Receive a summary of key features when using Scala with DataFrames.
- [Instructor] DataFrames are a real useful data structure…for data scientists working with Spark and Scala.…DataFrames are table-like data structures…and in Spark it's very easy to load data from…either Comma Separated Value files or JSON files,…and in fact several other formats are supported as well.…One of the especially useful features about DataFrames…is that we can use SQL statements to filter…and aggregate the data.…We can also join DataFrames to create new DataFrames…based on data that we already have in existing DataFrames.…
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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