Knowledge of SQL
Posted: Wed Jan 15, 2025 6:09 am
Top 5 Data Engineering Skills
Data engineering is a very broad and ever-evolving field. There are so many tools, frameworks, and technologies out there that it's nearly impossible to know and master them all. The tools you choose to learn may depend on the company you want to interview for or the data engineering group you fall into.
However, for most data engineering roles, there are five crucial areas you need to develop. If you need a starting point, begin with these essential data engineering skills:
1.
SQL is not just a skill, but a cornerstone in the field of data engineering. Mastery of SQL goes beyond basic queries; it involves understanding how to navigate and manipulate complex data sets using various SQL dialects, such as NoSQL for unstructured data, PostgreSQL for object-relational database systems, and MySQL for its flexibility and reliability. To truly excel in data engineering, it is crucial to develop a deep understanding of these variations.
If you want to get started with SQL, check out our SQL Fundamentals course , which gives you a complete introduction to the structured query language. You can also find our helpful Basic SQL Cheat Sheet , which covers the essential queries you'll need to know.
2. Data modeling techniques
Effective data modeling is critical in data engineering, serving as a blueprint for building scalable and optimized databases and warehouses. It encompasses more than just design—it involves understanding data relationships, constraints, and scalability. Mastering data modeling techniques is essential for executing efficient data pipelines, which form the backbone of data engineering projects, making it an essential data engineering skill.
You can get started with data modeling using tools like Power BI , and our Data Modeling in Power BI course is the ideal way to expand your knowledge.
3. Knowledge of Python
When it comes to programming languages, Python is often considered one of the most popular. With it, you can create data pipelines, integrations, automations, and clean and analyze data. It is also one of the most versatile languages and one of the best options to learn first.
Python is so ubiquitous that many data engineering tools use the language in their backend and often allow integration with data engineering tasks. To get started learning Python , check out our Data Engineer with Python course , which will teach you how to create effective data architecture, streamline data processing, and maintain large-scale data systems.
4. Hadoop for Big Data
Working with big data requires a specialized system, and Hadoop is among the most popular. It is a powerful, scalable, and low-cost tool that has become synonymous with big data.
Organizations and individuals produce huge amounts of data on a daily basis, and data engineers will often need to maintain, test, analyze, and evaluate these large data sets. Get started with big data by taking our Big Data Fundamentals with PySpark course .
5. Knowledge of AWS cloud services
The AWS cloud service consists of services like EC2, RDS, and Redshift. The use of cloud-based services has increased a lot over the years, and AWS is the most popular platform to get started with.
Data engineers need cloud computing skills, and you can start building your own with our AWS Cloud Concepts course .
To get some hands-on experience with cloud data warehouses, try our Exploring London's Travel Network project . It offers a great opportunity to work with AWS Redshift, Google BigQuery, and Snowflake right in the browser.
Additional Data Engineering Knowledge: Soft Skills
Beyond technical prowess, interpersonal skills play a critical role in the success email data of a data engineering career. Problem-solving, teamwork, and effective communication with both technical and non-technical audiences are crucial. These skills enable data engineers to not only excel in their technical roles, but also collaborate effectively, innovate, and drive projects to success. They must be cultivated alongside technical competencies to obtain a well-rounded professional profile.
Final thoughts
Data engineering is an increasingly vital field in today’s data-driven world. Its importance is reflected in the diverse roles and responsibilities of data engineers, from generalists to specialists in storage, programming, pipelines, and analytics. This field offers a dynamic and challenging career path, with the opportunity to constantly learn and adapt to new technologies and methodologies.
For those considering a career in data engineering or looking to improve their skills, the path is as rewarding as it is demanding. Key skills such as SQL, data modelling and Python form the foundation of a competent data engineer’s toolkit. However, remember that technical prowess must be balanced with essential interpersonal skills such as problem-solving, teamwork and effective communication.
If you want to pursue a career as a data engineer, our Data Engineer with Python career path will quickly get you up to speed on many of the core skills needed to land a job.
Topics
Data engineering is a very broad and ever-evolving field. There are so many tools, frameworks, and technologies out there that it's nearly impossible to know and master them all. The tools you choose to learn may depend on the company you want to interview for or the data engineering group you fall into.
However, for most data engineering roles, there are five crucial areas you need to develop. If you need a starting point, begin with these essential data engineering skills:
1.
SQL is not just a skill, but a cornerstone in the field of data engineering. Mastery of SQL goes beyond basic queries; it involves understanding how to navigate and manipulate complex data sets using various SQL dialects, such as NoSQL for unstructured data, PostgreSQL for object-relational database systems, and MySQL for its flexibility and reliability. To truly excel in data engineering, it is crucial to develop a deep understanding of these variations.
If you want to get started with SQL, check out our SQL Fundamentals course , which gives you a complete introduction to the structured query language. You can also find our helpful Basic SQL Cheat Sheet , which covers the essential queries you'll need to know.
2. Data modeling techniques
Effective data modeling is critical in data engineering, serving as a blueprint for building scalable and optimized databases and warehouses. It encompasses more than just design—it involves understanding data relationships, constraints, and scalability. Mastering data modeling techniques is essential for executing efficient data pipelines, which form the backbone of data engineering projects, making it an essential data engineering skill.
You can get started with data modeling using tools like Power BI , and our Data Modeling in Power BI course is the ideal way to expand your knowledge.
3. Knowledge of Python
When it comes to programming languages, Python is often considered one of the most popular. With it, you can create data pipelines, integrations, automations, and clean and analyze data. It is also one of the most versatile languages and one of the best options to learn first.
Python is so ubiquitous that many data engineering tools use the language in their backend and often allow integration with data engineering tasks. To get started learning Python , check out our Data Engineer with Python course , which will teach you how to create effective data architecture, streamline data processing, and maintain large-scale data systems.
4. Hadoop for Big Data
Working with big data requires a specialized system, and Hadoop is among the most popular. It is a powerful, scalable, and low-cost tool that has become synonymous with big data.
Organizations and individuals produce huge amounts of data on a daily basis, and data engineers will often need to maintain, test, analyze, and evaluate these large data sets. Get started with big data by taking our Big Data Fundamentals with PySpark course .
5. Knowledge of AWS cloud services
The AWS cloud service consists of services like EC2, RDS, and Redshift. The use of cloud-based services has increased a lot over the years, and AWS is the most popular platform to get started with.
Data engineers need cloud computing skills, and you can start building your own with our AWS Cloud Concepts course .
To get some hands-on experience with cloud data warehouses, try our Exploring London's Travel Network project . It offers a great opportunity to work with AWS Redshift, Google BigQuery, and Snowflake right in the browser.
Additional Data Engineering Knowledge: Soft Skills
Beyond technical prowess, interpersonal skills play a critical role in the success email data of a data engineering career. Problem-solving, teamwork, and effective communication with both technical and non-technical audiences are crucial. These skills enable data engineers to not only excel in their technical roles, but also collaborate effectively, innovate, and drive projects to success. They must be cultivated alongside technical competencies to obtain a well-rounded professional profile.
Final thoughts
Data engineering is an increasingly vital field in today’s data-driven world. Its importance is reflected in the diverse roles and responsibilities of data engineers, from generalists to specialists in storage, programming, pipelines, and analytics. This field offers a dynamic and challenging career path, with the opportunity to constantly learn and adapt to new technologies and methodologies.
For those considering a career in data engineering or looking to improve their skills, the path is as rewarding as it is demanding. Key skills such as SQL, data modelling and Python form the foundation of a competent data engineer’s toolkit. However, remember that technical prowess must be balanced with essential interpersonal skills such as problem-solving, teamwork and effective communication.
If you want to pursue a career as a data engineer, our Data Engineer with Python career path will quickly get you up to speed on many of the core skills needed to land a job.
Topics