Data engineers form the bedrock of an organization’s analytics strategy. Earlier, the data scientists were thought as tremendously demanding professionals in the analytics industry. They handled the core machine learning (ML) tasks to drive organization’s analytics based decision making. However, with the ginormous data stored at databases reaching from diverse data sources, it became difficult for the ML guys to fetch the right data they require for analysis.
It was the time when business management realized something was missing. They felt a need for someone who could provide the right data in a small time. That’s where the role of Data Engineer begins.
Cleaning, and organizing data is the first and foremost step towards data science. In this article, we will explore who a data engineer is and what are their professional goals within an organization.
All the information mentioned in this article is derived from the source document cited at the end, or otherwise linked internally within the article.
Who is a data engineer?
Data engineers empower business users with highly credible and rich data that is easily understandable and effective for fast query processing.
This is achieved in a series of engineering steps. A data engineer fetches data from various organizational sources, cleanses it, transforms the structure of the data, and makes it available for operational and analytical processing.
The above engineering steps are compoundingly called as the ETL functions: the extraction, transformation, and loading of data. A data engineer could also perform ELT (different from ETL) to extract, and load data in a repository and then using this raw and unstructured data to perform an infinite amount of SQL queries for limitless data exploration.
What we just mentioned is the most straight forward approach towards understanding data engineering. Now let’s look at a more technical and sound definition available in data engineering literature:
“A data engineer builds complex pipelines for ingesting raw data and performs transformations and optimizations to make it available for different data consumers“
Now we know the above definition contains a lot of technical terms and might need some words with them, so, let’s just discern each of them to understand quickly.
First, a data engineer builds complex pipelines. A data pipeline is a ‘workflow’ that describes the stages the data passes through while it is transmitted from one storage system to a different storage system. It identifies objects such as source, and destination systems as well as the in-between flow of data in the ETL subsystem.
The pipeline also consists of data ingestion stage before the ETL process. Data ingestion is the process of transportation of the most relevant data from across various sources to a single repository. It transfers the copy of raw data from all the many sources such as payroll systems, inventory logs, and other enterprise resource planning (ERP) systems to a Landing server. This landing server is then accessed for the ETL process.
Next, the terms transformations and optimizations refer to the processes of making data repository efficient for transactional query and analytical processing. These are the stages of ETL where most of the engineering magic happens. These concepts are demystified at great length in a separate article: What are data engineering pipelines and how do they work?
Finally, the term data consumers refers to the end users of our engineering pipeline. They are the ones whom data engineers support in data-driven decision making. They include Business Intelligence- BI experts and ML engineers. While a data scientist role is generally speculated as an ML engineer, it’s actually not true. In Pakistan, sometimes the data engineering, BI visualization and ML development tasks are all performed by a single person: the ‘Data Scientist’.
You can learn more about the right career advice to become a Data scientist in Pakistan with our analytics expert at Dice Analytics.
Why do organizations need a data engineer?
At this point where you know the core tasks of a data engineer, you might already speculate why organizations need a data engineer. But we want to put forward some well defined data engineering goals to strengthen your theoretical background.
- A data engineer must make information easily accessible:
Ease of accessibility is the ‘fast’ access to ‘simplified’ data. A data engineer makes the contents of data more understandable for the business users. This means the labels of the data in relational tables shall reflect the thinking and jargon of a business user by avoiding the technical terms understood only by developers. The data engineer designs the optimization process such that the database design supports faster access and contains the most relevant data required by the business users.
- A data engineer must present information consistently:
Consistency in data assures its credibility. It means similar data across various data sources must represent similar information and be labeled under a common category. A data engineer ensures consistency by cleansing, and compliance to quality of data.
- A data engineer must adapt to change:
Change is inevitable in the business world. A data engineer might face new business questions that are a result of changing user needs, business conditions, business data, and technology. The data model should be such that new changes shall not disrupt the presence of historical data. If there’s a need to change the data schema, then it’s a call for data engineers to effectively plan the accommodation of new data by creating a robust data structure. All the changes shall be made transparent to the user.
Having said that, it won’t hurt concluding that data engineers are critical to any data-driven business organization to enable the management of vast data repository that carries complex relationships among data.
Hope this article has been lucid enough to realize you the importance of data engineering for business organization. Learn about how data engineers enable their engineering magic using the ETL process in our next insightful article.
Take your learning to next level
If your are wondering where to take the first step in learning data engineering, we advise you visit our comprehensive data engineering course outline at Dice Analytics. We offer two data engineering courses, each covering market leading technologies in data engineering. Our first course is at database level featuring Data Analytics using Microsoft SQL server tools, while the second course is covered at the data warehouse level featuring Teradata technology under Data warehouse and Business Intelligence.
About Dice Analytics
Dice Analytics is Pakistan’s leading training and consultancy organization. Our team is empowering the BI ecosystem of Pakistan for about 5 years now. Based in Islamabad capital we reach to remote areas of Pakistan with our hybrid training facility to support both physical as well as offline audiences. Learn more about our interactive learning approach in one of our top courses.
