We are living in a data-driven world as organizations of all types and sizes rely on data to make decisions. Consequently, the data field is rapidly growing as more and more data is being generated every day. Data engineers play a vital role in ensuring that this data is of the highest quality and is available when and where it is needed. They are the link between the management’s big data strategy and the data scientists who need to work with data.
What is Data Engineering?
Data engineering is the development, implementation, and maintenance of systems and processes that take in raw data and produce high-quality, consistent information that supports downstream use cases, such as analysis and machine learning.
It is the intersection of security, data management, DataOps, data architecture, orchestration, and software engineering. A data engineer manages the data engineering lifecycle, beginning with getting data from source systems and ending with serving data for use cases, such as analysis or machine learning. They play an important role in creating core data infrastructure that allows for analysts and end-users to interact with data which is often locked up in operations systems.
Data engineering helps data scientists and analysts by unifying data sets and enabling them to answer questions quickly and efficiently. By bringing together an organization's data from different systems, it is able to give a comprehensive view of the market.
What does a data engineer do?
A data engineer works in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and analysts to interpret. There are three main roles for data engineers:
Generalists: These ones may have more skill than most but less systems architecture knowledge.
Pipeline-centric engineers: These ones typically work on midsize data analytics and more complicated data science projects across distributed systems.
Database-centric engineers: They implement, maintain, and populate analytics databases. Typically found in organizations where data is distributed across several databases.
“Doesn't matter how much data you have, it's whether you use it successfully that counts.”
Here are some of the tasks a data engineer does:
They use a systematic approach to plan, create, and maintain data architectures while also keeping them aligned with business requirements.
Before initiating any work on the database, they have to obtain data from the right sources. After formulating a set of dataset processes, data engineers store optimized data.
They conduct research in the industry to address any issues that can arise while tackling a business problem.
Data engineers use a descriptive data model for data aggregation to extract historical insights. They also make predictive models where they apply forecasting techniques to learn about the future with actionable insights. Likewise, they utilize a prescriptive model, allowing users to take advantage of recommendations for different outcomes.
They dive into data and pinpoint tasks where manual participation can be eliminated with automation.
You will often hear data engineers describe most of their work as moving data from point A to point B. They do this using data pipelines.
Data pipelines are generally structured as either an Extract, Transform, Load or an Extract, Load, Transform (ETL vs ELT.) Data Engineers monitor and troubleshoot these pipelines.
The Extract involves connecting to a data source such as an API and pulling the data out of it.
Transform is used to standardize data as well as starting to integrate data by adding in IDs which are also standardized, deduplicating data, and adding in more human-readable categories.
Load is meant to load data into a table in the data warehouse such as Snowflake.
Scenario
For a data engineer on a campus in Kenya, this is how they would typically work:
Data collection: The first step is to collect the data from a variety of sources, such as student records, research data, and financial data. This may involve developing and implementing data collection tools and processes, as well as working with other departments and stakeholders to ensure that the data is collected in a consistent and compliant manner.
Data cleaning and preparation: Once the data has been collected, it needs to be cleaned and prepared for analysis. This may involve removing errors and inconsistencies, transforming the data into a consistent format, and loading the data into a data warehouse or other data storage system.
Data modeling: The next step is to develop a data model that defines the relationships between the different data elements. This is important for ensuring that the data can be easily analyzed and queried.
Data pipeline development: Develop and maintain data pipelines. Data pipelines are automated processes that move data from one source to another and often perform transformations on the data along the way. Data pipelines are essential for ensuring that data is always up-to-date and accessible to users.
Data analysis: Work with data analysts and scientists to develop and implement data analysis solutions. This may involve developing machine learning models, building data visualization dashboards, and creating custom reports.
Data governance: This is the process of ensuring that data is managed in a secure and compliant manner. Take part in developing and implementing data governance policies and procedures, as well as monitoring and enforcing compliance.
Conclusion
Data engineering is a complex and challenging field, but it is also incredibly rewarding. Data engineers have the opportunity to work with cutting-edge technologies and solve complex problems. They play a vital role in helping organizations to make better decisions and improve their operations.