For a lot of new people in the industry of data, data science is the first thing they get to hear or know about, and this title sticks with them. Suddenly, everyone is either a data scientist or wants to be one.
However, this is not the sole role available in the industry. There are more roles, which are associated with data. Data scientists have been on the boundary of data for too long now, but it’s time for the emergence of Data engineers and more data-based positions.
Data engineers are the individuals who combine every part of the data ecosystem inside an institution or a company. They do this by doing stuff like:
- Capturing, accessing, inspecting, and data cleansing from systems and applications into a usable form
- Designing and maintaining structured datasets
- Creating data pipelines
- Managing and keeping track of every data system (security, scalability, and many more)
- implementing the output of data scientists in a scalable way
Doing everything mentioned above, a specific skill is primarily required: programming. Software engineers who practice data and data technologies are called data engineers.
This sets them apart from data scientists, who undoubtedly have programming knowledge but are not typically engineers. It is common for data scientists to hand off their work (for example, a suggestion system) to data engineers for real implementation.
And while it is data scientists and data analysts who perform the analysis, it is usually data engineers who create data pipelines and more systems required to ensure that everyone gets easier accessibility to the data they require (and those don’t have the access to the data who aren’t supposed to).
A solid foundation in programming and software engineering enables data engineers to create the tools that data teams and their businesses require to thrive. Or, in the words of Jeff Magnusson, “I like to think of it in terms of Lego blocks. Engineers design new Lego blocks that data scientists assemble in creative ways to create new data science.”
This takes us to the beginning of why you should consider becoming a data engineer.
Why Learn Data Engineering?
- Technically challenging: One of the functions of Python that data scientists and data analysts use the most is read_csv — out of Pandas library. This function imports tabular data from a text file into Python, enabling it to be examined and manipulated. The read_csv function exemplifies what software engineering is all about: creating abstract efficient, broad, and scalable solutions.
What does it mean, and how it applies to learning data engineering? Let’s dig a little deeper.
- Under the hood, when a computer reads a file, a highly complex process happens. However, our use of the function is straightforward; what happens behind the scenes is abstracted away from the utilization. To use read_csv effectively, you do not have to know what it’s doing “under the hood.”
- This Function also lets us specify the delimiter used in the text’s file tabular data (for example, commas, tabs, semicolons, etc.). This makes it simple to utilize with a range of CSV forms, which is music to the ears of a data scientist. There are countless other options that enable data practitioners to emphasize their goals rather than worrying about programming details.
- read_csv works efficiently and rapidly, and it is efficient to read in code as well.
- Another feature of this function lets us read files in bulk so that when a file is too big to read into the RAM of a computer, it can be read in bulk by bulk, enabling users to process files as big as they come.
- It Pays Good: According to IBM, “Jobs specifying machine learning skills pay an average of 114,000. and advertised data engineering jobs pay an average of $117,000.”
It is not shocking as to why. Data engineering skills, such as Python, SQL, and the shell routinely rank in the most-paying skills in the developer surveys of StackOverflow. And while writing this, And while writing this, there are approximately 70,000 results for the search Data Scientist on LinkedIn, and almost 112,500 results from searching Data Engineer. According to GlassDoor, the gap is even more noticeable: approx. 22,500 for data scientists vs approx. 77,100 for data engineers.
As of now you must have acknowledged the importance of data engineer certification.
- It is Beneficial Whether You Want to be One or Not: Even if you are not pursuing a career in data engineering, if you would like to work in data science, some knowledge of data engineering can be very useful. The benefits are many:
- Being a data practitioner, you are likely to be regularly asked to do tasks that imbricate with other job positions, including data engineering.
- Learning another way of looking at stuff can benefit your understanding.
- Possessing engineering skills makes you more self-sufficient.
- Learning data engineering skills can also help your team because you can be the connecting link that connects your team to the data engineering team.
Conclusion
Are you interested in becoming a data engineer? Hero Vired has the best data engineer certification course that equips you with the technique and skills of big data and engineering data systems.
