When you are curious to know about data collection, data management, data processing, and many other fields regarding data then you will definitely have an interest in the Big Data Engineer profession and can easily correlate each and every aspect thoroughly. In this blog, we will learn everything about big data engineer and its importance as a career option. But before that, you must be familiar with the term Big Data.
What is Big Data?
The term Big Data refers to the very huge volume of data that may be structured, unstructured or semi-structured form that is then processed to get a systematical extraction of information from numerous sources which is utilized in a variety of fields such as medical, education, hospitality, artificial intelligence for the development of your business, etc. Nowadays every task is in some or other ways related to the big data world. The data is continuously being added to the existing volume of big data which is difficult to analyze simultaneously with traditional ways. Today people are seeking big data online courses to learn more about this subject.
Who is a Data Engineer?
Now that we have learned about big data, let’s dive into the term big data engineer. Data Engineering is the branch that deals with data science practical analytics and collection of data. It is used to make compatibility with the real world. Rather than dealing with new systems, it focuses on the betterment of developed schematics and networks for the best flow and access of information.
The two fields Data Scientists and Data Engineer are correlated to each other. They both have their own significance but can’t go with the flow without each other. A Data Engineer is a person who develops and maintains data structures and architectures. He is responsible for the conversion of information which is in raw form and making it into a usable form of data. A data engineer must know languages such as Java, Scala, SQL, AWS, etc. He should also possess data structuring skills and Big Data (Hadoop and Kafka). For various IT companies, the role of a data engineer is very important.
Data engineering is the field that deals with the collection, management, and processing of data. Data scientists are people who are experts in statistics and mathematics whereas data engineers have sound knowledge of computer science and programming languages. There is no compulsion to get a computer science degree; some other fields are also involved in this profession. Some of the general skills you must possess such as :
- Data structures
- SQL, JAVA, PYTHON, SCALA
- Big data tools (Hadoop, Spark, Kafka)
- Algorithms and Data pipelines
- Distribution systems
Data structures can be defined as the manner of organizing the collected data in an efficient order to provide easy accessibility. The other name for data structures is databases, they are of various types such as Queue, Matrix, Array, Graphs, Binary tree, Heap, etc. When you get a sound knowledge of all these structures of data, then you can carry on to the abstract data structures.
Programming Languages (SQL, Python, Java, and Scala)
A data engineer must have a skillset consisting of various programming languages such as Java, SQL, Python, Scala, etc. SQL stands for Structured Query Language which has been used since the 70s in the market by many analysts, developers, and engineers. SQL helps you to store and edit data by the servers present on your databases. Python has its popularity because of its versatility and is easy and compatible to work with.
On the other hand, Java and Scala are equally important because various tools such as Hadoop, Apache Spark, Apache Kafka, and HBase for the databases are written in these two languages. To work with these tools, you must be very familiar with the above-mentioned languages. Each of them has its own significance.
Big Data Tools
There are some most popular tools available to databases namely Apache Spark, Hadoop, and Apache Kafka. Having knowledge of these tools makes you eligible for one more step to work effortlessly when you are dealing with Big Data technologies. This helps you understand the core concepts of managing and storing data very easily. For example, many professionals use Hadoop for solving problems which are vast data-based open source software solutions. Spark helps you with programming clusters. All such tools are very important for a data engineer to work efficiently and with less effort.
Algorithm and Data Pipelines
Data pipelines are the structure used to provide a good way or path to the flow of information without having manual disturbance while data is transformed from one point to another. Data pipelines are used to transfer bulk and chunks of data each time data may be carried from a database warehouse or any other source. Algorithms are the specific mannerisms to perform any actions. They are generally separate from programming languages. With respect to databases, you can use algorithms for performing insertion, sorting, and deleting an item. A data engineer must have knowledge about all algorithms applicable in analytical tasks.
Since information present over digital mediums is in clusters of large systems, hence it is a bit difficult to manage data correspondingly to the organization’s requirement so it is very important to have a person who manages all these clusters of data when there are huge simultaneous members present. There is a higher chance of developing problems as compared to smaller ones. For dealing as a data engineer, you should know every aspect of data clusters and their systems to give your hundred percent in the workplace. You must be clear to learn how problems are faced in a huge cluster and the methodology to solve them efficiently.
The above discussed are the basic fundamentals of the data engineering field but one must have a good PG course and learn advanced data engineering from the online courses available at a very reasonable price. The demand for data engineers is very high. So if you are willing to learn about this field you can start right now, the above are key skills toward a career in data engineering. You should possess a certification for Big Data Scenario that is easily available online through various popular sources.