Big Data Engineer: With large amounts of data streaming into companies of all sizes and across all industry sectors, it is not surprising that data science is in high demand. This is seen as a useful source of knowledge that might help companies make strategic decisions and stay ahead of the competition. Because of this, data engineers with the necessary skill sets are in great demand and attract high pay.
What Is Data Engineering?
Data science is the more well-known field, while data engineering is at its foundation. The latter focuses on data collection and analysis, i.e., the practical aspects of data science. Using science and technology, it seeks to construct and manage data pipelines inside an organization. The objective is to address issues in managing and processing data for a data science project. It acts as the backbone, the cornerstone of a data science project, and proficiency in this topic is crucial for anyone working in data science.
What Does A Data Engineer Perform?
A data engineer, sometimes known as a big data engineer, is responsible for assuring the integrity and accessibility of an organization’s data pipelines. To do this, they must provide the necessary architecture and infrastructure to create data. For this purpose, a data engineer employs:
- Querying languages: such as SQL
- Scripting languages: such as Python
- ETL (Extract-Transform-Load) tools
The system’s size changes based on the client’s requirements. A small-scale relational database management system might suffice if a tiny neighborhood shop needs data engineering (RDBMS). A Fortune 500 corporation, however, would find this inadequate and would likely need a data lake for comparable goals.
What Is The Level Of Demand For Data Engineers?
According to a 2019 analysis by Dice, the demand for big data engineers increased by 88 percent from 2018 to 2019. In light of the dearth of qualified specialists, yearly earnings are likewise fairly high, averaging INR 785,438. (USD 10,400).
What Are The Stages Required To Become A Data Engineer?
There is no one route to a data engineering profession. However, every route must have the following elements:
1. A Bachelor’s Degree
Due to the specialized nature of the candidate, applicants must possess a bachelor’s degree in computer science, mathematics, information technology, or a related discipline. If a candidate lacks these skills, it is recommended that they enroll in online courses on algorithms, database administration, and fundamental programming. A data science certification is a great approach to acquiring the necessary skills and knowledge.
2. Core Data Engineering Skills
A big data engineer should be proficient with database tools and querying languages (SQL), distributed systems (Hadoop, Kafka, Spark), and programming languages (Python, R) for statistics and modeling. In addition, they must know about operating systems and machine learning. Database administration, developing and constructing data warehouses, distributed systems, big data tools, and at least one operating system are further crucial topics.
3. Work Experience
Projects are a great method for a novice to get practical experience. It is beneficial to search for projects that provide the desired skills and carefully monitor whether the project is providing those skills. Some data engineer schools do address these skills in modules and assignments. The candidate should develop a GitHub project portfolio. Keep in mind that a big data engineer is the one that delivers high-quality data for the data scientist position. A data engineer, who combines the skills of a data analyst and a data scientist, is a crucial component of a successful data science project.