What is Data Engineering?:(The Secrets Behind the Scenes)
TL;DR (Categories of Data Engineers)
Original Blog Post
Introduction
Data engineers(You) are the superheroes of the data world.
Data Engineer expertise to transform raw data into a user-friendly and comprehensible format for all. They play a crucial role in making data accessible and understandable to individuals across various domains.
However, Not all data engineers are the same
In today’s world, business produces an immense amount of data. We called them Big data. Everything from customer feedback to sales performance, and stock price influences how a company operates. However, understanding what data tells us is not always intuitive. And that is the reason why most businesses rely on Data Engineering.
What is Data Engineering?
So, What is Data Engineering? Is it a role or is it a subject?
I see things a bit differently. To me, “Data Engineering is a field where we learn how to derive insight from data.” Data Engineering is the process of designing a scalable system that collects and analyzes large and complex datasets from different source systems. Let’s dive into how these systems help businesses use data in useful ways.
The Categories of Data Engineering
I genuinely believe that the distinctions among data engineers result from their skill sets, shaped by market demands driven by companies hiring for specific projects. The different categories may also arise from the diverse types of data prevalent within organizations. Typically, a data engineer’s career trajectory follows a similar path, and no single category inherently outshines another. In the Modern Data Stack(MDS) not having skills in certain areas can create challenges, affecting the quality of data pipelines and slowing down career growth.
- Data Explorer: Database/Data Warehouse and Analytics Specialist
- Skills: Proficient in data warehouse management, database analytics, metrics, and dashboard development. Additionally, skilled in SQL and data modeling.
2. Data Integrator: Python, Airflow, dbt, other tool Specialist
- Additional Skills: Advanced in Python, skilled in building data pipelines with tools like Airflow, Spring DataFlow, and data transformation using dbt.
3. Data Architect: Scala/Java, Distributed Systems, Big Data, ML Expert
- Additional Skills: Mastery in Java/Python/Scala extensive experience in designing distributed systems and writing connectors in Java and Python, and hands-on experience with advanced technologies such as Kafka, Spark, Big Data, and Machine Learning.
Does Your Business Need Data Engineering?
In the current world, every individual is generating an immense amount of data. You can think of any company on the internet. The problem with the data within the organization is that data is in a silo. which has enough information to drive the world. To make that data speak, companies spend a lot of money and time to make something intelligent out of it. Some companies succeed, but most of them do not. Because of a lack of knowledge and resources.
Yes, Companies, regardless of their size, grapple with a substantial volume of diverse data when attempting to address critical business inquiries. The role of data engineering is to facilitate the entire ecosystem of processes that ensure data is easy and readily available for informed decision-making. At all levels of the business, including areas like Data Analytics, Data Science, and Business decision.
Why Is Data Engineering Important ????
Read the Full Blog Here…
Footnotes:
Additional Reading
- AI vs ML vs DL vs Data Science
- Logistic Regression for Machine Learning
- Cost Function in Logistic Regression
- Maximum Likelihood Estimation (MLE) for Machine Learning
OK, that’s it, we are done now. If you have any questions or suggestions, please feel free to comment. I’ll come up with more Machine Learning and Data Engineering topics soon. Please also comment and subs if you like my work any suggestions are welcome and appreciated.
