Data Engineering – Concepts and Importance
This article was published as a part of the Data Science Blogathon
First of all, we are surrounded by data in day-to-day life. It shows us that software engineering wants an additional category to have data engineering, which is useful in many real-time platforms like data storage, transportation, etc.
In this article, we will learn the concepts like
- The role of Data Engineering
- Responsibilities of Data Engineers
- Data Engineering skills
- Other fields related to Data Engineering
The Role Of Data Engineering:
Data Engineering is the field associated with analysis and tasks to get and store the data from other sources. Then, process those data and convert them into clean data used in further processes such as Data Visualisations, Business Analytics, Data Science solutions, etc.
Data Engineering converts Data Science more productive. If there is no such field, we have to spend more time preparing data analysis to solve complex business problems. So, Data Engineering requires a complete understanding of technologies, tools, faster execution of complex datasets with reliability.
The goal of Data Engineering is to provide organized, standard data flow to enable data-driven models such as ML models, data analysis. The above-mentioned data flow can get through several organizations and teams. To achieve the data flow, we use the method called data pipeline. It is the system that has independent programs that make several operations on stored data.
Data Engineering is responsible for the design, maintenance, extension, and construction support of data pipelines. Many data engineering teams are building data platforms. So many organizations cannot manage with just one pipeline to save data in an SQL database. Hence, they have many teams with several kinds of techniques to access data.
Responsibilities of Data Engineers:
Data Engineer is a technical person who is responsible for architecting, building, testing, and maintaining the data system. They are responsible to find recent trends in datasets and create efficient algorithms to make data more useful. They need required skills like programming, mathematics, and computer science, experience, and also soft skills to communicate the trends of data which help the business growth.
Some of the key responsibilities are:
- Get the required datasets for the problem statement
- Develop, construct, and maintain architectures
- Align the architecture with business requirements
- Develop the dataset process
- Usage of programming languages and tools to execute dataset
- Find the method to improve data reliability and efficiency
- Use large datasets to solve company issues
- Import machine learning and statistical methods
- Made the machine learning models such as predictive and prescriptive
- Use the required data to prepare tasks that will be automated
- Deliver the results to stakeholders based on the analysis which have been made
The different types of approaches made by data engineers are:
We have to get input data in the form of XML data, batches of videos updated every hour, weekly batches of labeled images, and so on. Data Engineers consume data, design a model that can take those data from several sources, convert and store them.
Data Normalization and Modeling:
Data Normalization involves tasks that make those data more convenient to customers. It includes processes like clean the data, removing duplicates, and conforming data to a specific data model. Data Engineers store the normalized data in a relational database or data warehouse. Data normalization and modeling are part of the transform step of ETL(extract, transform, load) pipelines. Another way of transforming the method is data cleaning.
Data cleaning is the process of fixing or removing the incorrect, corrupted, incorrectly formated, duplicate, or incomplete data within the dataset. If we combine many datasets, there are many problems like duplicating, mislabel, incorrect outcomes, unreliable outputs.
In this method, we remove the duplicates or irrelevant observations, fix the structural errors, filter the unwanted outliers, handle the missing data, and finally give us the effective dataset without any null values.
It is one of the important responsibilities of the customer side data engineering team. Data Accessibility means the user’s ability to access or retrieve the data stored within a database or other repository.
Data Engineering Skills:
Data Engineering skills are mostly as same as skills needed for software engineering. In this section, we will see important skills like:
1. Programming languages
3. Cloud Engineering
Data Engineers should have a basic understanding of design concepts like data structures and algorithms, and object-oriented programming. The most popular programming language which was used for data engineering is Python. It is also widely used by machine learning and Artificial Intelligence teams. Scala is also a popular language like Python which is a functional language that runs on the Java Virtual Machine(JVM).
If we have more data for usage, we need some databases which can store those data in a warehouse. Mostly used database technologies such as SQL and NoSQL. SQL databases are coming under the category of relational database management systems(RDBMS). NoSQL databases are databases that can store non-relational data such as document stores in MongoDB, graph databases stores in Neo4j, and so on.
In this technique, we use a method to have independent segments of a pipeline that is running on separate servers made by a message like Apache Kafka. These systems need many servers, and overall distributed teams need access to the data often. There are so many private cloud providers like AWS(Amazon Web Services), Microsoft Azure, and Google Cloud which are the most popular tools to build and develop distributed systems.
Other Fields Related To Data Engineering:
There are some of the fields that are closely related to data engineering as follows:
1) Data Science:
Data science is the subset field of data engineering that is data scientists derive insights from various datasets whereas data engineers create reusable programs using software engineering techniques. Data scientists use statistics, machine learning algorithms, Python or R language to explore efficient data which will be reusable, extensive.
2) Machine Learning Engineering:
Machine learning engineering is the field of using software engineering techniques and analytical data science knowledge and create a new efficient machine learning model which is useful for product users or consumers. For example, an ML engineer can develop a new recommendation algorithm for a company’s product, whereas a data engineer provides the data used to train and test the algorithm made by the ML engineer.
3) Business Intelligence:
Business intelligence is the process by which enterprises use strategies and technologies to analyze the data with the aim of improving decision-making and provide a competitive advantage. Data science is focused on making forecasting and future predictions, whereas business intelligence is focused on providing a view of the current state of the business. These teams based on data engineers to build some tools that made them analyze and inform relevant data.
Data Engineer Salary:
This professional career pays the biggest advantage for us. The average salary for data engineering roles between $65,000 and $135,000 and also depends on your educational qualifications, professional certifications, experience(in years) in the relevant field, additional skills, etc.
The annual salary for some of the top positions, according to the Bureau of Labor Statistics in 2019, such that:
1. Database Administrator – $93,750
2. Computer Network Architects – $112,690
3. Computer Research Scientists – $112,840
According to Glassdoor, the estimated base salary for Data Engineers in 2020 was $102,864 annually.
According to the reports from Indeed.com, Data Engineers can earn up to $129,415 annually with an additional possible bonus of $5,000.
As of April 2021, the average Data Engineer salary in the US falls anywhere between $90,000 and $126,133.
Now, you can get an idea about some concepts and the importance of data engineering in real-world scenarios. This field is most suitable for those people who have an interest or educational background in computer science and technology fields. I hope that you are all excited about the blog. Does data engineering fascinate you? Let us know your thoughts in the comments!
Thanks for reading my article!
About the author:
Vikram Rajkumar – I am currently pursuing my Bachelor of Engineering (B.E.) in Electronics and Communication Engineering from Sri Krishna College of Engineering and Technology, Coimbatore. I have done projects and internships in the domain of data science and business analytics and also interested in data analysis, data visualizations.
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