Hadoop & Spark

Big data is the collection of data that is vast in size, however, growing exponentially faster with time. It’s data with so huge size and complexity that none of the traditional data management tools will store it or process it with efficiency.

Big Data is a field that treats ways in which to research, analyze, and consistently extract info from vast unstructured or structured data.

Python has various inbuilt features of supporting data processing whether it is small or huge in size. These features support processing for unstructured and unconventional data. This is the reason why Data Scientists and Big Data companies prefer to choose Python for data processing as it is considered to be one of the most important requirements in Big Data.

There are other technologies also that can process Big Data more efficiently than python. They are Hadoop and Spark.


Hadoop is the best solution for storing and processing Big Data because Hadoop stores huge files in the form of (HDFS) Hadoop distributed file system without specifying any schema.

It is highly scalable as any number of nodes can be added to enhance performance. In Hadoop data is highly available if there is any hardware failure also takes place.


Spark is also a good choice for processing a large amount of structured or unstructured datasets as the data is stored in clusters. Spark will conceive to store the maximum amount of data in memory so it can spill to disk. It will store a part of the dataset in memory and therefore the remaining data on the disk.

Toady Data Scientist’s first choice of language is Python and both Hadoop and Spark provide Python APIs that provides processing of the Big Data and also allows easy access to Big data platforms.