Reading and Writing Data

Reading in Data
...from Mounted Storage
dataset = sqlContext.read.format('csv') \
                    .options(header='true', inferSchema='true', delimiter= ',') \
                    .load('/mnt/<FOLDERNAME>/<FILENAME>.csv')
​
## or spark.read.format('csv')...
## Formats: json, parquet, jdbc, orc, libsvm, csv, text, avro
...when Schema Inference Fails
from pyspark.sql.types import *
​
schema = StructType([StructField('ID', IntegerType(), True),
                     StructField('Value', DoubleType(), True),
                     StructField('Category', StringType(), True),
                     StructField('Date', DateType(), True)])
​
dataset = sqlContext.read.format('csv') \
                    .schema(schema) \
                    .options(header='true', delimiter= ',') \
                    .load('/mnt/<FOLDERNAME>/<FILENAME>.csv')
Writing out Data
df.coalesce(1) \
   .write.format("com.databricks.spark.csv") \
   .option("header", "true") \
   .save("file.csv")
Other Resources
Apache Spark Data Sources Documentation: https://spark.apache.org/docs/latest/sql-data-sources.html​

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