You want to perform analysis on a large collection of images. You want to store this data in HDFS
and process it with MapReduce but you also want to give your data analysts and data scientists
the ability to process the data directly from HDFS with an interpreted high-level programming
language like Python. Which format should you use to store this data in HDFS?

A.
SequenceFiles

B.
Avro

C.
JSON

D.
HTML

E.
XML

F.
CSV

Explanation:
Using Hadoop Sequence Files
So what should we do in order to deal with huge amount of images? Use hadoop sequence files!
Those are map files that inherently can be read by map reduce applications – there is an input

format especially for sequence files – and are splitable by map reduce, so we can have one huge
file that will be the input of many map tasks. By using those sequence files we are letting hadoop
use its advantages. It can split the work into chunks so the processing is parallel, but the chunks
are big enough that the process stays efficient.
Since the sequence file are map file the desired format will be that the key will be text and hold the
HDFS filename and the value will be BytesWritable and will contain the image content of the file.
Reference: Hadoop binary files processing introduced by image duplicates finder