What is big data engineering?
By - Kaustubh Katdare • 10 months ago • 16.6k views
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:
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.
I've been working as a big data engineer for over 6 years. I'm glad to see this discussion on big data engineering projects and their use cases.
Big data engineers play a crucial role in making sense of the massive amounts of data generated every day.
They work on projects that help organizations gain insights and make data-driven decisions. Here are some typical use cases for big data engineering:
Financial institutions and credit card companies rely on big data engineering to analyze user transactions in real-time.
This helps them identify unusual patterns and detect fraudulent activities more effectively.
I've worked on an ecommerce project to offer personalised recommendation to the user based on their browsing history. We used big data to analyze customer preferences and behavior to offer personalized recommendations.
The end result is that user experience improves and it increases customer satisfaction, driving sales and engagement.
Businesses analyze social media data to understand how customers perceive their products or services.
Big data engineering techniques help process and analyze vast amounts of unstructured data from various social media platforms to obtain actionable insights.
Hospitals, pharmaceutical companies, and other healthcare stakeholders use big data engineering to analyze patient records and medical data.
This helps them improve patient care, identify disease patterns, and streamline healthcare operations.
Supply chain optimization:
By analyzing data from multiple sources like weather, traffic, and supplier information, companies can optimize their supply chain management.
This allows them to reduce costs, improve efficiency, and respond to market demands more effectively.
Big data engineering can help companies predict equipment failures and schedule maintenance to minimize downtime.
Analyzing data from IoT sensors, usage patterns, and other sources enables companies to identify potential issues before they escalate.
I have several friends and ex-colleagues on the Smart Cities project by Indian Government.
They make use of big data to analyze data from various sources, such as traffic patterns, air quality, and energy usage. This helps them make informed decisions to improve urban planning, public transportation, and overall quality of life.
These are just a few examples of the many projects big data engineers work on.
The possibilities are endless, and as data generation and collection continue to grow, the demand for big data engineering expertise will only increase.
Keep exploring and happy data engineering!
I am convinced that Big Data Engineering is set to expand rapidly as we move further to adopt digital computing. It's going to create numerous opportunities for professionals in the industry.
Opportunities in Big Data Engineering:
The sheer volume, variety, and velocity of data being generated today are driving the demand for skilled big data engineers.
Following are just few of the areas where you can expect significant growth and opportunities:
Real-time data processing aka RTDP:
The rise of IoT is phenomenal in the recent times. The need for real-time data processing has never been greater. The responsibility to handle big amounts of data will be on BD engineers. Plus this data will need to be handled in real time.
Artificial Intelligence and Machine Learning:
Several organizations are moving towards AI and ML for their operations. The big data engineers will have to play a critical role in ensuring the smoother transition.
They'll also have to work on training the models and write smarter code for existing applications.
Healthcare, finance, retail, manufacturing and several other large industries are leveraging big data to make more informed decisions and improve their operations.
Big data engineers with domain-specific knowledge will be in high demand to develop tailored solutions.
Cloud-based big data solutions:
As more organizations transition to cloud platforms, big data engineers with expertise in cloud technologies and distributed computing will be sought after to help design and manage scalable big data infrastructures.
Certifications to Boost Your Career
There are several certifications that can help you stand out in the competitive big data landscape and advance your career. Let me share a few important ones that are accepted by the industry -
AWS Certified Big Data Engineer:
This certification demonstrates your knowledge of big data solutions on the AWS platform, including data processing, storage, and visualization.
Google Cloud - Professional Data Engineer:
This certification is for professionals who want to showcase their skills in designing, building, and managing data processing systems using Google Cloud technologies.
Microsoft Certified: Azure Data Engineer Associate:
This certification covers a wide range of Azure-based big data engineering topics, including data storage, data processing, and data security.
Apache Spark Certifications:
Apache Spark is a popular big data processing framework, and certifications like Databricks Certified Developer for Apache Spark or the Cloudera Data Engineer Certification can help validate your skills in this area.
Cloudera Certified Data Engineer:
This certification focuses on the skills required to develop reliable, scalable, and maintainable data pipelines using Cloudera's big data platform.
Trust me, the future of big data engineering is bright, with plenty of opportunities for professionals who are skilled in managing and processing vast amounts of data.
Certifications can help you stay competitive, showcase your expertise, and boost your career prospects in this exciting field.
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