With vast quantities of data pouring into companies of all sizes and in different industry verticals, it is no surprise that there is a huge requirement for data science.
This is looked upon as a valuable source of insights that could guide strategic decision-making for companies and keep them ahead of their competition.
This is why data engineers with the requisite skill sets are in great demand and command impressive salaries.
What exactly is data engineering?
Data science is the more famous discipline, but at its core lies data engineering. The latter looks at collecting and analyzing data i.e. the practical tasks within the glamor of data science.
It looks to build and maintain the data pipelines in an organization by applying science and technology. The aim is to deal with problems in handling and processing data to be used in a data science project.
It essentially serves as the backbone, the foundation of a data science endeavor, and skill in this discipline is essential for someone working in the field of data science.
What does a data engineer do?
A data engineer, also known as a big data engineer, is tasked with ensuring that access to the data pipelines of an organization is clean and reliable.
To do this, he or she needs to set up the architecture and infrastructure needed to generate data.
For this, a data engineer makes use of:
- Querying languages: such as SQL
- Scripting languages: such as Python
- Extract-Transform-Load (ETL) tools
The scale of the system varies as per the requirement of the client. If a small neighborhood store requires data engineering, it could make do with a small-scale relational database management system (RDBMS).
However, a Fortune 500 company would find this insufficient, and may instead need a data lake for similar purposes.
How good is the demand for data engineers?
As per a Dice report of 2019, there was an 88% growth in the demand for big data engineers from 2018 to 2019. Given that there are not enough skilled professionals, the annual salaries are also quite high, at an average of INR 785,438 (USD 10,400).
What steps are involved in becoming a data engineer?
There is no uniform path to a career in data engineering. However, any path must include the following components:
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- A bachelor’s degree:
Given the specialized nature of work, a candidate must have an undergraduate degree in computer science, mathematics, information technology, or related fields.
In case a candidate has none of these, it is advised to take up online courses on algorithms, database management, and basic programming, among others. A data science certificate is a great way of picking up the skills and know-how required.
- Core data engineering skills:
A big data engineer must be skilled in the use of database tools and querying languages (SQL), distributed systems (Hadoop, Kafka, Spark), and programming languages (Python, R) for statistics and modeling.
He or she must also know about operating systems and machine learning, among others. Other essential areas include database management; designing and building data warehouses; distributed systems and big data tools; and at least one operating system.
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- Work experience:
Projects are a great way to get practical experience for a fresher. It helps to look for projects offering the skills one is looking for and to closely monitor if the project is indeed offering those skills.
Some data engineer programs do cover modules and assignments with these skills. The candidate should also create a project portfolio on GitHub.
The data scientist job attracts all the attention, but remember that a big data engineer is the one who provides a high-quality date for the former. Combining the skills of a data analyst and a data scientist, a data engineer is a very important part of a successful project in data science.