MESOS
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Module 2: Large Scale Data Storage-Module 2 Information
Module 2 Overview
Module 2 Introduction
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Module 2: Large Scale Data Storage-Lesson 2.1: MapReduce
2.1.1 Introduction to MapReduce with Spark
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2.1.2 MapReduce: Motivation
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2.1.3 MapReduce Programming Model with Spark
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2.1.4 MapReduce Example: Word Count
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2.1.5 MapReduce Example: Pi Estimation & Image Smoothing
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2.1.6 MapReduce Example: Page Rank
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2.1.7 MapReduce Summary
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Module 2: Large Scale Data Storage-Lesson 2.2: CAP Theorem & Eventual Consistency
2.2.1 Eventual Consistency – Part 1
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2.2.2 Eventual Consistency – Part 2
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2.2.3 Consistency Trade-Offs
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2.2.4 ACID and BASE
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2.2.5 Zookeeper and Paxos: Introduction
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2.2.6 Paxos
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2.2.7 Zookeeper
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Module 2: Large Scale Data Storage-Lesson 2.3: Distributed Key-Value Store
2.3.1 Cassandra Introduction
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2.3.2 Redis
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2.3.3 Redis Demonstration
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Module 2: Large Scale Data Storage-2.4: Scalable Databases
2.4.1 HBase Usage API
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2.4.2 HBase Internals - Part 1
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2.4.3 HBase Internals - Part 2
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2.4.4 Spark SQL
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2.5.5 Spark SQL Demo
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Module 2: Large Scale Data Storage-Lesson 2.5: Publish - Subscribe Queues
2.5.1 Kafka
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Module 3: Streaming Systems-Module 3 Information
Module 3 Overview
Module 3 Introduction
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Module 3: Streaming Systems-Lesson 3.1: Streaming
3.1.1 Streaming Introduction
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3.1.2 "Big Data Pipelines: The Rise of Real-Time"
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3.1.3 Storm Introduction: Protocol Buffers & Thrift
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3.1.4 A Storm Word Count Example
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3.1.5 Writing the Storm Word Count Example
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3.1.6 Storm Usage at Yahoo
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Module 3: Streaming Systems-Lesson 3.2: Advanced Storm
3.2.1 Anchoring and Spout Replay
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3.2.2 Trident: Exactly Once Processing
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Module 3: Streaming Systems-Lesson 3.3: Storm Internals
3.3.1 Inside Apache Storm
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3.3.2 The Structure of a Storm Cluster
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3.3.3 Using Thrift in Storm
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3.3.4 How Storm Schedulers Work
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3.3.5 Scaling Storm to 4000 Nodes
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3.3.6 Q&A with Bobby Evans (Yahoo) on Storm
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Module 3: Streaming Systems-Lesson 3.4: Spark Streaming
3.4.1 Spark Streaming
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3.4.2 Lambda and Kappa Architecture
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3.4.3 Streaming Ecosystem
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Module 4: Graph Processing and Machine Learning-Module 4 Information
Module 4 Overview
Module 4: Graph Processing and Machine Learning-Lesson 4.1: Graph Processing
4.1.1 Graph Processing
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4.1.2 Pregel - Part 1
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4.1.3 Pregel - Part 2
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4.1.4 Pregel - Part 3
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4.1.5 Giraph Introduction
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4.1.6 Giraph Example
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4.1.7 Spark GraphX
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Module 4: Graph Processing and Machine Learning-Lesson 4.2: Machine Learning
4.2.1 Big Data Machine Learning Introduction
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4.2.2 Mahout: Introduction
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4.2.3 Mahout kmeans
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4.2.4 Mahout: Naïve Bayes
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4.2.5 Mahout: fpm
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4.2.6 Spark Naïve Bayes
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4.2.7 Spark fpm
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4.2.8 Spark ML/MLlib
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4.2.9 Introduction to Deep Learning
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4.2.10 Deep Neural Network Systems
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Module 4: Graph Processing and Machine Learning-Lesson 4.3: Closing Remarks
4.3.1 Closing Remarks
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