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Data Engineering refers to a software engineering position that places a strong emphasis on data, namely on data processes, data pipelines, and the ETL (Extract, Transform, Load) procedure. Data engineers are being sought after by businesses of all sizes. From start-ups to those in the Fortune 500, ahead of data scientists, cybersecurity analysts, and web developers. In this blog, we will dive deeper into this topic and look at how to become a data engineer.
Why is Data Science Necessary?
To understand how to become a data engineer, one needs to understand the importance of data science. Looking back in time, we can see that older data was mostly structured and had a limited scope. To analyze such datasets, Business Intelligence (BI) would be sufficient in a straightforward manner. However, as time went on, data grew more complex, more ad hoc, or, most often, semi-structured. This mostly occurred because of how much data has been acquired recently and from different sources, such as text files, financial papers, multimedia data, sensors, etc. Furthermore, business intelligence tools are unable to process this wide range of data on their own. We also need advanced algorithms and analytical tools to glean meaning from the data. As a matter of fact, this is a key factor in the rise in the popularity of data science.
Moreover, data science is vital because it enables people to make better decisions by performing predictive analysis and identifying important patterns in data sets.
What is Data Science?
Data science is a discipline that enables one to draw beliefs and insights. From structured, semi-structured, and unstructured datasets using a variety of scientific techniques and algorithms that aid in making predictions and outlining data-driven solutions. And so, it is the result of the coordinated use of many statistical tools.
Data science is the coordination of several statistical methods to produce inferences and insights that are useful for making better decisions. Data for the study of data science is gathered from a wide variety of sources. It originates from a variety of places, including surveys, social networking platforms, e-commerce websites, browser searches, etc. Moreover, thanks to the most recent and cutting-edge technology employed in data collection, we have access to these data. Enterprises as a whole gain from data collecting and analysis since it enables organisations to foresee and provide insights about products so that they may make informed decisions supported by inferences from current data. In turn, helps such enterprises generate enormous financial returns.
In this blog about how to become a data engineer, let’s now look at what is the role of a data engineer.
What is the Role of a Data Engineer?
Data engineers are tasked with identifying patterns in data sets, creating algorithms, and setting up data pipelines to transform raw data into useful information for the organisation. Starting with a solid foundation in SQL and programming languages like Python, Java, etc. Moreover, this profession calls for a variety of talents. In addition, they must be effective communicators to collaborate with other divisions and advance the enterprise’s objectives. Professionals with expertise in data engineering build the groundwork for databases and architecture. They build a solid architecture using database tools and then implement the procedure to build the database from scratch. Moreover, as a data engineer, you must know how to optimise data retrieval, make dashboards, reports, etc.
What are the Main Skills Required to Become a Data Engineer?
Every tech enthusiast wants to master the best skills necessary to become a data engineer because data science has taken over the corporate world. As a matter of fact, with a job growth rate of over 650% since 2012 and a median income range of about $125,000, it is one of the fastest-growing employment categories. Data science is the practice of combining the right tools to complete a task. It aids in your ability to draw knowledge from facts to respond to your inquiry. Additionally, in plain English, it is a potent instrument that companies and stakeholders use to decide better and resolve real-world issues.
In this blog about how to become a data engineer, apart from education and fundamental skills, let’s look at the key skills that are required to become a data engineer.
Python Programming
Python is a powerful programming language that may be used to complete all of a data engineer’s jobs. Moreover, It allows for the easy import of SQL tables into your programmes and the collection of a wide variety of data formats.
Amazon Web Services
Programmers frequently employ the renowned cloud platform Amazon Web Services to increase their agility and scalability. Additionally, The AWS platform is used by data engineers to plan the data flow. You also need to understand how cloud-based data architecture is designed and deployed.
Kafka
Kafka is an open-source platform for processing. People use it to create real-time streaming applications and manage real-time data sources. Therefore, a data engineer can use the Kafka applications to find and use trends as well as respond to user needs.
Hadoop Platform
In order to distribute the processing of large amounts of data among different computing hardware, people use it. The Hadoop platform has its own Hadoop Distributed File System (HDFS) to store and deliver vast amounts of data to user programmes like MapReduce. Even though it may not be necessary, having some user experience with programs like Hive, Pig or Hoop is a plus on your resume. You should familiarise yourself with cloud computing solutions like Amazon S3.
SQL Database
SQL, or Structured Query Language, is a computer language that enables users to store data in relational database management systems, query it, and alter it. In addition to performing analytical tasks and modifying database structures, you can add, delete, and extract data from databases.
Apache Spark
One of the most well-known big data technologies worldwide is the processing engine Apache Spark. Additionally, it can work with big, unstructured datasets and interface with Hadoop with ease. However, the sole distinction between them is that Spark is significantly faster than Hadoop. Hadoop must read from and write to disk, which slows down the process, whereas Spark stores its computations in memory.
Machine Learning
Although, not all data science positions require expertise in deep learning, data engineering, or natural language processing. However, knowledge of machine learning methods will help you stand out in a sea of data engineers. These include ensemble learning, supervised machine learning, decision trees, logistic regression, k-nearest neighbours, and random forests.
Intellectual Curiosity
“Curiosity” is the word used to refer to an individual’s desire to explore and learn about various subjects and ideas, rather than just one particular topic. Someone who enjoys learning is intellectually curious. Therefore, you should have a lot of questions as a data engineer.
Communication Skills
A person with strong communication abilities can explain technical concepts to non-technical team members like those in the marketing or sales departments. Hence, Data engineers must comprehend the requirements of their non-technical coworkers.
We now know how to become a data engineer, let’s now look at a data engineer’s salary.
Data Engineer’s Salary
Burning Glass’ Nova Platform study ranked Data Engineer as the top position in the technical field, with a 88.3% increase in job posts. Despite the rising demand for data engineers, there remains a global scarcity of trained data engineers. Moreover, data engineers’ pay is based on several criteria, including the industry they work in, their years of experience, the size of the company, and others. However, one major benefit of becoming a data engineer is that they are constantly in demand on a global scale. Additionally, because of the freedom and flexibility that come with this profession, you always have the choice to move somewhere else if you grow tired of working in a certain city or nation.
Here is a list of the highest paying countries and also tells us the average data engineer’s salary.
- India ( Average salary is ₹830,000 )
- The USA ( Average salary is USD 116,591)
- Germany ( Average salary is €60,632 )
- United Kingdom ( Average salary is £43,725 )
- Canada ( Average salary is CAD 80,000 )
- Australia ( Average salary is AUD 103,346)
In this blog about how to become a data engineer, one should know that today, every business, large or small, views data science as a critical industry with the capacity to alter consumer behaviour.
About Henry Harvin’s Data Science Professional Course
If you want more information on how to become a data engineer, you should check out Henry Harvin’s Data Science course. It is a 32 hours two-way live online interactive classroom session with + 50 hrs E-Learning Access. Experienced Industry Mentors with 10+ years of experience will train you. You will also get a guaranteed internship with Henry Harvin or partner firms.
Additionally, with the help of this course, you can apply different Data Science and Machine Learning methods and skills. As well as practise utilising the many tools that data scientists use and get experience with them.
Learn to think and act like a data scientist by mastering the main processes required in solving a data science problem. This course will open immense career growth opportunities.
If you want to know more about Henry Harvin’s Data Science course, you can check out this Youtube Video below.
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