Learn about the resilient distributed dataset (RDD) structure.
- [Instructor] Spark has a data structure called…the Resilient Distributed Dataset, or RDD for short.…These are immutable distributed collections.…They're organized into logical partitions,…and they're a form of fault-tolerant collection.…Data in resilient distributed datasets…may be kept in memory or persisted to disk.…RDDs are like parallel collections in a lot of ways.…They're groups of data of the same type or structure,…the data is processed in parallel,…and RDDs are generally faster than…working with sequential operations.…
Now, there are some differences between RDDs…and parallel collections.…RDDs are partitioned by a hash function.…Parallel collections are broken into subsets…and distributed across cores or threads…within a single server at run time.…Now, RDDs are distributed across multiple servers.…Parallel collections work across a single server.…Within RDDs, the data can be easily persisted…to permanent storage while working with the RDD.…
RDDs are broken up again into partitions,…and here is an example of a set of four partitions,…
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
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.