Online course for Scalable programming with Scala and Spark

About Course

• What's Spark? If you are an analyst or a data scientist, you're used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.

  Scala: Scala is a general purpose programming language - like Java or C++. It's functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark. 

  Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease. 

  Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

Topics covered in this course

• Section 1: You, This Course and Us
  	Lecture 1	You, This Course and Us Preview	02:16
  	Lecture 2	Installing Scala and Hello World	09:43
  Section 2: Introduction to Spark
  	Lecture 3	What does Donald Rumsfeld have to do with data analysis? Preview	08:45
  	Lecture 4	Why is Spark so cool?	12:23
  	Lecture 5	An introduction to RDDs - Resilient Distributed Datasets	09:39
  	Lecture 6	Built-in libraries for Spark	15:37
  	Lecture 7	Installing Spark	11:44
  	Lecture 8	The Spark Shell	06:55
  	Lecture 9	See it in Action : Munging Airlines Data with Spark	03:44
  	Lecture 10	Transformations and Actions	17:06
  Section 3: Resilient Distributed Datasets
  	Lecture 11	RDD Characteristics: Partitions and Immutability Preview	12:35
  	Lecture 12	RDD Characteristics: Lineage, RDDs know where they came from	06:06
  	Lecture 13	What can you do with RDDs?	11:08
  	Lecture 14	Create your first RDD from a file	14:54
  	Lecture 15	Average distance travelled by a flight using map() and reduce() operations	06:59
  	Lecture 16	Get delayed flights using filter(), cache data using persist()	06:10
  	Lecture 17	Average flight delay in one-step using aggregate()	12:21
  	Lecture 18	Frequency histogram of delays using countByValue()	02:10
  Section 4: Advanced RDDs: Pair Resilient Distributed Datasets
  	Lecture 19	Special Transformations and Actions	14:45
  	Lecture 20	Average delay per airport, use reduceByKey(), mapValues() and join()	13:35
  	Lecture 21	Average delay per airport in one step using combineByKey()	08:23
  	Lecture 22	Get the top airports by delay using sortBy() Preview	02:51
  	Lecture 23	Lookup airport descriptions using lookup(), collectAsMap(), broadcast()	10:56
  Section 5: Advanced Spark: Accumulators, Spark Submit, MapReduce , Behind The Scenes
  	Lecture 24	Get information from individual processing nodes using accumulators	09:25
  	Lecture 25	Long running programs using spark-submit Preview	07:11
  	Lecture 26	Spark-Submit with Scala - A demo	06:09
  	Lecture 27	Behind the scenes: What happens when a Spark script runs?	14:30
  	Lecture 28	Running MapReduce operations	10:53
  Section 6: PageRank: Ranking Search Results
  	Lecture 29	What is PageRank? Preview	16:44
  	Lecture 30	The PageRank algorithm	06:15
  	Lecture 31	Implement PageRank in Spark	09:45
  	Lecture 32	Join optimization in PageRank using Custom Partitioning	06:28
  Section 7: Spark SQL
  	Lecture 33	Dataframes: RDDs + Tables	15:47
  Section 8: MLlib in Spark: Build a recommendations engine
  	Lecture 34	Collaborative filtering algorithms Preview	12:19
  	Lecture 35	Latent Factor Analysis with the Alternating Least Squares method	11:39
  	Lecture 36	Music recommendations using the Audioscrobbler dataset	05:38
  	Lecture 37	Implement code in Spark using MLlib	14:45
  Section 9: Spark Streaming
  	Lecture 38	Introduction to streaming Preview	09:55
  	Lecture 39	Implement stream processing in Spark using Dstreams	09:19
  	Lecture 40	Stateful transformations using sliding windows	08:17
  Section 10: Graph Libraries
  	Lecture 41	The Marvel social network using Graphs	14:30
  Section 11: Scala Language Primer
  	Lecture 42	Scala - A "better Java"? Preview	10:13
  	Lecture 43	How do Classes work in Scala?	11:02
  	Lecture 44	Classes in Scala - continued	15:50
  	Lecture 45	Functions are different from Methods	07:30
  	Lecture 46	Collections in Scala	10:12
  	Lecture 47	Map, Flatmap - The Functional way of looping	11:36
  	Lecture 48	First Class Functions revisited	08:46
  	Lecture 49	Partially Applied Functions	07:31
  	Lecture 50	Closures	08:07
  	Lecture 51	Currying	10:34
  Section 12: Supplementary Installs
  	Lecture 52	Installing Intellij	12:42
  	Lecture 53	Installing Anaconda	09:00

What you get from this course?

• Good Stuff :

  • Music Recommendations using Alternating Least Squares and the Audioscrobbler dataset
  • Dataframes and Spark SQL to work with Twitter data
  • Using the PageRank algorithm with Google web graph dataset
  • Using Spark Streaming for stream processing 
  • Working with graph data using the  Marvel Social network dataset 

   course all the Spark basic and advanced features: 

  • Resilient Distributed Datasets, Transformations (map, filter, flatMap), Actions (reduce, aggregate) 
  • Pair RDDs , reduceByKey, combineByKey 
  • Broadcast and Accumulator variables 
  • Spark for MapReduce 
  • The Java API for Spark 
  • Spark SQL, Spark Streaming, MLlib and GraphX

  Features :

  • Use Spark for a variety of analytics and Machine Learning tasks
  • Understand functional programming constructs in Scala
  • Implement complex algorithms like PageRank or Music Recommendations
  • Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
  • Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX
  • Write code in Scala REPL environments and build Scala applications with an IDE

Who should purchase this course?

• • Engineers who want to use a distributed computing engine for batch or stream processing or both
  • Analysts who want to leverage Spark for analyzing interesting datasets
  • Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
  • All examples work with or without Hadoop. If you would like to use Spark with Hadoop, you'll need to have Hadoop installed (either in pseudo-distributed or cluster mode).
  • The course assumes experience with one of the popular object-oriented programming languages like Java/C++