Scattering Programming with Scala and Spark – Scalable Programming Tutorial with Scala and Spark
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++. Its 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.
Table of Contents:
– You, This Course and Us
– 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
– Spark SQL
– MLlib in Spark: Build a recommendations engine
– Spark Streaming
– Graph Libraries
– Scala Language Primer
– Supplementary Installs
Manufacturer: Pakkt Publishing
Language of instruction: English
Level of training: Elementary, Secondary
Time of training: 9 hours +
File size: 5000 MB
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