Data science is a field that is related to the processes, tools, techniques, and methodologies used to analyze, interpret, discover, and produce useful information about both structured and unstructured data gathered in large quantities. It includes all the methodologies used for analyzing and extracting information from data. Data analytics can be considered to be a section within data science where large quantities of raw data termed big data or metadata are analyzed to gain useful information. It involves applying algorithmic techniques to gain insights from the raw data.
According to the SAS institute, Hadoop is an open-source framework for storing data and running applications on groups of large data sets stored in computer clusters. It enables data analysts and other data science professionals the ability to store massive quantities of data in an organized manner that is easy for processing the data sets at a later date. It also helps data science professionals to multitask on the analysis of multiple data sets. Thus, provides the scale-up facility where initially a single computer can be used for processing a data set and after a period of time, if necessary; multiple systems can be brought together via the cluster to process a single or multiple data sets.
It is not necessary for a data scientist to now Hadoop in-depth but he/she is not solely focused on building or administering clusters. The main aim of a data scientist is to extract useful information from whichever data source they can get. But they must also be able to filter out those data that is unnecessary or redundant. They need to do this for huge data sets. Hadoop provides a suitable platform that is cheap, flexible with other software, and able to store large data sets.
The first and foremost advantage that Hadoop brings to the table for enhancing data analysis is the scalability and vast capacity addition to data science professionals. For instance, a data set needs to be analyzed and executed within 30 minutes. A single computer is able to do it in 30 minutes, so logic dictates that with an increase in the number of computers the process is completed at a shorter time. When, the data analyzed is of huge quantities this capacity addition is very crucial. A data scientist can get more computers to increase the exploration part of data analysis. He/she can upload the data set or sets onto a Hadoop platform and then analyze using questions to extract useful information. They don’t need to do additional work of trying to structure the raw data to fit in with a platform before starting the analysis.
Hadoop increases the computing power that a data scientist possesses. It is beneficial when the volume of data to be analyzed exceeds the system’s memory. The costs associated with Hadoop are relatively cheaper since it is an open-source based software framework. Data scientists can implement other programs into this too due to this flexibility. A prime example is Hadoop MapReduce
Since the majority of a data scientist’s work and time is spent on preparing data for analysis and they need to filter data for that, Hadoop provides a useful method of data exploration which reduces the time for data preparation. Data exploration here refers to the storing of data in its raw form. Thus, Hadoop is ideal for big data analysis by data scientists but it is not the only one available. Innovations by data scientists are producing new models that perform the same activities of Hadoop.