Study Guide for the Developer Certification for Apache Spark

Table of contents

1. Introduction
   1. Introduction and Course Overview 00:04:10
   2. About the Author 00:00:35
   3. Spark’s concepts and approach 00:06:04
   4. Resilient Distributed Databases (RDD) 00:05:03
   5. Creating a Project in IDEA 00:02:54
2. Spark Core API & Best practices
   1. Base RDD 00:06:46
   2. Transformations 00:05:35
   3. Actions - Part 1 00:01:40
   4. Actions - Part 2 00:02:42
   5. Hadoop Combiners In Spark 00:04:52
   6. Direct Acyclic Graph And Lazy Evaluation 00:07:20
   7. Joins 00:06:15
3. Closure serialization
   1. How does the magic of Spark works 00:07:30
   2. Serializers and how to change them 00:04:10
4. Shared variables and performance
   1. Broadcast 00:04:07
   2. Accumulators 00:05:05
   3. Caching & Persistence 00:09:22
5. Spark SQL
   1. Spark SQL 00:12:32
   2. Inferring A Schema 00:07:38
   3. Applying A Schema 00:06:27
   4. Loading And Writing 00:06:07
   5. SQL Caching And UDF 00:08:48
6. Spark MLLib
   1. Spark MLLib And Supervised Example - SVM 00:10:02
   2. Unsupervised With Iris Dataset - KMeans 00:08:54
7. Spark GraphX
   1. Graph Construction 00:07:06
   2. Graph Algorithms 00:06:52
8. Spark Streaming
   1. Streaming And The Microbatch 00:13:57
   2. Mutable Transformations And Checkpointing 00:09:07
   3. Windows And RDD Transformations 00:08:43
   4. Streaming With Spark SQL, MLLib And Core 00:12:28
9. Deployment and Infrastructure
   1. Cluster Managers And Submission - Standalone, Mesos And Yarn 00:13:20
10. Conclusion
    1. Resources And Where To Go From Here 00:04:06