Data is the modern-day fuel of every business. It tells everything about the business, customer behavior, market trend, etc. So, businesses are giving attention to every data and performing a detailed analysis. This type of analysis is creating more demand for professionals who are good at working with data sets. The ability to turn various types of data into actionable insights is needed, and if you are capable of doing such actions, your career will be more rewarding. This is why we often hear big data training, Hadoop certification, and other such data analysis tools. Hence, in this post, let us look at everything that helps you become a data engineer.
Basic skills required to become a data engineering
The educational qualification is a must for every data engineer. Data scientists worldwide are highly educated, and 88% have a minimum master’s degree, and 46% have PhDs. This is required since the data scientist must deal with varieties of data, and to understand them, one must have a strong educational foundation.
Complete in-depth knowledge of analytical tools is required for a data engineer, and the knowledge of the R programming language is mostly preferred. R is designed to solve many data-related problems. Over 43 percent of data engineers use R programming to solve data-related statistical problems. However, learning this programming language is not as easy as others, and it is more difficult to learn other programming languages.
The knowledge of Hadoop is not mandatory to become a data engineer, but many organizations prefer it. Also, the understanding of Pig or Hive is preferred by the recruiters. Additionally, the knowledge of cloud tools, cloud storage is also added value to your resume. Hadoop knowledge is preferred since the data generation is exceptionally growing, and if the storing capacity of your system exceeds, Hadoop knowledge is required. This is why Hadoop certification courses are getting more demand these days.
Python coding is the knowledge required for a data engineer. It is the more preferred language, and the knowledge of Perl, Java, C, C++ will be the added advantages. Python is preferred since it is more versatile, and you can use it for solving all types of data-related problems.
SQL coding and database
Recently Hadoop and NoSQL are largely used by data scientists, but a data engineer is still expected to execute queries using SQL. SQL language helps to add, extract and delete data from the database. SQL is specially designed to assist in getting access, establishing communication, and working with data.
It is becoming one of the popular technologies for dealing with big data. It is a big data computation framework similar to Hadoop, but it is faster than Hadoop. It is specifically used in data science to run complicated algorithms.
AI and Machine learning
Many data engineers are not well versed in Machine Learning and Artificial Intelligence. Hence, if you want to stand out from the rest, learning these two hot topics helps you achieve a rewarding career.
A picture says more than a thousand words, and hence the representation of data in easily understandable graphical or other pictorial representation is more useful.
The skill of dealing with data
The most important skill is the ability to work with data. The more you love dealing with data, your job becomes more fun.
To become an effective data engineer, one must understand the way their business works. This skill doesn’t come with big data Hadoop training courses, but one must try to develop it. Understanding your business problem and how it affects the business is required to come with the correct resolutions.
The most important thing is the ability to communicate effectively. A data engineer, in most cases, needs to translate the technical data into non-technical people. The sales or marketing manager, the decision-makers of organizations, need to know the meaning of data, and the data engineer needs to make them understand effectively.
The data engineer must have the skill of mingling with others. He or she needs to coordinate with managers, executives, designers, and marketers. In most cases, you must work with everyone, including customers.