Introduction.
Data engineering is the process of building and designing systems that can help in collecting and analyzing data from multiple sources and in different formats.
With the large amount of data being stored everyday, data engineering is an emerging field that is growing continuously. If you have an interest in building pipelines that allow for transfer of data then you should consider a path in data engineering.
Roles and responsibilities of a data engineer.
- Identifying ways to improve data quality, reliability and efficiency.
- Preparing data for machine learning operations.
- Ensuring data is kept in the correct format.
- Building data pipelines and data warehousing.
- Deploying machine learning models.
What you should know.
To become a data engineer, there are a lot of things you need to learn. This article will give you a step by step guide that will help you become an expert data engineer.
1. Learn the basics of programming.
The best way to kickstart your journey as a data engineer is by learning a programming language. This is because you are able to enhance your ability to solve problems in a structured manner. Python is a nice option to start with in your programming journey. You can start by learning the basics of the language and working on simple projects as well, to enhance your skills.
After learning the basics of python programming you can now focus on pandas, a python library for data manipulation. After learning pandas you will be able to load data, handle missing values in data, manipulate columns in data as well and perform a lot of operations that can be done on data.
2. Learn SQL
Since you will be dealing with databases as a data engineer, learning database management systems is essential. With an understanding of python it will be easier to understand SQL and also NoSQL databases. Learn the basics and advance to solving complex queries in database systems.
3. Data Integration and ETL pipelines.
Understand ETL(Extract, Transform, Load) and ELT(Extract, Load , Transform) processes as well and how they work. They basically involve extracting data from a specified source, transforming data into the required format and loading the data into a specific location. These processes are used in data engineering projects.
4. Big data tools
As a data engineer you are required to know how to work with big data. Big data can be batch data or streaming data. Batch data is accumulated as time goes by. To work with such data you need specialized tools like Apache Spark. Learn Apache spark and understand how it can be used to conduct ETL processes.
5. Cloud Computing.
It has become easier to work with data now that most of it is stored on the cloud. Cloud computing technologies have made it easy to manage complex processes on the cloud. Learning AWS enables you to be able to work with data in the cloud without much struggle.
6. Data analysis and machine learning.
Data analysis is the process of understanding and analyzing data so as to gain meaningful insights from it. There are data analytics tools that can be used for this, with python being one of them. Also you can learn data visualization techniques using python or explore other visualization tools such as PowerBI, Tableau which can help you in creating reports and building dashboards.
Learn machine learning using python using machine learning libraries such as sci-kit learn and Tensorflow.
7. Familiarize yourself with git and GitHub.
Understand the importance of version control, collaboration in programming and how to use command line interfaces in data engineering
8. Projects
Work on data engineering projects to improve on skills and learn new ones as well. You can also contribute to open source projects on GitHub and collaborate with others. Always remember that the more projects you work on, the more you learn and grow your skills. Keep learning progressively.
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