~Author Mr. Girish Singhania, CEO, EduBridge
Data Engineer in a Nutshell
Approximately 7.5 septillion terabytes of data are produced per day globally. Data Engineers processes the data, making it useful for organisations and enterprises. The definition entails, a specialist in charge of creating the systems that gather, manage, and transform unprocessed data into information that can be used, to make future forecasts on ways of improving businesses, and processes A Data Engineer’s main duty is to make data accessible so that organizations may assess and improve their performance.
What does a data engineer do?
Collect Data
At first, the data is gathered from reputable sources, post which data sets are created before the optimised data is stored.
Data Structure
A data engineer applies a methodical approach when planning, building and maintaining data architectures that align to the business requirements.
Conduct Analysis
Research on market trends and patterns is a key component of a data engineer’s job. To resolve business challenges, he or she must address key issues.
Model Building and Pattern Recognition
Data aggregation is useful to extract information, a Data Engineer is necessary to apply descriptive data aggregation to extract historical insights. He/she applies forecasting techniques to learn about the future by utilizing predictive models.
What qualifications and abilities
Coding Skills: Working as a Data Engineer necessitates knowledge of coding languages. He /she must be familiar with languages used in computer programming, including Java, Scala, NoSQL, SQL, Python and R.
Big Data Tools: A Data Engineer works with more than just conventional data. Big data management is something he or she must do. Data Engineers need to be familiar with the various platforms and tools that businesses use for big data management, including Hadoop, Kafka, and MongoDB.
Cloud Computing: Knowledge of cloud computing and cloud storage is necessary for the position of a Data Engineer. Many businesses are already exchanging their physical servers for cloud services.
ETL: Extract, Transform, and Load is referred to as ETL. Data can be moved from databases and other sources into a single repository using the ETL method. Xplenty, Alooma, Stitch, and Talend are a few popular ETL tools.
Automation and Scripting: Since it makes it possible to gather so much data, automation is essential for big data. Scriptwriting is a must for Data Engineers who want to automate repetitive processes.
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Salary Packages offered to Data Engineers in India
In India, the median annual income for a data engineer is 17 lakh rupees. The usual median income for Data Engineering specialists working in the internet and computer sector is however between 28 and 29 lakhs per year. Annual compensation of around Rs 19 lakh is the norm for Data Engineers working in the Bengaluru and Delhi NCR region. According to analyticsindiamags.com’s research, the income of a Data Engineer increases by an average of 15–17% annually. This cites immense scope for growth in the profession.
Overall, Data Engineering is a critical function in dealing with the ever-growing mass of big data. As technology improves and more organizations increasingly demand timely insights from their huge data reservoirs, the career prospects for Data Engineers will continue to expand worldwide. If you have the necessary skill sets and experience, now is an excellent time to join this exciting and growing field.
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