Scalable programming with Scala and Spark

Scalable programming with Scala and Spark

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 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.

What’s Covered:

Scala Programming Constructs: Classes, Traits, First Class Functions, Closures, Currying, Case Classes

Lot’s of cool 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

**… 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

Mail us about anything — anything! — and we will always reply :-)

#NoSQL #BigData #Spark #Scala #Spark Streaming #Spark Tutorials #Learn Spark