The data architect works with business leaders and data science teams to gather information requirements, translate them, and use those requirements to develop data-centric solutions. To define information requirements within an enterprise Here are the primary functions of data architecture, and how the data architect’s role relates to them: The data architecture design acts as a vision or model for the eventual interactions between data systems. What are the functions of data architecture?ĭata architecture refers to the design of different data systems within an organization, and the rules that govern how the data is collected and stored. However, an MBA or an advanced degree in data science, such as UVA’s online MSDS program, will give anyone serious about becoming a big data data architect an extra edge. The requirements often ask for a bachelor’s degree in computer science, computer engineering or relevant field. By the time they become a lead data architect, they will have a wide variety of skills, including an understanding of data modeling, data warehousing, database management, and ETL (Extract, Transform, and Load: the process of integrating raw data from various data sources into a repository such as an enterprise data warehouse).Ī data architect job description will call for proven experience in data analysis and management, with excellent analytical and problem-solving abilities. The data scientist will then use that data for analysis and to provide metrics.ĭata architects normally start off as data engineers to gain database architecture experience and gather skills in the information technology sector and related fields. The data engineer then develops, tests, and maintains data pipelines and architectures. The data architect plans and manages big data databases, they study existing data infrastructure and develop a new design to integrate current systems with a desired future state in mind. The data architect is an essential part of any enterprise company’s data science team, they have a holistic vision of the company’s architecture. Their job is to conceptualize and visualize data frameworks, they will also provide knowledge and guidance in handling disparate data sources from varied databases. The data architect understands both software engineering and statistics. The data scientist has a background in statistics and their job involves cleaning and analyzing data, and then using the data to answer questions and provide metrics to solve business problems. Data engineering builds and maintains data frameworks. The data engineer has a background in software engineering and works with big data in data lakes, cloud platforms, and data warehouses in the cloud. Those data professional roles are the data scientist, and data engineer. To understand the role of a data architect, it is useful to look at the two other main roles within data management and business analysis and see how they are differentiated from each other. These types of companies also need the data architect to help them figure out how the data-both structured and unstructured-should be stored and how it should be integrated with different IT systems.Īt the University of Virginia’s School of Data Science, we offer the online Master of Science in Data Science (MSDS) program which gives students a solid foundational knowledge of data management while also teaching students practical skills such as machine learning and programming languages so that they can prepare for a career as a data architect. ![]() Companies looking to implement any sort of robust data strategy need a data architect who knows their way around large scale databases, data analysis and machine learning, using programming languages. One of the most in-demand jobs within data science is that of the data architect. Data is the backbone of modern management and strategy. High-performing companies, as revealed in a study by McKinsey, place critical importance on this wealth of data to drive decision-making at multiple levels of the organization. While smaller companies are just being turned on to the potential of data analytics, larger companies have several different sources of data and a large amount of data activity. Companies, both large and small, are clamoring for the best talent in the market to help them efficiently capture, process, and analyze massive data sets. ![]() The demand for data science professionals is at an all time high.
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