July 2017
Intermediate to advanced
796 pages
18h 55m
English
Content preview from Scala and Spark for Big Data Analytics
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DataFrame API and SQL API
The creation of a DataFrame can be done in several ways:
- By executing SQL queries
- Loading external data such as Parquet, JSON, CSV, text, Hive, JDBC, and so on
- Converting RDDs to data frames
A DataFrame can be created by loading a CSV file. We will look at a CSV statesPopulation.csv, which is being loaded as a DataFrame.
The CSV has the following format of US states populations from years 2010 to 2016.
| State | Year | Population |
| Alabama | 2010 | 4785492 |
| Alaska | 2010 | 714031 |
| Arizona | 2010 | 6408312 |
| Arkansas | 2010 | 2921995 |
| California | 2010 | 37332685 |
Since this CSV has a header, we can use it to quickly load into a DataFrame with an implicit schema detection.
scala> val statesDF = spark.read.option("header", "true").option("inferschema", ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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