Learn about transformations and implementation considerations on RDDs.
- [Instructor] If we're working with Spark,…we're probably working with big data…and if we're working with big data…we probably want to use statistics.…There are two general kinds of statistics.…There's descriptive statistics…which help us understand what's the shape of our data,…how do the numbers fall in various ways.…Then there's also the other branch of statistics…which help to test hypotheses and make predictions.…Let's take a quick look…at some of the statistics functionality…that comes with a Spark and RDDs.…First thing I want to do is import some packages…that we'll be using.…
So, I want to import scala.util.Random…which helps us with random number generation…and I'll import from org.apache.spark.mllib.stat.Statistics.…Okay, so I've imported a couple of packages…that we'll need.…I'll clear the screen so we can start at the top.…
First thing I want to do…let's work with a big parallel dataset…and as you may remember in our previous video,…we can create val big range,…we're going to define that…as scala.til.Random.shuffle(1 to 100000)…
Dan also focuses on using Scala with Spark, a distributed processing platform. He first describes how to work with Resilient Distributed Datasets (RDDs)—a fundamental Spark data structure—and then explains how to use Scala with Spark DataFrames, a new class of data structure specially designed for analytic processing. He wraps up the course by providing a summary of advantages of using Scala for data science.
- The advantages of Scala for data science
- Scala data types
- Scala arrays, vectors, and ranges
- Parallel processing in Scala
- Mapping functions over parallel collections
- When and when not to use parallel collections
- Using SQL in Scala
- Scala and Spark RDDs
- Scala and Spark DataFrames
- Creating DataFrames
Skill Level Intermediate
Java for Data Scientists Essential Trainingwith Charles Kelly2h 43m Intermediate
1. Introduction to Scala
2. Parallel Processing in Scala
3. Using SQL in Scala
4. Scala and Spark RDDs
5. Scala and Spark DataFrames
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