The Ultimate Data Engineer’s Learning Path
What is data engineering?
Data engineering is the art of designing, building, and maintaining the systems and infrastructure used to collect, store, process, and analyze data.
Data engineering also sees to the creation of data management systems and infrastructure that allows data scientists and analysts to access, process and analyze data with ease. This includes building data lakes, warehouses, and marts and creating data access and retrieval systems. It is a very important component of the data life cycle which enables organizations to effectively collect, manage and use large volumes of data. Data engineering is where software engineering, cloud and DevOps meet.
Who is a data engineer?
A data engineer is a person who is responsible for designing and maintaining data pipelines, data storage systems, and data processing systems. These tasks include data modelling, warehousing, integration, quality management, and security. They also ensure that the data pipelines are efficient and scalable and can handle the volume, velocity and variety of data that the organization is dealing with to ensure that the data is accessible, accurate, and useful for analysis.
Should you become a Data Engineer?
Apart from the fact that data engineers are in high demand, it is very exciting because …
What skills do you need to be a data engineer?
Data engineering is a highly technical field that requires a diverse set of skills and technology that are always changing. Here are some of the key skills that a data engineer should have include:
- Strong programming skills: Data engineers need to be proficient in at least one programming language, such as Python, R, or Scala, as well as SQL. They should also be familiar with big data technologies such as Apache Hadoop and Apache Spark.
- Familiarity with data storage and management systems: Data engineers should be familiar with various data storage and management systems, such as relational databases, NoSQL databases, and data warehousing systems. They should also be familiar with data modelling concepts and techniques.
- Data pipeline and ETL tools: Data engineers should have experience with data pipeline and ETL (extract, transform, load) tools, such as Apache NiFi, Apache Kafka, Apache Airflow, Talend etc. These tools are used to build data pipelines that collect, store, and process data.
- Understanding of distributed systems: Data engineers should understand distributed systems, such as how data is stored and processed across multiple machines. This knowledge is essential for designing and implementing big data systems that can handle large amounts of data.
- Cloud computing skills: As more and more organizations are moving their data infrastructure to the cloud, data engineers should have experience with cloud computing platforms such as AWS, Azure, and Google Cloud — The big three. This includes an understanding of how to deploy, manage and scale data infrastructure in cloud environments.
- Strong analytical and problem-solving skills: Data engineers should have strong analytical and problem-solving skills, as they are responsible for designing and implementing data pipelines, troubleshooting issues, and ensuring data quality.
- Understanding of data governance and security: Data engineers should be familiar with the best practices and how to implement them in the data pipeline, such as data encryption, access control, and data masking.
- Strong communication and collaboration skills: Data engineers often work with cross-functional teams and must be able to communicate effectively with data scientists, analysts, internal and external customers and other stakeholders.
The Data engineering learning path
The diagram below is in no way exhaustive or comprehensive. I would recommend picking at least one tool from each box and studying extensively(this is what I did). Knowledge of one product or tool is usually transferable to the other; for example, AWS cloud infrastructure can be transferable to Microsoft Cloud Infrastructure etc. It is important to find what you enjoy learning about- maybe Linux or wrangling NoSQL data and learn more about them.
This learning path was inspired by Temilola Onaneye. You can connect with him on Twitter
No one course or tutorial covers all you need to know about a certain topic, so ensure you research and find a tutorial or course that explains the topic you want to learn to your understanding.
Build a portfolio of data engineering projects:
To grow your skills and flex your newly gained knowledge, you should build projects that expose you to real-life scenarios. Here are some sample data engineering projects you could build
- Project Ideas by @SeattleDataGuy
- Batch Processing Project
- Real-time Streaming Pipeline by Tim Berglund
- Spark End-to-End Project by @dataengineeringforeveryone2186
- Apache Kafka Project by @dataengineeringvideos
- Databricks Project by @cloudfitness
Build projects! ask for help when you are stuck but do not give up.
Conclusion
Do you want to make data accessible to other data professionals to aid their job? Do you like putting data together and maintaining them? Data engineering might be the place for you!
There is a high demand for data engineers. According to Indeed, the number of job postings for data engineers has increased by more than 400 percent in the past five years, and the market is waiting for you!
It is important to note that data engineering is a rapidly evolving field, and technologies and practices are emerging and changing daily. Therefore, data engineers should continuously learn and adapt to new technologies and trends to stay relevant and updated in the field.
If you have not, you can read my story on My Journey to Data Engineering, it details my journey and how i found Data Engineering and connect with me on socials. I am always rooting for you!!
