A distinct discipline is emerging as the area of data science matures: data engineering. The importance of data engineers compared to data scientists is acknowledged by tech giants, including Facebook, Amazon, and Google. That’s why they recruit candidates with expertise such as data pipelines and warehouses to construct vital infrastructure.
By assisting graduate students in developing high-level data engineering skills, the finest computer science degrees keep up with this trend. Enrolling in a big data certification is the best way to migrate to this field.
Table of Contents
- What is a Data Engineer?
- Why Does Data Engineering Matter?
- Skills to Pick Up to Work in the Big Data Space
- Conclusion
Let’s dive in the Big Data and data engineers reliance on each other.
What is a Data Engineer?
The systems that enable data scientists and analysts to perform their work are designed and optimized by data engineers. To be reliable and open to people who need to work with it, every business relies on its data. The data engineer ensures that all information is received, converted, stored, and made available to other users properly.
The data engineer provides the framework on which the data analysts and scientists construct. It is the responsibility of data engineers to create data pipelines, and they also have to use sophisticated software and techniques to manage data on a scale. Data engineering tips a lot more toward a software development skill set, unlike the previous two career paths.
Data engineers may have multiple focus areas in larger organizations, such as using data resources, maintaining databases, and developing and managing data pipelines. A good data engineer allows a data scientist or analyst to concentrate on solving analytical issues, whatever the subject might be, rather than trying to transfer data from source to source.
The mindset of the data engineer is always based more on building and optimization. Examples of tasks a data engineer may be working on are the following:
- Constructing data consumption APIs.
- Integrating external or new databases through existing pipelines of knowledge.
- Usage of function transformations on new data for machine learning models.
- Monitoring and checking the device constantly to ensure optimized performance.
If you are new to this field, you can take up a big data certification for beginners.
Why Does Data Engineering Matter?
Data science is one of the most in-demand career areas in computer science; work openings rose by 56 percent over the past year. Through a review of vast databases, these big data experts discover useful insights. Their abilities are important at the highest level for developing machine learning algorithms and applications for artificial intelligence (AI).
Data scientists need data in order to work their magic. And they need a clean dataset, not just any data. That implies that they need to transform raw and messy data into a consistent format that can be used with the analytical tools of the data scientist. Like computer science, students know well, as a data set increases in size, this simple-sounding task becomes increasingly difficult and time-consuming. In reality, before it is ready to be analyzed, some data scientists spend as much as 80 percent of their time ‘wrangling’ or ‘munging’ data.
That is where the engineering of data comes in. Using programming languages like R and Python to construct data pipelines and warehouses, data engineers analyze, parse, and clean datasets. For data science to generate big data items, this infrastructure efficiently delivers clean datasets at scale. The specialized expertise of the data engineer becomes crucial to the success of a company as it grows; a startup that can only afford to employ one data scientist may have no choice but to direct 80% of its hours to data engineering. When the firm scales up, this inefficiency becomes debilitating.
Much as data scientists and data engineers may often have different positions within a company, top professionals can often come from different educational backgrounds in these fields. Data engineers are mostly machine thinkers and programmers at heart, while a data scientist will usually concentrate on math and statistical analysis.
As the data industry continues to expand, it becomes evident that it is a major benefit to your career to specialize in data engineering early in your education.
Skills to Pick Up to Work in the Big Data Space
Investing in these skills will provide the best way to kickstart your career in this space in order to get the most out of your data engineering course.
- NoSQL: NoSQL databases such as MongoDB and Couchbase are now increasingly replacing conventional Oracle, DB2, etc. SQL databases. This is because NoSQL databases are better suited to fulfill the requirements for broad data access and storage. So much so that in most areas, big data engineers with NoSQL expertise are in immediate demand.
- Apache Hadoop: Over the past few years, Apache Hadoop has seen tremendous growth. Even though Hadoop is now almost a decade old, due to its ability to produce perfectly mapped performance, many software companies still rely heavily on their clusters. You can also enroll in big data Hadoop training online.
- Setting Up Cloud Clusters: Several cloud clusters are set up based on the specifications of the enterprise to support the large volume of big data. The elasticity provided by the cloud not only makes it suitable for big data engineering, but cloud clusters often make it easier for engineers to distinguish trends by crunching vast quantities of data. Being well-versed in setting up cloud clusters will offer prominent multinational businesses enormous growth opportunities.
- Apache Spark: Apache Spark is also extremely common in roles involving big data analytics, in addition to the Hadoop system. A simpler and easier alternative to complex systems such as MapReduce, many companies are now expanding their operations and searching for Spark-experienced professionals.
Conclusion
A good data engineer saves a lot of time and effort. A data engineer works on the “back-end,” constantly developing data pipelines to ensure that the information on which the company depends is reliable and usable. To ensure that the data is correctly interpreted and that the data is accessible to the user when they need it, they can leverage all kinds of different resources. Big data engineers have an annual salary growth of about 9%, and the top salary bracket makes big data engineers the top 5% of the highest-earning roles!
