Having been an ex-Head of Engineering at an AI startup, I got exposed to the exciting field of data engineering. In this article, I'll answer a few questions I'm often asked about data engineering.
What is data engineering?
Data engineering is a field of computer engineering that deals with managing, storing, optimizing, transporting and modeling data. It's a very broad field that deals with huge amounts of data produced; hence often referred as big data engineering.
Think of an ice-cream factory. The owner wants to monitor and optimize production at each step of manufacturing. The engineers put sensors that capture data every second at each process and communicate it to single server.
That's a lot of data per second, right?
How do you make sense of all the data collected by sensors? Well, first, we need to ensure the integrity of the data and make sure that it's ready for processing by data scientists.
You may think about the banking transaction data as another example. Tens of thousands of transactions happen every second - and all that data needs to be securely stored, transferred and maintained for further processing.
That's data engineering for you in a nutshell. It's an emerging field that's gain lot of popularity in recent times. In fact, big cloud providers like Google, Microsoft and Amazon (AWS) are all bullish about building tools to make data engineering faster and better.
Is big data engineering hard?
Data engineering is not at all hard to learn and to make entry into. If you have programming experience and are well versed with programming languages like Python, Java, Rust - you can pick data engineering in just about a week or two.
Even if you don't, you'll need a few weeks to build expertise into tools provided by cloud-service providers. These are easy to master and there are several tutorials available on the Internet. I'd highly recommend the following full-course on data engineering by Intellipat:
<iframe width="560" height="315" src="https://www.youtube.com/embed/OoHPhLV43gg" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>Is big-data engineering in demand?
I've offered a detailed answer to this question: Is data engineering in demand: Is big data engineering in demand?. The short answer is - yes! This is an ever-growing field and I do not see any reason why data engineering won't be in demand 5 or even 10 years from now.
The cloud adoption all over the world is growing; and there are several legacy projects that need data engineers to help maintain data at large scale.
Big data engineer salary - US vs India
Data engineering entry-level salary in India starts at about Rs. 8.5 LPA. The data engineer salary are around $130K/annum in the US. However, the salary you can get totally depends upon the role, your experience and above all, your negotiation skills.
Data Engineer vs Data Scientist vs Data Analyst
Data engineer, data scientist and data analyst are all different roles; but are often confused to be the same. Data engineer job is to make sure that data is collected, stored and maintained. Data engineers typically work with unstructured data.
Data scientists will use the structured data to build models and make predictions. They'll deal with AI/ML algorithms to make sense of the available data.
Data analysts will perform analysis on the data available and make sure that it can be visualised easily. There are several tools used by data analysts like Excel, Tableau, Spark, Microsoft Power BI.
Big data engineering future scope
Data engineering is still in nascent stage and has a huge future scope. The demand for data engineering will grow as big cloud providers pump money into building solid data-pipeline systems. The entry into this field is easy; and there are very few talented data engineers available.
Is big data and data engineering same?
No, they are not the same. Big data is a term referred to large amounts of data produced by companies. While data engineering, as we learned, is about management of this big data.
If you have follow-up questions, let me know.
