Data Scientist vs Big Data

Differences Between Data Scientist vs Big Data

One Data Scientist should have knowledge of the entire flow of full data lake architecture starting from data loading till the presentation of an end user. Whereas big data is one of the parts of the entire architecture.

Data lifecycle will be like below:

Huge data came from varieties sources like Data Warehouse tools, Managed Document Repository, File Shares, Databases, and Cloud or External.
Data has been loaded to HDFS system which called Enterprise Data Lake. It can need to learn at the time of understanding big data. How that loaded and how it stores.
After data loaded successfully, there have several methods to pick those data and create one require big data dictionary. One of the very popular is Hive which handles loading the data as an alike table and supports HiveQL (which is SQL like language). It internally used map reduce program which essential to learn for understanding big data.
Now there has one other prospect to create business rules which will use big data dictionary for analytics and be reporting purpose. These business rules were written by business rule developer, who are mainly experts in statistics, mathematics, and wonderful understanding of the current business of that organization including predictive calculation.
Now business rules and big data dictionary both are ready. Now the task for report developer. They designed reporting structure in different view based on rules defined by business rule developer using big data dictionary. The report can be easily accessible and provide the future prospect for that organization.

Now, if we consider entire flow there 4 kinds of people involved for set up, deploy and presentation.

Hadoop Admin (for set up HDFS system)
Big Data Developer (responsible to load data and prepare dictionary by fetching those huge data)
Business Rule Developer (responsible for developing business rule)
Report Developer (design and presentation to end user)

Now one data scientist should have entire knowledge of above 4 parts which normally divided as individual responsibility.

Head to Head Comparison Between Data Scientist vs Big Data

Below is the Top 3 Comparison between Data Scientist vs Big Data

Key Differences Between Data Scientist vs Big Data

Some key differences are explained below between Data Scientist vs Big Data

Data Scientist has knowledge of entire data lake architecture including data loading and fetching for preparing data dictionary. Whereas big data only limited on data loading, data fetching and preparing data dictionary task.
The responsibility of data scientist on proper execution and a developed end to end flow, starting from data loading till presentation to the end user. Whereas big data ensures properly data loading and data fetching part for preparing expected data dictionary.
For improving system performance to end user on presentation, data scientist mainly dependent on big data people, as maximum performance tuning can be possible on data fetching part. Whereas big data people are fully responsible for data or speed optimization in the point of data loading and data fetching logic. People are normally involved in tuning on map reduce task or move entire set up to hive or spark based on data volume or organization requirement.
Data scientist need to have a clear knowledge of the business requirement of any organization for helping on preparing business rules or presentation logic. They are the key person to provide a proper probability of organization growth based on their business performance or current activity. Whereas big data guy does not require to know about organization business or presentation logic at all. Those guys mainly concentrate on how data from various sources load smoothly and fetching can be faster for preparing data dictionary.
Data scientist normally have basic knowledge on HDFS system set up. Whereas big data guy knows about entire set up of HDFS system, whether they involve as admin on that task or not. As working with performance tuning on data loading or data fetching are clearly related to that system set up. An increasing number of the system will automatically be impacting the performance of data loading or fetching. But everything depends on how much data is really required for that organization which again decided by Data Scientist.
Rule development is one if the key task for a data scientist, whereas big data guys can easily avoid that one.