Differences Between Data Scientist vs Big Data
Data Scientist has the knowledge of the entire flow of full data lake architecture starting from data loading till the presentation of an end-user. Data scientists execute and develop the flow of data from the beginning of data loading until the end-user gets the appropriate data in a presentation format. Whereas big data is one of the parts of the entire architecture. Big data is limited to data loading, fetching and preparing data dictionary task respectively. Big data make sure that the data that is loading and fetching is a part of preparing the expected data dictionary.
Data Lifecycle 
- 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 the 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 views based on rules defined by business rule developer using big data dictionary. The report can be easily accessible and provide a 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 the entire knowledge of above 4 parts which normally divided as individual responsibility.
Head to Head Comparison Between Data Scientist and Big Data
Below is the top 3 comparison between Data Scientist and Big Data:
Key Differences Between Data Scientist and Big Data
Some key differences are explained below between Data Scientist and Big Data:
- 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 a map-reduce task or move entire set up to hive or spark based on data volume or organization requirement.
- Data scientists need to have a clear knowledge of the business requirement of any organization for helping in 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 a data dictionary.
- Data scientist normally have basic knowledge on HDFS system set up. Whereas big data guy knows about the 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 is 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 of the key tasks for a data scientist, whereas big data guys can easily avoid that one.
Data Scientist and Big Data Comparison Table
Below is the Comparison table between Data Scientist and Big Data.
| BASIS FOR
COMPARISON |
Data Scientist | Big Data |
| Main Task | Ensure end to end the flow of data lake architecture, starting from data loading till presentation to end-user. | Ensure huge data loading smoothly and fetching those data for preparing big data dictionary which can be easily used for presenting end-use by applying business rules. |
| Knowledge | We should have knowledge of the entire flow, including business rules, current organization business track and user-friendly presentation for an end-user. | Should have knowledge of huge data loading smoothly from various sources, and fetching data as quickly as possible without any mistake. |
| Technology | Data Scientist normally has an idea of all the technologies or processing tools like Hive, Map Reduce, R, Spark or the related technologies or tools. | Those guys have clear ideas on data loading and data fetching related technologies or tools. There normally experts on Hive, Spark, MapReduce, Pig, Cassandra, etc. |
Conclusion
Data Scientist vs Big Data are the similar kind of specialist who helps to transfer data (came from various sources) in a presentable format which given proper identification or guidance to that specific organization about their probability of future growth or improvement points.
So as a conclusion data science can have knowledge of below entire sections
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- 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)
And big data developer have the knowledge on below:
- The process of data loading from various types of resources.
- Accepting structured and unstructured data, and managing to load those data based on system requirements.
- Full knowledge of HDFS and Map-Reduce programming.
- Knowledge of updated data engine like hive or Spark.
- Very much involved in data optimization based on the requirement of the end-user.
- One of the key member for ensuring data flow of entire data flow architecture.
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