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A Comprehensive Guide On Data Engineering

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With the progress in the data space, data engineering has emerged into a separate role which works closely in collaboration with data science. These two go hand in hand to provide comprehensive solutions to add value to any business.

Data engineering definition says that, a role which majorly focuses on the end application of collecting and analyzing data.

Data scientists spend a lot of time going deep into the science behind any information and data, but they do not know how to actually make use of all this analysis and form a product for a practical end application.

For all this scientific data to be of any use in real-world, it should be applied to form a useful end product. That is where data engineering comes into the picture, they apply all the scientific data and information to produce a functioning system.

In the past few years, the roles related to functioning ahead with data have become very specific.

The job market is brimming with opportunities and as per the latest reports from the UK, the Median salary of Data Engineers has been going up by 3.85% since last 6 months.

The skill set required to specialize in one area of this vast development is very unique and centric. This is how data engineering emerged as an entirely different role than data science.

With this development, the end result of any innovation can be expected to be of top-notch quality.

Data Engineering

The Scope of Data Engineering

Data engineering and analytics as a whole is a relatively new concept but this has been in the data industry since the time the industry originated. It is just that now it has emerged as a totally different role which is comparable to data infrastructure or data architecting.

According to data engineering definition, through the employment of a data engineer, one can expect a seamless flow of data from one instance to the next one.

What Are The Tasks Involved In Big Data Engineering?

A big data engineer creates and maintains the analytics infrastructure which is responsible for enabling the different operations in the data industry.

Architectures like processing systems and databases are developed, constructed, maintained and tested by the data engineers.

Data Engineering Source: makemeanalyst

Data Engineer: Roles & Responsibilities

The data set processes prove to be useful in mining, verification, modelling and acquisition. This data set process is created and provided by the data engineers.

They have to closely monitor the new trends in the data industry and develop the algorithms so as to make the raw available data more useful in a business enterprise.

For big data engineering, a strong command on the scripting languages as well as the tools which are used in the data industry is required by a data engineer.

This is basically an IT role and having acquired technical skills like SQL database design and different programming languages, is very essential.

A data engineer is expected to use these skills to work on improving the quality and quantity of data. They are expected to do this by improving and leveraging the analytics system of data.

A data engineer would also be required to have good communication skills so that they can interact well with the various departments as well as the business leaders.

This way they can get to know and understand what exactly the business enterprise leaders want to gain ultimately from the available data sets and then work efficiently towards those ultimate gains.

In simple words, it is the responsibility of the data engineer to ensure that the concerned data is monitored, managed and then channelled effectively in a working system.

Why Is Data Engineering & Analytics So Crucial?

Data engineering and analytics is the greatest innovation which makes the development of new services and products for the success driven company possible. A data scientist would gather a lot of information and then feed it into a system or an application. This is not a long-term solution for the efficient functioning of any organization.

Data Engineering Source sudeep.co

Data Science Lifecycle

It has become more crucial now that a system should have the ability to provide answers within seconds. Automatic and frequent collection of data, integration, and cleaning is what is required in today’s fast-moving technological world.

A data engineer would possess the skills which are required for this operation and therefore proves to be an essential part of any business enterprise who plans to leverage the advantages of big data engineering.

Using the strong skill set in advanced data analytics and data science, the data scientists record and analyze the major issues concerning the business organization. This information is very important to tackle that issue for ensuring the growth of the company.

After this, a data engineer would take all the collected information and then structure it to form an application which can then be used by the company to get answers related to the operational needs.

This would mean that a data engineer is the new and advanced version of a software engineer and a data scientist. Through data engineering, the data science distribution on a larger scale can be enabled.

How Does Data Engineering Work?

Going by data engineering definition, it works in order to make the data scientists perform their job in a better way. With the use of their strong programming skills and a thorough understanding of the vast data ecosystem, the following tasks will be performed by a data engineer.

Data Engineering Source - techvelocitypartners.com

Data Engineering

(i) Data Warehouses

So once the related and important data has been collected by the data scientists, the next question which arises is, where and how can this data be stored? This is when data warehouse comes into the picture.

A data warehouse will have all the information and data which is essential to a company or organization so that it becomes easier to analyze that later on.

The raw data which is collected it is generally heterogeneous in location and its format. In order to avoid the data becoming anomalous, it is essential that this raw data be stored in a normalized form. Joins are required next to perform complex queries on the data which has been normalized.

Joins are pretty expensive and when the data collected is distributed and used on a large scale, the employment of joins becomes even more expensive. In order to avoid this, the data can be denormalized but this would again lead to an increase in the space which it occupies.

A data engineer is responsible for designing, implementation, and maintenance of these data warehouses.

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(ii) ETL Pipelines

ETL or extract, transform and load, refers to moving the data from an initial source to another space or location. The designing, implementation, and maintenance of ETL pipelines are done by a data engineer.

(iii) DevOps

The role of the data engineer demands him/her to work alongside raw compute and storage nodes. This happens more in the organizations where the roles are not delineated.

(iv) Infrastructure Tools

Even this job is more common in smaller organizations. In such organizations, the data engineer will have to work on the infrastructure tools. In a larger organization, there are separate tooling teams which work essentially on this.

Becoming A Data Engineer

To start your role as a data engineer, you need to possess a few skills. These skills can be developed over time by following the steps below.

Data Engineering Source - dataconomy.com

Becoming a Data Engineer

(i) First, you would have to earn your undergraduate degree. The preferred streams for an aspiring data engineer include software engineering, computer science engineering, information technology.

Regardless of your major, make sure that you take a few courses which are closely related to computer programming, software designing, data structuring, data architecture, and database management.

(ii) Next, you should look out for job positions which offer entry-level experience. Your best option would be to work as an IT assistant. This way you can improve your ability in applying your skills to real use.

(iii) Once you have enough experience at an entry-level job position, you can start looking out for companies and business enterprises who are looking for data engineers. You can focus your search to software corporations, computer system design companies and computer manufacturers.

(iv) Work on getting certified as a professional in this field. A number of industry certifications are available for the data engineers. Do your research and apply to get certified.

(v) You can even pursue a higher degree like masters in computer science. But this step is generally not a necessary requirement for the data engineering jobs. If you have the correct set of skill sets and professional experience then you need not worry about getting a higher degree.

Conclusion

Data engineering as a whole is a very exciting and rewarding field. Data engineering can be essential in making the data scientists highly productive.

These days, the data-driven decisions made in any organization has become more crucial and centric and with this, the data engineering opportunities have also increased.

Are you inspired by the opportunity of Data Science? You may also enroll in a Data Science Master Course for more lucrative career options in Data Science.

Deepika is a LOMA certified & mainframe trained technologist. Having worked at multiple client locations, she has been engaged in onsite client Management & project development. Currently, Deepika is engaged as a Senior Content consultant with multiple organizations spread across business verticals like blockchain, Artificial Intelligence & software solution development.

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