If you're an individual who has a keen interest in numbers and data, using and exploring tools and skills that help in making sense of data and extracting some useful insights, data engineering might be the right choice for your career. Also, in the last decade, the use of the internet has increased to another level which results in a high amount of data generation. So to know all about data engineering and what is a data engineer, continue reading this guide till the end.
What is Data Engineering? Who is a Data Engineer?
Data engineering is collecting, presenting, and maintaining the data in a format that is easily and readily usable by the data scientist. Data engineering is a broad field in itself and has various elements like data mining, data modeling, data acquiring, and many more, which we'll be seeing later in detail.
Big companies and organizations need lots of insights from data scientists. And therefore, they can extract and collect huge loads of data for them. But, this data is highly unstructured and cannot be directly used by data scientists; therefore, they need data engineers to process the data. So, you might want to know what exactly data engineers do. Let's see that also.
Data engineering is a technical job that mainly involves preparing and arranging the data for the analytics purposes needed by data scientists. These software engineers are often in charge of constructing data pipelines to combine data from various source systems. They prepare the data for use in analytics applications by integrating, consolidating, and cleaning it. The amount of data that these engineers work with depends totally upon the size of the organization they work for.
Now, we guess, you got a basic idea of what is data engineering and who is a data engineer. Let's come to our next topic of how to become a data engineer.
The Path to Becoming a Successful Data Engineer
Below mentioned steps will answer your question about how to become a data engineer. These steps will take you on the path to becoming a successful data engineer.
Acquire the necessary data engineer skills.
As mentioned earlier, data engineering is a technical domain that requires a list of mandatory skills. To build a career as a data engineer, one can start by learning skills like coding, ETL, data storage, machine learning, and more. These skills are explained in detail in the later part of this guide.
Learn Advanced Mathematics
A data engineer should be proficient in vector calculus, and linear algebra, differential equations, by which we mean advanced mathematics. You don't need to worry about studying them directly because most high school textbooks cover these maths concepts.
The important element in the data engineering career path is to get certified for the skills that you have. Having a valid certification for your skills showcases that you have learned those concepts. You can explore various in-demand programs and courses from a well-trusted platform and go ahead with the learnings.
Fundamentals Of Cloud Computing
Working on the cloud is incredibly helpful for data engineers. This gives them unrestricted access to the data for use in their work. A further benefit of the widespread use of cloud computing technology is that managing complicated business operations wholly in the cloud has gotten much simpler.
Create A Portfolio
The way to get a job as an entry-level data engineer and take a step further in your data engineer roadmap is by applying the skills you've learned and start creating a portfolio. Having an active portfolio will help you to get into a data engineering career.
Earlier, we mentioned the skills that you must become a data science engineer. Let's see data engineer key skills in detail.
Data Engineer Key Skills
The must-have data engineer skills are:
Coding and programming languages
As mentioned earlier in the guide, to become a data engineer it is important to have technical knowledge like coding and programming languages. Recruiters and employees favor the candidates who have such knowledge despite being freshers in the field. Some of the important languages you can start with are C, C++, Ruby, Python, and more.
Data mining, which involves extracting crucial information to detect patterns in enormous data sets and preparing them for analysis, is another crucial ability for handling big data. Data classification and forecasting are made easier using data mining. Big data specialists need to be familiar with a variety of data mining tools, including Rapid Miner, Apache Mahout, KNIME, Weka, and others.
By spotting trends and patterns, machine learning integration helps speed up the big data processing process. The incoming data can be categorized using machine learning algorithms, which can also spot trends and turn the data into insights. Strong mathematical and statistical foundations are necessary to understand machine learning.
Big data can be effectively used by businesses with the aid of data warehouses and ETL. Data is extracted, transformed, and loaded into the warehouse using the ETL, or extract, transform, load, process. Talend, InformaticaPowerCenter, Amazon Glue, Stitch, and other well-known ETL tools are a few examples.
Despite being non-technical, communication skills are one of the important skills one needs to have to become a data engineer. Because data engineers work with both technical and non-technical colleagues, excellent communication skills are crucial.
Algorithms & Data Structures
To choose suitable algorithms and data structures, you must have a thorough understanding of how each one operates. You must pick a data structure that is appropriate for your needs. Making the wrong decisions can cause your systems to behave unexpectedly or with serious performance issues.
Big data specialists frequently use visualization tools in their work. The created insights and learnings must be presented in a way that the end users may easily consume them. Tableau, Qlik, TibcoSpotfire, Plotly, and other widely used visualization technologies can all be mastered.
So, these were the skills required to become a data engineer. The next part of our blog is about data engineers' jobs or, basically, are data engineers in high demand?
Are Data Engineers In High Demand?
Many of you, who are new to this domain and are still exploring technical jobs, might think that data science is a highly in-demand career. While it certainly is true, people tend to get confused between data science and data engineering. Apart from this, there are various job titles and fields available that deal with data. Coming back to the point, data engineering job is in-demand due to various reasons. Like big organizations often spend a huge amount of their budget on collecting data. But before processing the data for the data scientist, there is a requirement to process the data, which is performed by data engineers. And therefore, there is a huge demand for data engineers in the market.
Every day, new data is produced, and this trend will only continue. The demand for engineers who can handle data will rise due to the absurd amount of data. And, if you're a little bit aware of the economics and the theory of demand and supply, you might have already guessed the condition of data engineer salary, which without a doubt is our next point of discussion.
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Data Engineer Salary
The overall Average Engineer income in India, according to Glassdoor, is around Rs. 8,56,643 LPA. Of course, several variables affect the Data Engineer compensation, such as the company's size and reputation, location, educational background, position held, and experience. Data engineers are typically paid well by reputable businesses and major participants in the Big Data sector. Also, your industry value will increase the more Big Data-related employment experience you have.
Now, after knowing the demand and salary details of the field, if you're also confused between data science and data engineering, worry not. Let's see the difference between the two.
Data Engineer And Data Scientist
A data specialist who organizes the information system for analysis is known as a data engineer. They are primarily concerned with the raw data's production readiness as well as components like formats, resilience, scaling, data storage, and security. Data scientists focus on extracting discoveries from the information that data engineers have prepared for them. They conduct online tests as part of their employment and produce business forecasts. Languages like Java, Python, SQL, and Scala are among the ones that data engineers are typically adept in. They typically have backgrounds in software engineering. In contrast, they might hold a degree in statistics or math. A thorough understanding of various methods in big data platforms, data mining, algorithms for machine learning, and statistics is crucial for data scientists. With some further training, data engineers and scientists can both become one another. As both professions share many abilities, such as knowledge of programming languages and experience with data pipelines, it should be quite simple for a member of either field to switch over to the other.
With this, let's see our next topic and get to know everything about the data engineer job description and data engineer roles and responsibilities.
Data Engineer Roles And Responsibilities
To know exactly what a data engineer does, let's see data engineer roles and responsibilities in detail. Job descriptions for data engineers vary greatly between businesses. The range of these positions is largely determined by the size of the business, the expertise of its servers, and the amount of data gathered.
On a small team, a data engineer could be in charge of all aspects of data flow, from managing analytical tools to configuring data sources. In other words, they would perform the duties of a full-stack data scientist, architecting, building, and managing databases, data warehouses, and data pipelines.
In a mid-sized enterprise, data engineers and data scientists collaborate to create the specialized tools they require to achieve particular big data analytics objectives. They are in charge of the software used to link data sources and data warehouses. These pipelines either perform general operations or merely move data from one location to another.
A typical job description for a data engineer calls for them to concentrate on setting up and building analytics databases, optimizing them for quick analysis, and developing table schemas in a huge organization with very complicated data demands. This requires ETL labor, which is the process by which data is extracted from a source, transformed into a format for analysis, and then stored in a data warehouse.
These were the roles of data engineers based on the size of the company they worked in. Let's see what the responsibilities of a data engineer are. These professionals use their programming abilities to create, alter, and maintain integration tools, networks, stores, and analytical systems since a data engineer is first and foremost a developer. The requirement of a data engineer profile is the conversion of information into an analytically relevant format. Data engineers would evaluate the dependability and efficiency of each component of a system during the development stage. Alternatively, they could assist the testing crew. Both structured and unstructured data can be kept in a warehouse. A data engineer is responsible for using database management systems to maintain and properly structure the stored data.
As long as the storage facility needs to be cleaned sometimes, keeping an eye on the system's general performance and stability is crucial. Since data, models, and needs might change, it is also important to monitor and modify a pipeline's automated components.
As you probably concluded from reading all the above-mentioned resources, being a data engineer is no simple task. To become one, you must have a firm work ethic, and a thorough awareness of the necessary equipment, and skills. Because of the recent data boom, this position is in high demand in the business and will continue to be a lucrative career path for those who are ready to pursue it.