What Do Data Engineers Do?
As big data becomes more prevalent, the need for data engineers who can collect and manage large amounts of data is increasing. They develop systems that gather and convert raw data into meaningful information for data scientists to interpret. The ultimate goal of a data engineer is to provide easy access to data for optimizing an organization’s performance.
Who is a Data Engineer?
Data Engineers collect data from multiple sources and convert it, then build and manage systems to generate this data. They prepare the collected data for data scientists and analysts using complex queries. The role of the data engineer is almost similar to that of a software engineer. They both use the same process when building data systems and architecture.
What is the Role of a Data Engineer?
The primary role of a data engineer is to procure and prepare data for data scientists and analysts to use, but they also have three primary functions:
1. Pipeline-centric engineers
Pipeline-centric engineers usually work in a midsized data analytics team with more complex data science tasks in distributed systems. This role is typical in large and mid-size companies.
Generalists are data engineers who focus on working on small teams, handling end-to-end data collection and processing. They often have more skill in data engineering, with a deeper understanding of systems architecture. The generalist is the best fit for a data scientist interested in becoming a data engineer.
3. Database-centric engineers
Data-centric engineers are responsible for creating, maintaining, and creating analytics databases. This role commonly exists in large firms which distribute data across multiple databases. These engineers develop data pipelines, customize databases to facilitate analysis, build table schemas using extract, and convert load ETL methods.
ETL is a data pipeline process where the engineer extracts data sources, transforms the data into a unified format to meet specific business needs, and loads the modified data on the storage.
The Responsibilities of a Data Engineer
The primary focus of a data engineer is to transform the raw data into something usable and readable before presenting it to a data scientist. In addition, they must design, create, test, combine, manage, and optimize the data from various sources. They also make the infrastructure for generating this data.
Data engineering aims to design data pipelines that run smoothly by writing complex queries to provide easy access to data. The primary responsibilities of a data engineer include the following:
- Create, evaluate, manage storage and maintain the database
- Analyze data and develop new validation methods
- Obtain datasets that comply with business needs
- Building algorithms to transform data into usable and viable information
- Gain a better understanding of the organization's goals by working with its management
The responsibilities of a data engineer may vary depending on the organization. For example, a data engineer in a small organization is responsible for setting up and maintaining its data infrastructure because a formal framework may not exist, so they have to perform general data-related tasks.
Meanwhile, data engineers in large organizations are responsible for creating data pipelines and maintaining data warehouses. Also, they may work with data infrastructure teams to solve problems by automating certain aspects of the data engineering process.
The Skills that Data Engineers Need
If you're considering a career in Data Engineering, you might be wondering what skills you'll need. Here are some skills that will be useful for Data Engineering.
1. Data Processing
Data processing is the core of the data engineering system. The primary responsibility of a data engineer is sourcing and processing data, which involves verifying data formats, selecting the right data sets, and processing data via batch or stream methods.
2. Manage Storage
Your data storage management influences how your downstream system processes data. A data engineer must correctly format and store data in the right place for easy access. As a data engineer, you must understand the pros and cons of the various file formats depending on data access and querying.
A data engineer works with different databases, such as NoSQL or SQL, which are becoming increasingly popular due to their scalability. For instance, the NoSQL allows you to add more features and scoring details. You also work with data warehouses, unique databases for analytical queries usually used by data scientists and analysts.
4. Machine Learning Framework
The machine learning framework may overlap with data science since you help in developing their models and help them design and scale them. Data engineers must familiarize themselves with machine learning frameworks such as Scikit-Learn and TensorFlow.
Containerization revolves around reusability, and a data engineer understands containers for packaging creations and transfers them to different environments. You can also scale up your infrastructure using tools to deploy these containers.
Data caching is crucial in ensuring that the data systems are highly responsive. Catching is faster than going to the database every time, and you can use memory to store the most frequently used data. Simply query the cache to increase the response time.
The Difference Between Data Engineer vs. Data Scientist
Data engineers organize and prepare data in various formats, including databases. They also create data pipelines so that data scientists can access the data. The data scientists analyze this data to help businesses run more efficiently and achieve better results.
The main difference between the two is their focus and skill sets. Data engineers have no specific focus and are usually all-rounded and competent in different areas. In contrast, a data scientist has a particular area of focus: in-depth data analysis. They solve large-scale problems, while data engineers create the infrastructure for them to do so.
How to Become a Data Engineer
It’s possible to have a rewarding career in data engineering if you have the right skills and knowledge. Most data engineers have a degree in computer science or a closely related field. In this rapidly evolving field, a degree can help you build the data engineering knowledge base you need.
Apart from the degree, you must also learn the basics of coding, ETL, machine learning, cloud computing, and database design to advance your career in data engineering. Obtaining a certification is a great way to demonstrate your skills to employers and an excellent means of developing your knowledge and expertise.