Data Scientist vs Big Data Developer: What's the Difference?

By now everyone seems to be living in the age of big data as they believe that big data is adding exceptional value to the business. Seems like it is an over-hyped technology but the truth is quite opposite to the fact as it is becoming highly beneficial to today’s fast-moving businesses to take better care of their large volume of data. As technology is innovating at a very rapid pace, both big and small organizations are embracing big data technology to empower their organizations in a better way. However, when it comes to dealing with the diverse amount of data from different sources, big data developers are helping greatly helping organizations solve big data challenges and requirements.

Working of the big data

As big data is quite a broad concept that deals with a large volume of complex data, big data is categorized into varying data formats for easier understanding. 

• Structured data includes data that is addressable for effective analysis. It is primarily based on a relational database table. For instance, OLTP, RDBMS, and other formats. 

• Unstructured data is based on binary data and characters. It is more flexible and more scalable. This data is not organized in an arranged manner and requires modeling techniques to extract information from the data. For instance, digital images, blogs, emails, Word, PDF, and others. 

• Semi-structured data is not based on the relational database but based on XML/RDF. It is more flexible and scalable than structured data. For instance, text files, and XML files.

Regardless of the type of data, big data works by three actions that include Integrate, Manage, and Analyze. 

Integrate

During data integration, big data collects data from multiple sources using different integration methods such as extract, transform, and load. While integrating, data needs to be in a proper format so that it is processed in the right way so that the business analysts can get started. 

Manage

Big data requires the storage of the data in any form for the necessary processing requirements. The data storage solution can be the cloud, most people prefer storing the data in the format in which it is already residing. 

Analyst 

Analyzing big data takes time. But once the data is prepared, it can be converted into big insights. Different big data analysis methods include deep learning, predictive analytics, and data mining. 

Know whom you need for dealing with your organization’s big data

When it comes to hiring professionals for dealing with the organization’s big data, it is important to understand whom you need either a data scientist or a big data developer. 

Who is a data scientist?

Data scientists are analytical professionals who are responsible for collecting, analyzing, interpreting extremely large volumes of data. Data scientists come from a technical backgrounds including statistics, mathematics, and computer science. 

Responsibilities of a data scientist

As a data scientist is responsible for collecting, organizing, and analyzing data, it helps businesses to make informed decisions. Data scientists make use of different techniques such as machine learning, programming, advanced statistical modeling tools, etc. to analyze large volumes of complex data. A big data scientist needs to have in-depth knowledge about R or SAS. As python is a common programming language used in data science, a data scientist needs to have a strong command of python alongside Java, C++, and Perl.

Moreover, a data scientist needs to be able to write and execute queries in SQL. Above all, the data scientist must be able to work with unstructured data available in different formats like video feeds, audio, and social media.

Who is a big data developer?

A big data developer is responsible for the programming and coding of Hadoop applications in the big data domain. Moreover, they are responsible for preparing and creating the data extraction process from a wide range of data sources. A big data developer is also responsible for creating the algorithms that help in transforming the data into different business or operational formats.

Responsibilities of big data developer

Big data developers need to have expertise in database structures, theories, principles. Moreover, big data developers need to have experience in machine learning algorithms and automated machine learning to build data learning machines and processes and generate knowledge from the volume of big data. However, as big data developers are responsible for building complex data queries to create pipelines, they should also be able to maintain the data platform. 

Besides, big data professionals should be able to perform analysis of diverse data stores and uncover data insights. Above all, big data developers need to master big data tools such as Apache Spark, YARN, MySQL, Oozie, PIG and HIVE, and many more. 

Conclusion

The increasing trend of big data in different industries reveals that big data technology is going to stay here forever. It will help many businesses thrive at a faster pace than the competitors and help businesses to evolve like never before. Depending on the needs of the data dealing of the organization, businesses need to understand whom they need for getting their big data job done professionally and efficiently.