What Is Data Engineering And Why Is It
Important In 2024?
In the world that we live in today, information is power. Data is power. And only when you are able to make the best of it, you are able to get the best for your business. In this blog we will delve in the realm of that power, data; how it is mined and engineered for businesses to attain success and why it is important.
What is Data Engineering?
Data engineering, also known as information engineering is the approach of developing information systems. It comprises of:
- Sourcing
- Transforming and
- Managing data from various systems.
It makes varied forms of information useful and accessible. In short, data engineering is all about the practical application that follows collecting data and analyzing it.
Needless to say data comes in all shapes and forms and one has to order this chaos, gather it and authenticate it. This is done with the help of data integration tools and artificial intelligence. The practice of data engineering involves designing and building systems that are able to collect, store and analyze data on a large scale and it is instrumental in just about every industry.
Now that we have understood what data engineering is, let us come to the role of data engineers.
Who are Data Engineers?
The typical role of a data engineer is to build the information architecture or data ecosystem of the company that enables them to process big data by utilizing a range of different data management tools to create data pipelines. They also analyze data using artificial intelligence and machine learning algorithms. Furthermore by employing data visualization, the data engineers also generate reader-friendly business intelligence reports.
Based on their skills, responsibilities and roles, there are three types of data engineers:
- Generalist data engineers – the ones typically working on small teams and collecting end to end data.
- Pipeline centric data engineers – a data pipeline means a workflow of data that consolidates data from various disparate sources. A pipeline centric data engineer then works across the distributed system on a complex project.
- Database centric data engineers – are the ones who focus solely on analytics databases, i.e they work closely with data scientists across multiple data warehouses.
Engineers who handle overly complex or large sets of data are known as big data engineers.
What are Data Engineering Technologies?
To collect, parse, manage, analyze, and visualize large data sets in the company, data engineers use a number of data engineering technologies and tools like, Stitch, Tableau, Allstacks, IBM Engineering Lifecycle Management, Logilica Insights and Data band (also an IBM company).
There are certain criterias, on which data engineers decide employing a specific data engineering tool such as,
- User interface
- Integration and flexibility
- Usability
- Value for money
- Setup time
- Programming language compatibility
Importance of Data Engineering
As mentioned above, data engineering helps order the chaotic hurdle of information bombarded by the systems each day. Hence, it enables businesses to optimize data towards usability. It also plays a significant role in the following pursuits:
- Helps one to find the best practice that refine the software development life cycle.
- Enhances information security and protects the business from cyber attack.
- Increases the knowledge of the business domain.
How does Data Engineering benefit your business?
Data engineering is indisputably important in every business and for digital automation. From future analysis, to present day to day operations, data engineering is the key to the longer shelf life of your business entity. While you might be able to record the data incoming on a daily basis, it is of little to no use if it is not comprehensible, moreover it must also be coherent, a task best done by data engineers. To be able to use data, it must be readable and it is the very innate function of data engineering. In fact, accessible and actionable business intelligence has been proven to facilitate upto 5x faster decision making.
Though the importance of data in the business might not be a new concept, people seldom confuse its vitality with other far distinctive roles. Let us take a minute to understand those differentiations here.
Data Science vs Data Engineering
- Role of Data Scientist: A data scientist cleans and analyzes data, answers questions, and provides metrics to solve business problems.
- Role of Data Engineering: a data engineer develops, tests, and maintains data pipelines and architectures, which the data scientist uses for analysis.
Thus, The data engineer does the legwork to help the data scientist provide accurate metrics.
Software Engineering vs Data Engineering
Role of Software engineers: Software engineers are responsible for developing software’s for applications and systems that a user can interact with. They also set up networks, build operating systems and maintain It documentation of the company.
Role of Data Engineers: Unlike software engineers, data engineers are responsible for the development, integrity, and maintenance of an organization’s data infrastructure, i.e ensuring a solid foundation of data for use in business-critical analytics or reporting.
What is the Data Engineering Lifecycle?
What Is the Data Engineering Lifecycle? Data engineering is the process of turning raw data ingredients into a useful end product that can be used by analysts, data scientists, machine learning engineers, and others.
What are Data Engineering Services?
Your organization will advance to the next level of data utilization, data management, and data automation with the assistance of the company that provides data engineering services. Using automated advanced data pipelines, you can focus on extracting insights.
Some of the data engineering services provided include
- Generating complete end-to-end Data Pipelines
- Ingesting Data from various sources into preferred destinations
- Manipulating various file format conversions
- Executing Data Transformations
- Executing Data Cleansing
- Preserving Data Integrity
- Developing Data Models
- Executing ETL and/or ELT jobs
- Enriching Data for downstream Analytical Purposes
- Executing Data Analytics
- Performance Tuning
Bottom line
Data engineering makes it easier for one to solve complex business problems. We at Nuox Technologies, helps with digital automation and artificial intelligence services that extend faster execution of complex datasets with reliability. This helps every stakeholder to gain adept knowledge of the business’s momentum and make more sound decisions which ultimately lead to success. Our team of expert data engineers and developers help small business owners to acclimate with the change and seek more from their daily data management. Because we believe that it is only a matter of time till every business entity learns how to use their data to harness optimum business strategies and solutions.
