Introduction to Data Architect vs Data Engineer
An engineer who follows the data architecture by managing the design and deploying the organization’s needs into the database is called a Data architect. They work with data management systems where a plan is always there with them to maintain the database for all the requirements so that information can be collected from the right place. Raw data is changed into more useful information with the help of graphs and algorithms by a Data Engineer who works with structured and unstructured data always. Various programming languages and deep knowledge in SQL is necessary to become a data engineer.
Head to Head Comparison Between Data architect vs Data Engineer (Infographics)
Below are the top 5 differences between Data architects vs Data engineers:
Differences between Data architect vs Data engineer
Data architects work with different people in various fields such as data engineers, data scientists, data miners, and data analysts, and therefore their work is mostly with data storage, data visualization, data security, and systems access. Data architect’s role is not purely technical and not completely into the software. Hence, they act as an intermediary between the company’s management and IT department. Data engineers work with data where unstructured data is converted into structured and reliable data. They invest their time in collecting data, forming algorithms based on the same, and managing the team for working on different datasets for any predictive analysis or forecast.
Key Differences
- Data architects work closely with data scientists where data analysis is done more actively. A supporting framework is created by data engineers where they will not support any other team but work for themselves. Data architects should need knowledge in database management systems and the way they work and the people with whom they should provide access. Data engineers should have knowledge in SQL and more than one programming knowledge. The skills of data engineers are measured with the number of programming languages they know and the various cloud services they work in.
- The data framework is made into completion by a Data architect who gives the idea of the same, provides the concepts and various ideas to build it, and visualize the framework to the stakeholders so that the importance of the framework is known to all. A data engineer is someone who builds the framework based on inputs from the data architect and maintains them for all the types of data being received into the database. The data architect decides who should work on the database whereas the data engineer decides what work should be done on it.
- The input of the Data Engineer’s work is provided by the Data architect which involves a blueprint of the data frame and the specifications of the framework. Data into the framework is managed and provided by a data engineer who collects the information and adds relevant details into the data. This data is basically used by data scientists and data analysts. Based on this information of data, data architects provide insights to the company by providing necessary visualization that how the data has been changed in the past years.
- Data engineers work on any programming language, database, and any cloud platform for their data analysis work whereas data architects work on data management tools and ETL tools to manage the database and provide blueprints for the framework. Data modelling and data warehousing are the skills to be known by a data architect.
| Data Architect | Data Engineer |
| Data architects are involved in the process of data extraction, data transformation, and data loading. They provide insights to data engineers about how to organize the data and in what format the data should be presented to data scientists. | Data engineers collect the data and store it in the system and make it ready for analysis by wrangling it and fixing the data anomalies. Data cleaning is also involved here as the data in the system will be unstructured. |
| Data architects should be strong in database skills as they will decide the access roles to the database whether a user should be allowed to read and write in a database or not. Data modelling is also important for a data architect as the framework is based on their design.
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Data is collected from the source and structured in the cloud and moved to a database. This is done with the help of data pipelines and data engineers should be strong in creating the data pipelines and maintaining the pipelines and databases. |
| It is good for data architects to have machine learning skills but it is not important for them to survive in their job. They need not know about the algorithms used to transfer the data into another format. | Machine learning skills are important for data engineers as they should know about the algorithms and which algorithm should be used the incorrect places to manage the data. Knowledge of all the libraries is also important for a data engineer. |
| Though the database is designed by the database administrator, the database is managed by a data architect and hence it is important for them to know the database architecture and all the data warehousing activities being carried out in the database for a smooth flow of work. | Database administration is not a skill for data engineers but data ingestion is. It is important for a data engineer to know about data ingestion and where the data is copied after data is copied from the source. |
| Data architects should focus on the systems development and storage space of databases or containers. Their main goal should be to enlarge the data framework by managing several data points. | Data engineers should create new data applications always so that the data present should be easily accessible to the end-user. They should know where the data is placed and how an external user can access the data. This can be done by creating a web UI and an application. |
Conclusion
Both the roles are important in big data where the framework should be designed by a data architect and the framework should be built by a data engineer. The knowledge is basically the same but it is used differently in the area to make use of the expertise of both professionals.
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