The blog includes all the crucial aspects of how to pave your career to become a successful Big Data Engineer. Below are the topics that will be covered in this blog today:
Big Data Engineer, as a career, enjoys great demand. It is undoubtedly a promising career option for all Big Data enthusiasts and aspirants. But before going ahead, it is essential to understand what Data Engineering entails.
Learn how to become a Big Data Engineer from Intellipaat:
What is Big Data Engineering?
One has to think about engineering as associated with designing and building things. The key concept of the domain lies in that. However, in this case, it involves the designing and building of pipelines for the transformation and transportation of data into a usable state to be used by Data Scientists and other end-users.
The pipelines aid in gathering data into a single warehouse from disparate sources. Data Engineering does not involve experimental design but focuses more on the development of systems for easier flow and information access.
Who is a Big Data Engineer?
Data generation is of no use unless processed and analyzed with competence. Professionals in the field of Big Data are responsible for undertaking this arduous task. Big Data Engineers develop, test, and evaluate the Big Data infrastructure of a company for making the data fit for analyses, which in turn brings in growth for the company.
What is the difference between Data Engineer and Data Scientist?
Both Data Scientist and Big Data Engineer are critical roles in an advanced analytics team. For Data Science to be meaningful and effective, the support of Big Data Engineers should not be neglected. Although the knowledge of tools and the priority skills are different, frequent collaborations are often seen between Data Scientists and Data Engineers.
Data Scientists deal with the advanced analytics of data generated and stored in databases. On the other hand, Data Engineers are responsible for the design, optimization, and management of the data flow among those databases. Evidently, Data Scientists will need to be highly skilled in statistics, math, R programming, Machine Learning algorithms, and techniques. Likewise, Data Engineers will need to be well-versed in SQL, NoSQL, MySQL, cloud technologies and architecture, and frameworks such as Agile and Scrum.
What are the job responsibilities of a big data engineer?
Let’s discuss the responsibilities of a Big Data Engineer in detail:
- Design and implementation of software systems, along with their verification and maintenance
- Development of robust systems for the purpose of data ingestion and data processing
- ETL (Extract, Transform, and Load) process and operations
- Data quality improvement through research on various new methods
- Building data architectures to meet business requirements
- Generation of structured solutions through the integration of programming languages and tools
- Data mining from disparate sources for the development of efficient business models
- Collaboration with Data Analysts, Data Scientists, and other teams.
Above are only a few of the key responsibilities of a Big Data Engineer. Next, we will take a look at the skill sets that are crucial in carrying out these responsibilities.
Steps to Become a Big Data Engineer?
One does not necessarily require a background in computer science to enter this domain. People from diverse backgrounds can be seen in this field but with a set of skills. Here are some of the skills that can get you into the field of Big Data Engineering.
Algorithms
They are one of the fundamental concepts of Big Data Engineering. Algorithms are, basically, instructions that enable a sequence of actions to be performed in a certain order. They can be used regardless of the programming language used. Algorithms are used to find, insert, sort, or delete items in a database.
Data Structures
Data handling requires an efficient order for easier access. Data structures (or databases) aid in better management of data by organizing it well. Some different data structures are array, binary tree, matrix, graph, etc. One can later move from basic data structures to abstract data structures.
SQL
SQL (Structured Query Language) is one of the most popular programming languages in the world of Big Data and has been in the market for a long time. It is primarily used for the generation of queries from a client program to the database. Simply put, it allows for the editing and storage of data on database servers.
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Programming Languages
Python is widely used for its versatility, and it is easy to work with. It is a must-have skill for every data enthusiast. There is a Python library for every task that needs to be performed. Along with Python, Scala and Java are equally important skill sets that are crucial to Big Data Engineers as tools such as Hadoop, Apache Spark, Apache Kafka, HBase, and others mostly use these languages. Learning these programming languages will enable one to use these Big Data tools without difficulty.
Apache Hadoop, Spark, and Kafka are some of the popular Big Data tools. They are vital in making data management and storage easier and more straightforward. For instance, Hadoop is used to come up with solutions to problems related to huge amounts of data. Spark provides an interface for programming clusters. Big Data Engineers will need to get familiar with more tools as they progress further into the field.
Distributed Systems
This includes Software Architect and Software Engineer skills. Data is stored in clusters that operate independently. Big Data Engineers need to have a good knowledge of data clusters and their systems, including the number of problems faced by these data clusters and how to come up with the right solutions.
Data Pipelines
Data pipelines are software solutions that build pathways for the flow of data. They help to do away with several manual steps from the process of data transfer. Aside from data warehouses, data pipelines can be implemented to transfer data to applications too. Data Engineers spend a considerable amount of time in the building and management of data pipelines.
Data Modeling
Data modeling skills are very essential for Data Engineers as they are required to understand where to normalize and denormalize data in the warehouse, how to structure tables and partitions, how to retrieve certain attributes, etc.
Above are the skills required for a Big Data Engineer to have a strong command of the domain. Apart from these, they should also be proficient in analytics, data mining, ETL, cloud platforms, automation, etc.
How to acquire Data Engineer skills?
There are many courses available nowadays to help aspirants to attain data engineering skills. Apart from courses, you can even get a head start by looking up online tutorials, e-books, and other self-help resources that are equally good.
It is very convenient to take up certification courses online from reliable institutes like Intellipaat that focus on providing learners with hands-on experience in the domain. This not only helps the learners get acquainted with the practical aspects of the domain, but these skills also prove to be very useful when it comes to working on real projects for companies.
Scope of Big Data Engineers in 2023
The demand for Big Data professionals has become higher and higher over the years as more data is generated every day. As observed by Forbes, Data Engineer is among the top emerging jobs on LinkedIn. Data Engineers who are willing to update themselves can earn high salaries, mainly because their job is incredibly complex that demands new skills and the knowledge of the latest technology.
Big Data Engineer Jobs
Big Data Engineers are in high demand as much as other data-related jobs in the market. Let’s see some statistics.
- There are over 64,000 Data Engineer jobs in India listed on Glassdoor.
- Over 16,800 Big Data Engineer jobs are listed in the United States, according to Indeed.
From the above numbers, you can gauge the demand for Big Data Engineers and start preparing for your entry to the field for a lucrative career.
Big Data Engineer Salary
As per Glassdoor, below are the average salaries paid to Data Engineers:
Conclusion
Having mentioned all of the above, it is important to remember that Data Engineering is an evolving discipline, and with such variety, it is no surprise that some companies struggle to understand Data Engineering and how to hire the right professionals. It is a vast field and one of the careers with a better scope. So, if data is your interest, you can consider becoming a Big Data Engineer.