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Introduction to Spark
Resilient Distributed Datasets
Advanced RDDs: Pair Resilient Distributed Datasets
Advanced Spark: Accumulators, Spark Submit, MapReduce , Behind The Scenes
PageRank: Ranking Search Results
MLlib in Spark: Build a recommendations engine
Scala Language Primer
You, This Course and Us Installing Scala and Hello World Downloadable Files
What does Donald Rumsfeld have to do with data analysis? Why is Spark so cool? An introduction to RDDs - Resilient Distributed Datasets Built-in libraries for Spark Installing Spark The Spark Shell See it in Action : Munging Airlines Data with Spark Transformations and Actions Downloadable Files
RDD Characteristics: Partitions and Immutability RDD Characteristics: Lineage, RDDs know where they came from What can you do with RDDs? Create your first RDD from a file Average distance travelled by a flight using map() and reduce() operations Get delayed flights using filter(), cache data using persist() Average flight delay in one-step using aggregate() Frequency histogram of delays using countByValue() Downloadable Files
Special Transformations and Actions Average delay per airport, use reduceByKey(), mapValues() and join() Average delay per airport in one step using combineByKey() Get the top airports by delay using sortBy() Lookup airport descriptions using lookup(), collectAsMap(), broadcast() Downloadable Files
Get information from individual processing nodes using accumulators Long running programs using spark-submit Spark-Submit with Scala - A demo Behind the scenes: What happens when a Spark script runs? Running MapReduce operations Downloadable Files
What is PageRank? The PageRank algorithm Implement PageRank in Spark Join optimization in PageRank using Custom Partitioning Downloadable Files
Dataframes: RDDs + Tables Downloadable Files
Collaborative filtering algorithms Latent Factor Analysis with the Alternating Least Squares method Music recommendations using the Audioscrobbler dataset Implement code in Spark using MLlib Downloadable Files
Introduction to streaming Implement stream processing in Spark using Dstreams Stateful transformations using sliding windows Downloadable Files
The Marvel social network using Graphs Downloadable Files
Scala - A "better Java"? How do Classes work in Scala? Classes in Scala - continued Functions are different from Methods Collections in Scala Map, Flatmap - The Functional way of looping First Class Functions revisited Partially Applied Functions Closures Currying Downloadable Files
Installing Intellij Installing Anaconda [For Linux/Mac OS Shell Newbies] Path and other Environment Variables
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What will I learn?
- 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.
About the course
This course is taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data. Get your data to fly using Spark for analytics, machine learning and data science.
Get your data to fly using Spark and Scala for analytics, machine learning and data science
Let’s parse that!
- 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.
Scala Programming Constructs: Classes, Traits, First Class Functions, Closures, Currying, Case Classes.
- 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.
.. and of 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.
Who should take the 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.
Pre-requisites & Requirements
- 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++