You have a large dataset stored in ORC format and want to convert it to Parquet format using PySpark. Which sequence of commands correctly performs this?

hard📝 Application Q8 of 15

Hadoop - Performance Tuning

Adf = spark.read.parquet('/data/orc_files'); df.write.orc('/data/parquet_files')

Bdf = spark.read.orc('/data/orc_files'); df.write.parquet('/data/parquet_files')

Cdf = spark.read.csv('/data/orc_files'); df.write.format('parquet').save('/data/parquet_files')

Ddf = spark.read.orc('/data/orc_files'); df.save.parquet('/data/parquet_files')

Step-by-Step Solution

Solution:

Step 1: Read ORC files correctly
Use spark.read.orc() with the ORC file path to load data into a DataFrame.
Step 2: Write DataFrame as Parquet
Use df.write.parquet() with the target path to save data in Parquet format.
Final Answer:
df = spark.read.orc('/data/orc_files'); df.write.parquet('/data/parquet_files') -> Option B
Quick Check:
Read ORC then write Parquet = df = spark.read.orc('/data/orc_files'); df.write.parquet('/data/parquet_files') [OK]

Quick Trick: Read ORC, then write Parquet with correct methods [OK]

Common Mistakes:

Mixing read/write formats
Using csv() for ORC files
Wrong method chaining

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You have a large dataset stored in ORC format and want to convert it to Parquet format using PySpark. Which sequence of commands correctly performs this?

Step 1: Read ORC files correctly

Step 2: Write DataFrame as Parquet

Final Answer:

Quick Check:

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