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2. Data engineering - Wikipedia

   en.wikipedia.org/wiki/Data_engineering

   Around the 1970s/1980s the term information engineering methodology (IEM) was created to describe database design and the use of software for data analysis and processing. [3] [4] These techniques were intended to be used by database administrators (DBAs) and by systems analysts based upon an understanding of the operational processing needs of organizations for the 1980s.

3. Data custodian responsibilities - Wikipedia

   en.wikipedia.org/wiki/Data_custodian

   [1] [2] Simply put, Data Stewards are responsible for what is stored in a data field, while data custodians are responsible for the technical environment and database structure. Common job titles for data custodians are database administrator (DBA), data modeler,ETL developer and data engineer.

4. Data architect - Wikipedia

   en.wikipedia.org/wiki/Data_architect

   The data architect is asked to conceptualize and influence application and interface projects, and therefore must understand what advice to give and where to plug in to steer toward desirable outcomes. Depth in data modeling and database design: This is the core skill of the data architect, and the most requested in data architect job ...

5. List of computer occupations - Wikipedia

   en.wikipedia.org/wiki/List_of_computer_occupations

   Security engineer; Software analyst; Software architect; Software design; Software engineer; Software project manager; Software quality analyst; Software test engineer (Tester) Solution architect; Support technician (Help Desk) System administrator; Systems analyst; Systems architect

6. Database administrator - Wikipedia

   en.wikipedia.org/wiki/Database_administrator

   Training for DBAs with accompanying certifications is widely available, offered by database vendors and third parties. Offerings include: [7] IBM Certified Advanced Database Administrator – DB2 10.1 for Linux, Unix and Windows [8]

7. Data science - Wikipedia

   en.wikipedia.org/wiki/Data_science

   In addition to statistical analysis, data science often involves tasks such as data preprocessing, feature engineering, and model selection. For instance, a data scientist might develop a recommendation system for an e-commerce platform by analyzing user behavior patterns and using machine learning algorithms to predict user preferences. [38] [39]