Understanding Data Engineering: The Unsung Heroes of the Data Science World
As we navigate the complex world of data science, it's easy to overlook one of the most crucial players in the ecosystem: the data engineer. But what exactly is a data engineer, and how do they contribute to the success of a data-driven organization? In this article, we'll delve into the world of data engineering, exploring its definition, responsibilities, and the skills required to excel in this role.
The Role of Data Engineers
Imagine being hired as a data scientist at a young startup tasked with predicting customer churn. You want to use a fancy machine learning technique that you've been honing for years, but after digging around, you realize that all your data is scattered across multiple databases, optimized for applications rather than analysis. To make matters worse, some legacy code has caused corruption in the data, making it difficult to work with. This is where the data engineer comes in – someone who can extract data from these sources, load it into a single database, and optimize the database scheme to make it faster to query.
The Data Engineer's Task
A data engineer's task is to make life easier for data scientists like you. If the data currently comes from several different sources, no problem! The data engineer extracts the data from these sources, loads it into a single database ready to use, and optimizes the database scheme so that it becomes faster to query. They also remove corrupt data, ensuring that the data quality is safeguarded.
The Definition of Data Engineering
In 2015, Data Science Again published an infographic on "Who Does What" in the data science industry. The definition of a data engineer was clearly stated: an engineer who develops constructs tests and maintains architectures such as databases and large-scale processing systems. This definition has remained largely unchanged since then, but it still holds up today. A data engineer is focused on processing and handling massive amounts of data, setting up clusters of machines to do the heavy lifting.
The Tasks of a Data Engineer
So, what are the typical tasks of a data engineer? Developing a scalable data architecture is at the top of their list. This involves designing and implementing systems that can handle large volumes of data. Streamlining data acquisition is another key task, as data engineers need to ensure that data is coming from multiple sources in a timely and efficient manner. Setting up processes that bring data together from these sources is also crucial. Finally, safeguarding data quality by cleaning up corrupt data is essential.
Cloud Technology
Data engineers are typically experienced users of cloud technology, particularly cloud service providers like AWS, Azure, or Google Cloud. They need to have a deep understanding of how to set up and maintain these systems in order to ensure that they are running efficiently and effectively. This requires a strong grasp of cloud architecture, security, and scalability.
Comparison with Data Scientists
While data engineers focus on processing and handling massive amounts of data, data scientists spend their time mining for patterns and insights in large datasets. They develop sophisticated machine learning models, build predictive models using statistical techniques, and create tools to monitor essential business processes. While data scientists have a deep understanding of the business itself, data engineers have a strong technical foundation.
Conclusion
In conclusion, data engineering is a critical component of the data science ecosystem. Data engineers are the unsung heroes who make it possible for data scientists to focus on analyzing and interpreting data rather than worrying about where to find it or how to manage it. By understanding their role, responsibilities, and skills, we can better appreciate the importance of data engineers in our organizations.
"WEBVTTKind: captionsLanguage: enhey my name is Vincent I'm a data and software engineer at data gap if you've ever heard of data science there's a good chance you've heard of data engineering as well this course will help you take your first steps in the world of data engineering all very exciting so let's get started in the first chapter we'll start off by introducing the concept of data engineering in the second chapter you'll learn more about the tools data engineers use the third chapter is all about extracting transforming and loading data or ETL finally you'll get to have a peek behind the curtain in the case study on data engineering at theta camp but first let's understand what data engineers to imagine this you've been hired as a data scientist at a young startup tasked with predicting customer churn you want to use a fancy machine learning technique that you have been honing for years however after a bit of digging around you realize all of your data is scattered around many databases additionally the data resides and tables that are optimized for applications to run not for analysis to make matters worse some legacy code has caused a lot of the day that to be corrupt in your previous company you never really had this problem because all of the data was available to you in an orderly fashion you're getting desperate incomes the data engineer to the rescue it is a data engineer's task to make your life as a data scientist easier do you need data that currently comes from several different sources no problem the data engineer extracts data from these sources loads it into a single database ready to use at the same time they've optimized the database scheme so it becomes faster to query they also removed corrupt data in this sense the data engineer is one of the most valuable people in a data-driven company that wants to scale up back in 2015 data again published an infographic on precisely this who does what in the data science industry in this infographic we described the data engineer as an engineer that develops constructs tests and maintains architectures such as databases and large-scale processing systems Allah has changed since then but the definition still holds up the data engineer is focused on processing and handling massive amounts of data and setting up clusters of machines to do the computer typically the tasks of a data engineer consists of developing a scalable data architecture streamlining data acquisition setting up processes that bring data together from several sources and safeguarding data quality by cleaning up corrupt data typically the data engineer also has a deep understanding of cloud technology they generally are experienced using cloud service providers like AWS Hesher or Google Cloud compare this with the tasks of a data scientist we spent their time mining for patterns and data blind sophistical models on large data sets building predictive models using machine learning developing tools to monitor essential business processes for cleaning data by removing statistical outliers data scientists typically have a deep understanding of the business itself let's seehey my name is Vincent I'm a data and software engineer at data gap if you've ever heard of data science there's a good chance you've heard of data engineering as well this course will help you take your first steps in the world of data engineering all very exciting so let's get started in the first chapter we'll start off by introducing the concept of data engineering in the second chapter you'll learn more about the tools data engineers use the third chapter is all about extracting transforming and loading data or ETL finally you'll get to have a peek behind the curtain in the case study on data engineering at theta camp but first let's understand what data engineers to imagine this you've been hired as a data scientist at a young startup tasked with predicting customer churn you want to use a fancy machine learning technique that you have been honing for years however after a bit of digging around you realize all of your data is scattered around many databases additionally the data resides and tables that are optimized for applications to run not for analysis to make matters worse some legacy code has caused a lot of the day that to be corrupt in your previous company you never really had this problem because all of the data was available to you in an orderly fashion you're getting desperate incomes the data engineer to the rescue it is a data engineer's task to make your life as a data scientist easier do you need data that currently comes from several different sources no problem the data engineer extracts data from these sources loads it into a single database ready to use at the same time they've optimized the database scheme so it becomes faster to query they also removed corrupt data in this sense the data engineer is one of the most valuable people in a data-driven company that wants to scale up back in 2015 data again published an infographic on precisely this who does what in the data science industry in this infographic we described the data engineer as an engineer that develops constructs tests and maintains architectures such as databases and large-scale processing systems Allah has changed since then but the definition still holds up the data engineer is focused on processing and handling massive amounts of data and setting up clusters of machines to do the computer typically the tasks of a data engineer consists of developing a scalable data architecture streamlining data acquisition setting up processes that bring data together from several sources and safeguarding data quality by cleaning up corrupt data typically the data engineer also has a deep understanding of cloud technology they generally are experienced using cloud service providers like AWS Hesher or Google Cloud compare this with the tasks of a data scientist we spent their time mining for patterns and data blind sophistical models on large data sets building predictive models using machine learning developing tools to monitor essential business processes for cleaning data by removing statistical outliers data scientists typically have a deep understanding of the business itself let's see\n"
