Data Analyst Vs Data Engineer: How Do They Differ In The Technical Guild
With careers in data science booming in the recent years, young graduates or even seasoned IT professionals are interested to be data science connoisseurs. But, what exactly would the job roles be in data science? People looking to kick start their career in this field might often be stuck and feel clueless. This article will help enthusiasts choose two mainstream roles, the data analyst and the data engineer, which are quite popular in the field. The article presents what to master before you ace these two distinct roles. Data Analyst : The Analyser and Visualiser The role of a data analyst in an organisation entails dealing with tasks such as data extraction, data cleansing, data exploration and data visualization. The analyst is not just restricted to performing these tasks but also research to find the right data to fit the client/customer requirements. In addition, data is to be handled using statistical methods, and therefore he/she should analyse a large number of sources pertaining to data. On top of that, he/she should have an eye for detail to go through various data reports to sharpen reporting and auditing skills. Not to mention teamwork, which is also an essential factor. All these may seem intimidating at first, but with consistent efforts and keen interest it will be a cakewalk. Educational Background A degree (bachelor’s/master’s) in statistics or computer science is usually preferred. Streams related to these fields are also considered. Most importantly, the candidate should have a strong liking towards mathematics and statistics since he/she deals with analysing data on a regularly basis in the role. Technical Skills requirement When it comes to technical skills of a data analyst, the options are diverse. The candidate should be well-versed in programming skills as well as visualising data. The top programming languages and data visualisation tools which are hot news in the current market are listed below. Programming R SAS Python Data Visualisation Tableau Qlikview Power BI The software listed above are not just limited to data analyst tasks but also help with domains such as business intelligence and data mining. Tip : Data analysis is critical for any large-scale business these days. It is suggested that the candidates be thorough with the market scenario. On top of that, he/she  should introspect whether the career deems fit for their knowledge and interests. Data Engineer : The Architect and Caretaker Data analysts are often confused with data engineers since certain skills such as programming almost overlap in their respective domains. But, there is a distinct difference among these two roles. A data engineer builds infrastructure or framework necessary for data generation. The engineers work on the architecture aspect of data, such as data collection, data storage, data management among many other tasks. Their primary focus would be database management and big data technologies. Notably, data warehousing is one specific area of interest when it comes to data extraction. Educational Background A degree in Computer Science or Information Technology is a must for anyone to be a data engineer. Certifications from top tech companies such as Google and IBM who offer on-the-job training, will be an added advantage and increase the chances of securing data engineering jobs as well as enhance one’s career growth in this field. Technical skills requirement Since the job role mainly concentrates on database systems, an exhaustive knowledge of Structured Query Language (SQL) is mandatory. In contrast, there is another popular database system called NoSQL, in which the database modelling totally deviates from SQL. The knowledge of both technologies is essential if one wants to expand his/her horizon over the data engineering domain. Along with this, Big data has been catching up lately in this field too. There are a host of big data tools to learn for managing large amounts of data.The popular ones are mentioned below. Apache Hadoop Apache Hive Apache Spark The popular databases which rely on NoSQL are also listed below. Apache Cassandra MongoDB Couchbase A beginner may choose to master the above mentioned tools since they offer more features and are still the best in IT sector. When it comes to choosing big data tools, the options are numerous. It is recommended that the data engineer should look into the scalability and flexibility aspects for a project before choosing a tool of his/her choice. Tip : The role of a data engineer is quite challenging. Therefore, it is suggested that any beginner in this field has a vast outlook towards learning database architecture and constantly up-skill with the latest related technologies. Data Analyst vs Data Engineer in a nutshell Conclusion: The article highlights the job roles of a typical data analyst and data engineer in brief so that the reader gets a good understanding of what the work involves. The jobs are also enticing and also offer better career opportunities. One should research better before they take a final frontier in these data science careers.
With careers in data science booming in the recent years, young graduates or even seasoned IT professionals are interested to be data science connoisseurs. But, what exactly would the job roles be in data science? People looking to kick start their career in this field might often be stuck and feel clueless. This article will […]
People looking to kick start their career in this field might often be stuck and feel clueless. This article will help enthusiasts choose two mainstream roles, the data analyst and the data engineer, which are quite popular in the field.
["Big Data", "big data in auditing", "data analyst", "data engineer", "Data Science", "database management", "informatica", "programming"]
["data science", "Go", "informatica", "data engineer", "AI", "programming", "MongoDB", "Apache Spark", "Scala", "database management", "RAG", "Python", "data analyst", "big data in auditing", "SQL", "Data Science", "Big Data", "R"]
["AI", "data science", "RAG", "Apache Spark", "MongoDB", "Python", "R", "SQL", "Go", "Scala"]
I research and cover latest happenings in data science. My fervent interests are in latest technology and humor/comedy (an odd combination!). When I'm not busy reading on these subjects, you'll find me watching movies or playing badminton.