10 years ago, we might not familiar with the terminology or role named “Data Engineer”. Either the role has been evolved or the terminology have been modernized, we might say both are relevant to the current’s era of big data. Data Engineer for 10 years ago might be responsible for development of the Enterprise Data Warehouse or Data Mart but to today it was believing it is more than that.
Data Engineers' Responsibilities
The data engineer is someone who develops, constructs, tests and maintains architectures, such as databases and large-scale processing systems. The data scientist, on the other hand, is someone who cleans, massages and organizes (big) data. Data engineers deal with raw data that contains human, machine or instrument errors. The data might not be validated and contain suspect records. It will be unformatted and can contain codes that are system-specific.
The data engineers will need to recommend and sometimes implement ways to improve data reliability, efficiency, and quality. To do so, they will need to employ a variety of languages and tools to marry systems together or try to hunt down opportunities to acquire new data from other systems so that the system-specific codes, for example, can become information in further processing by data scientists. Very closely related to these two is the fact that data engineers will need to ensure that the architecture that is in place supports the requirements of the data scientists, the stakeholders and the business. Lastly, to deliver the data to the data science team, the data engineering team will need to develop data set processes for data modelling, mining, and production.
Skills Required to Become a Data Engineer
SQL
SQL serves as the fundamental skill-set for data engineers. One cannot manage an RDBMS (relational database management system) without mastering SQL. To do this, data engineer will need to go through an extensive list of queries. Learning SQL is not just about memorizing a query. Data engineer must learn how to issue optimized queries.
Data Warehousing
Get a grasp of building and working with a data warehouse; it is an essential skill. Data warehousing assists data engineers to aggregate unstructured data, collected from multiple sources. It is then compared and assessed to improve the efficiency of business operations.
Data Architecture
Data engineers must have the required knowledge to build complex database systems for businesses. It is associated with those operations that are used to tackle data in motion, data at rest, datasets, and the relationship between data-dependent processes and applications.
Coding
To link database and work with all types of applications – web, mobile, desktop, IoT – data engineer must improve programming skills. For this purpose, learn an enterprise language like Java or C#. The former is useful in open source tech stacks, while the latter can help data engineer with data engineering in a Microsoft-based stack. However, the most necessary ones are Python and R. An advanced level of Python knowledge is beneficial in a variety of data-related operations.
Apache Hadoop-Based Analytics
Apache Hadoop is an open-source platform that is used to compute distributed processing and storage against datasets. They assist in a wide range of operations, such as data processing, access, storage, governance, security, and operations. With Hadoop, HBase, and MapReduce, data engineer can further the skill sets.
Machine Learning
Machine learning is mostly linked to data science. However, if data engineer can have some idea of how data can be used for statistical analysis and data modelling, it will serve well during data engineer job.
References
1. Data Engineer Roles and Responsibility. Retrieved from https://www.simplilearn.com/data-engineer-role-article
2. Data Engineer vs Data Analyst. Retrieved from https://medium.com/@vegi/data-scientist-vs-data-analyst-vs-data-engineer-using-word-cloud-902ab83d0879
Data Scientist vs Data Engineer. Retrieved from https://www.datacamp.com/community/blog/data-scientist-vs-data-engineer
Comments