Data Engineer vs Data Analyst vs Data Scientist: Understanding the Key Roles in Data Management

Roles, Tools and Responsibilities in Data Management

Data Engineer:

  • Data Engineering is a specialized field of engineering that centers around creating and managing systems for data collection, storage, and management. A data engineer helps developers in managing the database component.  Data engineers also contribute to creating database APIs that can be easily accessed by data analysts and data scientists. 

 

 MySQL


  • MySQL is a popular database around the world. 

  • The first version of MySQL appeared in 1995. 


          MongoDB

  • It Is Classified as a NoSQL database product.  

  • MongoDB utilizes JSON-like documents with optional schemas.  

  • MongoDB was first released In 2009. 


  • The first version of PostgreSQL appeared In 1996.


Data Analyst:

  • Data analysts are like detectives who gather, clean, organize, and analyze business data. They use their programming and math skills to crack the code and find hidden trends. Leverage suitable tools to detect, analyze, and clarify patterns and trends within complex data sets. A data analyst is involved in creating reports and dashboards to share important information with stakeholders. 

 

Power BI

  • Power BI is an interactive data visualization software product developed by Microsoft with a primary focus on business intelligence. 

  • In 2016, Microsoft released Power BI Embedded on its Azure cloud platform. 

  Tableau

  • Tableau Software is a data visualization software company focused on business intelligence. 

  

Data Scientist:

  • Data scientists use advanced data mining and analysis techniques to optimize business processes and overcome existing challenges. Data scientists use machine learning algorithms & artificial intelligence tools to make predictions and answer critical business questions. Data scientists perform predictive analytics .

 

  • Machine Learning Tools (Mining Tools): 


  • KNIME integrates various machine learning and mining components through its modular data pipelining "Building Blocks of Analytics" concept.  

  • In 2006, the first version of KNIME was released. 


  • RapidMiner is a cross-platform tool that enables data mining and analysis. 

  • In the 2018 annual software poll, RapidMiner was one of the most popular data analytics software. 


  • Artificial Intelligence Tools: 


  • Pandas (styled as Pandas) is a software library written in Python for data manipulation and analysis. 

  • The development of Pandas began in 2008. 

 

 

 

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