Getting into the field of data engineering, you must have the basic yet conceptual knowledge of the field as this would provide you with a solid foundation to develop further.
In this article, You will understand the steps in which data flows within an organization, who are data engineers, and their responsibilities.
Data Workflow
A workflow is a series of activities performed to do a specific task. Data workflow involves the steps in which data flows through an organization.
There are four general steps involved in this process:
data collection and storage,
data preparation,
exploration and visualization and finally,
experimentation and prediction
Data collection and storage: In this step, data professionals collect data from various sources such as surveys, browser history, social media etc. Due to the massive amount of data generated daily in various formats and from various sources, this task becomes a daunting one. After this collection, the data is stored in its raw format.
Data preparation: In this step, data is cleaned to enable the discovery of instances of missing or duplicated values. This step also involves the conversion of data into a more organized format. This leads to the next step
Data exploration and visualization: In this step, statistical techniques are used to identify and describe the structure of the data, the relationship between different data values and other crucial concepts which would enable the creation of visual aids (such as dashboards and diagrams) for tracking and comparing changes in datasets.
This step enables you to gain a good grasp of your data before you move on to the final step
A dataset is a collection of related data.
Experimentation and prediction: In this step, it is assumed you have a good grasp of your data and are thus ready to perform certain tasks such as running experiments or building predictive models.
Data Engineers
Having understood the flow of data in an organization the answer to the question of the role of a data engineer becomes clearer.
Data engineers are responsible for the first step of the data workflow, which involves collecting and storing data.
They are the foundation of the data workflow process as they lay the groundwork for other professionals such as data analysts, data scientists and machine learning engineers who would prepare, explore and experiment with the data. Data engineers ensure that correct data is delivered efficiently, in the right format and to the right people.
Data engineers ingest data from various sources, optimize the databases for analysis, and manage data corruption. They develop, construct, test and maintain architectures such as databases and large-scale processing systems to process and handle the massive amount of data gotten from different sources.
In simple terms, they collect data from many sources and manage it for other data professionals to access and use.
Conclusion;
data engineers are responsible for the first step in the data workflow, collecting and storing data
they lay the groundwork for data analysts, data scientists and machine learning engineers ensuring that data is not scattered, corrupted or difficult to access.
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