Get your data engineers ready! Every day is an opportunity to make sense of complex data sets and drive meaningful insights.
While many data-related roles have a few things in common, it’s essential that we’re able to distinguish that data engineering stands out as the superstar of the bunch.
Designing and implementing robust data pipelines, data warehouses, and data lakes that enable data analysts and data scientists to do their job effectively, it’s safe to say that data engineers are the backbone of any data-driven organisation.
But what sets data engineering apart from other data-related roles is the breadth of skills required.
Get to know all about data engineering with us. Join in on the data revolution and let’s dive in!
Understanding Data Engineering
Did you know that data engineering is projected to be the fastest-growing job role over the next ten years, with a 50% growth rate?
Data engineering involves building, designing, and maintaining the infrastructure required for data storage, processing, and analysis. Data engineers use their expertise in data modelling, data warehousing, ETL (extract, transform, load) processes, and cloud computing to ensure that data is accessible and accurate for other data-related roles.
In other words, data engineering is the backbone of the data-driven world.
The Differences between Data Engineering and Other Data-related Roles
1. Data Analytics
Data analytics focuses on analysing and interpreting data using statistical and quantitative methods to extract insights.
That’s where data analysts create reports, dashboards, and visualisations to communicate these insights to stakeholders. While data engineering builds the infrastructure required for data processing, data analytics focuses on analysing and interpreting the data.
2. Data Science
Data science involves using machine learning algorithms and statistical models to make predictions and identify patterns in data.
So data scientists build predictive models and algorithms to solve business problems. While data engineering builds the infrastructure required for data processing, data science focuses on building predictive models and algorithms.
3. Business Intelligence
Business intelligence involves using data to make informed business decisions.
It’s where we need business intelligence analysts create reports, dashboards, and visualisations to provide insights into business performance.
While data engineering builds the infrastructure required for data processing, business intelligence focuses on using data to inform business decisions.
4. Database Administration
Database administration involves managing the databases that store an organisation’s data.
We need database administrators to ensure the performance, security, and reliability of the database. While data engineering builds the infrastructure required for data processing, database administration focuses on managing the databases that store the data.
The Importance of Data Engineering
Data engineering is crucial for organisations dealing with large amounts of data.
And businesses are placing more importance on hiring data engineers to improve their ability to enact real-time decision making.
In fact, Dr M Maruf Hossain, a data science expert, reveals that businesses are seeing the importance of the role of data engineers, as data scientists spend months cleaning the data and generating insights. Only to go back and do the same processes and analyses on newly arrived data, which often renders the insight useless due to the time taken to produce it.
Without proper data engineering infrastructure, it can be challenging to store, process, and analyse data.
Data engineering ensures that data is accessible, accurate, and consistent for other data-related roles. It gives organisations the ability to make informed business decisions and gain a competitive edge in their industry.
The Future of Data Engineering
The amount of data being generated is only going to increase.
According to Harvard Business Review, it’s estimated that over 463 exabytes of data will be created each day globally by 2025. Which, essentially, means that there will be an upsurge in the need for data engineering in the years to come.
Data engineering will also continue to play a critical role in enabling organisations to manage and process the data effectively.
With the rise of cloud computing, organisations can build and maintain their data engineering infrastructure more efficiently.
Get The Right Data Engineers To Help You
Data engineering plays a critical role in enabling organisations to effectively manage and process large amounts of data. It focuses on building the infrastructure required for data processing.
As the amount of data being generated continues to increase, the importance of data engineering will only continue to grow.
At Teraflow.ai, we specialise in providing data engineering services that enable organisations to make AI work for them. We have a team of experienced data engineers who can design, build, and maintain the infrastructure required for data storage, processing, and analysis.
By leveraging our expertise, organisations can ensure that their data is accurate, consistent, and accessible to other data-related roles.
Contact us today to learn more about how we can help your organisation make the most out of its data!