A report by Gartner suggests that only 15% of Big Data projects ever make into production. The primary reason for this is that companies aren’t always capable of effectively handling and sorting a large amount of data. This has led to the popularity of data engineers.
Data engineering is an area of data research which focuses on the realistic application of data processing and analysis. For all of the work performed by data scientists to answer questions using vast collections of information, there must be processes for gathering and validating the information. In order to actually have some meaning attached to this job, processes must also be in place to extend it in any manner to real-world operations. These are both engineering projects: the application of technology to functional, operating processes.
Due to the value that they add, the demand for data engineers is soaring. In fact, according to Hired’s 2020 State of Software Engineers, the demand for data engineers rose by 45% in 2020. But, who is a data engineer and why is the demand for data engineers rising? Keep reading to find out!
Who Is A Data Engineer?
Data engineers provide end-users with clean data sets, modelling data in a manner that empowers end-users to address their own questions. If the data analyst spends his time reviewing data, the analytics programmer spends his time converting, evaluating, installing and recording data. Data engineers incorporate best practices in information engineering, such as version management and continuous delivery into the analytics code base.
Data engineers work on data technologies and harvesting. Their functions do not require much analysis or experimental design. Rather, they are there where all the rubber reaches the ground (actually, in the context of self-driving automobiles), building interfaces and systems for streaming and access to information. They could specialize in:
Programming
System architecture
Interface and sensor configuration
Database design and configuration
Even though data engineers don't often have the opportunity to come up with wild ideas by querying and integrating big data sources, their work is critical in creating data stores which are used in this work, and in getting those ideas and bringing them to use in the real world.
How Are Data Engineers Different?
Like any other specialist employed in computer technology space, data engineers need to be in contact with business tools. A data engineer whose profile is not sprinkled with connections to Hive, Hadoop, Spark, NoSQL, and other high-tech data management and manipulation methods is definitely not much of a data engineer.
However as important as the experience with the technological methods is, the principles of data engineering and pipeline design are much more important. Data Engineers need to have a clear intellectual knowledge of:
Relational and non-relational database design
Query execution and optimization
Logical operations
Information flow
Data models
Comparative analysis of data stores
Data engineering is somewhat similar in many respects to software engineering. Starting with a concrete objective, data engineers are involved in bringing together practical structures to accomplish the goal.
In addition to providing extensive knowledge of the database program itself, some knowledge of the basic system hardware is also useful.
Data engineers could also be requested to provide data resources for other people to consume. These pipes run the other way to those that bring information to the data warehouse. Rather they are popular APIs (Application Programming Interfaces) that offer reliable access mechanisms to back up data stores. Basically, data engineers compose interpreters for their data stores which use a consistent language to obtain data even though the stores themselves vary considerably.
Hence, to carefully understand and analyze data pointers to derive actionable pointers, it’s important to collaborate with professional agencies like SG Analytics that provide data engineering services.
