Every organization deals with a large amount of data. However, to use the data effectively, it has to go through the process of data engineering. It plays a major role in the process of collecting and maintaining the data. Data engineering helps professionals like data analysts and data scientists break down information easily and perform further analysis. Therefore, it has become the most in-demand skill in the IT discipline in recent times.
Let’s discuss the following topics:
Who Is a Data Engineer?
A Data Engineer can be critically termed as the first member of the Data Science team. He/she works with huge amounts of data to maintain the analytics infrastructure, making it suitable for Data Scientists to work on.
Enterprise data is stored in various formats: databases, text files, or any other storage sources. Data Engineers are the professionals who build pipelines to transform this data into formats that are readable and usable for Data Scientists. They convert the data in such a way that it is suitable for analysis. This pipeline involves taking data from discrete sources and storing it in a single warehouse, where the data will be represented uniformly.
Data Engineering Definition
Data Engineering is a term that is used for collecting and validating quality data for analyzing it. It is a vast field that employs different modules and steps, such as data infrastructure, data mining, data crunching, data acquisition, data modeling, and data management.
Companies need someone who can organize and ensure data availability and its quality to make it secure enough for them to work on. This is where Data Engineers come in. They lay the foundation for the successful initiatives of Data Science practices.
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Why Is Data Engineering Important
Data engineering is the first step in the process of data analysis and model building. Without well-organized data, analysis cannot be performed. Also, with the advent of Artificial Intelligence, data has become more important than ever. With that being said, collecting and maintaining data have become crucial for any organization. Therefore, data engineering plays a major role in the following tasks:
- Collecting data: Collecting and sourcing data from various sources. Various techniques can be used for collecting data such as ethical web scraping, API calling, etc.
- Maintaining databases: Databases can be different based on the company you are working for. Examples of databases are MySQL, PostGreSQL, Oracle database, etc.
- Preparing data for further analysis: Basic steps of data cleaning and data treatment are performed by a data engineer so that further analysis can be done by data analysts and data scientists.
What Do Data Engineers Do?
Data Engineers maintain the data infrastructure to support business applications. As part of their responsibilities, they fuel Artificial Intelligence analytics and the Machine Learning process.
Here are some of the most common tasks that are performed by data engineers:
- Data engineers ingest, design, and manage the sources of data essential for business insights to build a Data Engineering architecture. With in-depth knowledge of SQL and XML, they can integrate and organize certain parts of the data management system.
- Data engineers are the ones who need to be proficient in programming languages such as Python and Julia. They design, integrate, and prepare the data infrastructure, adhering to all data management norms.
- They also design and maintain database systems to ensure that users can access all functions seamlessly. They also optimize the speed of databases and work against workflow interference.
Why Does Data Need Processing Through Data Engineering?
Data engineers now have more data to manage and provide to downstream data consumers for analytics due to the growth of data lakes. Data engineers must work with unstructured and unformatted data found in data lakes before the business can use it to its advantage.
Fortunately, a data set may be read and understood more quickly and easily when it has been thoroughly cleaned and formatted using data engineering. Businesses are continuously producing data; therefore, it’s critical to identify software that can automate some of these procedures.
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Roles of a Data Engineer
A Data Engineering career has a long yet worthy path to success. It develops through various roles, as explained below:
| Roles of Data Engineer | Description |
| A Generalist Data Engineer | A Generalist Data Engineer is someone who works with a small team. He/she is typically a data-focused person and works on ingesting data to process it for further analysis. |
| Pipeline-centric Data Engineers | Pipeline-centric Data Engineers work for mid-sized companies, where they have to deal with a little more complex data needs. They have to work according to the Data Engineering methods in collaboration with Data Scientists to transform the data. Knowledge of computer science and distributed systems is essential for these professionals to execute such analyses. |
| A Database-centric Data Engineer | A Database-centric Data Engineer is someone who sets up and populates analytics databases. He/she works with the pipeline, tuning for quick analysis and designing schemas. These Data Engineers usually work for larger organizations where the data is distributed across several databases. |
Data Science projects largely depend on the information infrastructure structured by Data Engineers. They typically implement their pipelines based on the ETL (extract, transform, and load) model. The Data Engineering basics revolve around the typical Data Engineering tools that find their usage in the daily life of a Data Engineer.
- Distributed Streaming Platforms: A streaming platform enables you to capture, process, and store data streams in real-time. It is a backbone for real-time data pipelines and streaming applications. Examples of distributed streaming platforms are Amazon Kinesis, IBM Streams, Apache Kafka, etc. Knowing these tools can greatly help a data engineer manage data infrastructure.
- Databases: Knowing databases is a must-have skill for a data engineer. Examples of databases are MySQL, PostgreSQL, etc.
- Programming Languages: Having basic to intermediate-level knowledge of programming languages can improve the efficiency of a data engineer. Learning programming languages like Python, R, and C can come in handy.
- Cloud Storage: Knowing cloud services like AWS and Azure can add to the skillset of a data engineer.
- Big Data Framework: Learning big data technologies can vastly assist a data engineer in dealing with very large datasets. Frameworks like Google BigQuery, Presto, and Apache Hadoop help in storing and processing large amounts of data.
The following table will help you summarize:
| Technology | Tools |
| Distributed Streaming Platforms | Amazon Kinesis, IBM Streams, Apache Kafka |
| Databases | MySQL, PostgreSQL, Oracle |
| Programming Languages | Python, R,, C |
| Cloud Storage | AWS, Azure, google cloud |
| Big Data Framework | Google BigQuery, Presto, Apache Hadoop |
Data Engineer vs. Data Scientist
| Data Engineer |
Data Scientist |
| Data engineers are more concerned with developing data infrastructure. |
Data scientists are concerned with analyzing data. |
| They collect data from various sources and maintain large data files. |
They apply Machine Learning algorithms and perform predictive analysis on the collected data. |
| Data Engineers use technologies like Bigdata frameworks, Databases, cloud technologies, etc. |
Data scientists use technologies like Notebook IDEs, Machine Learning, Deep learning, etc. |
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Salary Trends in Data Engineering for 2025
| Job Role | Average Salary in India | Average Salary in the USA |
| Data Engineer | Minimum –3.5 LPA | Minimum – 81,368 USD |
| Average – 10.8 LPA | Average – 127,435 USD |
| Highest – 21.0 LPA | Highest – 199,583 USD |
Future Trends in Data Engineering
In the case of Data Engineering, AI can take care of repetitive tasks by reducing the number of time-consuming processes in the field. AI models can be used to automate the process of data collection. AI models trained on large datasets can also be used to find anomalies in the data, easing out the process of anomaly detection and data cleaning. AI can be looked at as a dependable tool in the field of data engineering.
In the coming years of Data Engineering, the following are the fields where there is a scope for AI advancement:
- Automated Data Pipelines: AI can be used to automate the creation and maintenance of data pipelines, which are essential for moving data from various sources to a data lake.
- Intelligent Data Governance: AI models can analyze data sources and use patterns to automatically ensure data quality and data security.
- Predictive Maintenance: AI can be used to monitor and optimize data infrastructure and processes. By analyzing historical data and real-time performance metrics, AI models can predict potential failures.
Conclusion
Data Engineering is all about dealing with the efficiency of data management. Therefore, Data Engineers must frequently update their skill sets to ease the process of leveraging the Data systems. Because of their wide knowledge, Data Engineers can be seen working in collaboration with Database Administrators, Data Scientists, and Data Architects.
Without a doubt, the demand for skilled Data Engineers is growing rapidly. If you are a person who finds excitement in building and tweaking large-scale data systems, then Data Engineering is the best career path for you.
FAQs
What do we mean by data engineering?
In simple words, data engineering can be defined as a department that deals with data collection, data storage, and developing data infrastructure.
Where is data engineering used?
Data engineering is the first step in the field of data science. Data engineering is used to maintain the data, which is later used for analysis.
How much programming is required in data engineering?
Basic-level programming and coding are required for data engineering. Basic knowledge of Python can come in handy.
How will AI affect data engineering jobs?
AI can be looked at as a tool that significantly improves productivity. Think of it this way: as engineers become more productive, their needs spread across industries. The demand for data engineers will exponentially grow in the coming years.
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