Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In order for that work to ultimately have any value, there also have to be mechanisms for applying it to real-world operations in some way. Those are both engineering tasks: the application of science to practical, functioning systems.
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- SMU - Master of Science in Data Science - Bachelor's Degree Required.
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
- Villanova Business - Master's in Analytics and Study Data Mining, Predictive Analytics Online
Data engineers focus on the applications and harvesting of big data. Their role doesn’t include a great deal of analysis or experimental design. Instead, they are out where the rubber meets the road (literally, in the case of self-driving vehicles), creating interfaces and mechanisms for the flow and access of information. They may be experts in:
- System architecture
- Database design and configuration
- Interface and sensor configuration
Although data engineers don’t always get the glory of coming up with crazy insights by querying and combining big data sources, their work is important in building the data stores that are used in that work, and in taking those insights and putting them to practical use.
Data Engineers Are Hands-On Information Processing Professionals
More than any other professional working in data science, data engineers have to be hands-on with the tools of the trade. A data engineer whose resume isn’t peppered with references to Hive, Hadoop, Spark, NoSQL, or other high-tech tools for data storage and manipulation probably isn’t much of a data engineer.
But as important as familiarity with the technical tools is, the concepts of data architecture and pipeline design are even more important. The tools are worthless without a solid conceptual understanding of:
- Data models
- Relational and non-relational database design
- Information flow
- Query execution and optimization
- Comparative analysis of data stores
- Logical operations
Data engineering is very similar to software engineering in many ways. Beginning with a concrete goal, data engineers are tasked with putting together functional systems to realize that goal.
From Robots to Cars, Data Engineers Turn Data Science Into Useful Systems
Data engineering has recently become prominent through ventures in autonomous vehicle design. While the servos and actual control mechanisms for self-driving cars are relatively straightforward to install and configure, the difficulty in building an autonomous car lies in duplicating the dozens of decisions made every second while using those controls:
- When to stop and when to go
- Where to turn
- How to recognize road signs and traffic controls
- How to interpret the actions of other vehicles and pedestrians
- What route to take from point A to point B
These are all inherently data-driven decisions. While data scientists may come up with the fancy algorithms that break a map down using artificial intelligence or designing machine learning techniques to train the vehicle what a bicyclist looks like from any angle, data engineers are responsible for creating the systems to take in the sensor information from GPS, LIDAR, cameras, and motion devices, process it, and turn it into actions for the wheel, gas, and brakes of the vehicle.
Of course, data engineering has many applications outside of autonomous vehicles, as well. Like most of the field of data science, the data engineering role is still being defined and may incorporate different aspects of the job at different organizations. Data engineers may be responsible for:
- Data architecture
- Database setup and management
- Data infrastructure design and build
In organizations with large amounts of data, particularly from disparate sources, all of this often boils down to building and filling up a data warehouse.
Data Warehousing Is The Killer App For Corporate Data Engineers
A data warehouse is a central repository of business and operations data that can be used for large-scale data mining, analytics, and reporting purposes. The warehouse allows many different data sources and repositories to be combined into a single useful tool for data scientists and business users to reference.
The process of building this resource, however, typically involves some significant extract, transform, and load (ETL, in industry parlance) operations, taking data form the source databases and reformatting it for inclusion into the warehouse. The design and coding of the processes behind the ETL operation are usually the responsibility of data engineers, as are the automation steps that are usually created at the same time to ensure a continuous data pipeline that can function without human intervention.
The organic growth of database support systems in modern businesses has made architecting and building functional data warehouses a complicated businesses indeed, and data engineers are the experts that companies turn to when it’s time to figure out how to get sales data from an Oracle database to talk with inventory records kept in a SQL Server cluster.
It’s the responsibility of data engineers to manage and optimize these operations as well. Some understanding of the underlying server hardware is often helpful in addition to having an expert knowledge of the database software itself.
Data engineers might also be asked to create data services for other users to consume. These pipelines run in the opposite direction of those bringing information into the data warehouse. Instead, they are common APIs (Application Programming Interfaces) that provide consistent access mechanisms to backend data stores. Essentially, data engineers write translators for their data stores that use a consistent language for accessing information even when the stores themselves differ considerably.
Learning to Be a Data Engineer
Data engineers need just as much education for their position as any other type of data scientist. Instead of high-level information theory and advanced analytics skills, data engineers focus more on learning:
- Data modeling techniques
- Relational and non-relational database theory and practice
- Database clustering tools and techniques
- ETL design
- Architectural projections
Although they will commonly go through regular data science master’s programs, data engineers will take electives that focus more on programming skills and data storage and manipulation tools.
When entering the workforce, they will often find it beneficial to seek out certifications that are specific to the tools they plan to work with, such as Microsoft’s family of SQL Server-related certifications, or MongoDB’s Certified Professional certification.
There are also a number of data engineering certifications however:
- Google Cloud Data Engineer Certification
- Cloudera Certified Professional Data Engineer
- Microsoft Certified Solutions Associate in Data Engineering with Azure
Although these are also tool-specific certifications (for Google Cloud Platform, Hadoop, and Microsoft Azure, specifically) they discuss those tools specifically from the data engineering perspective, teaching you how the systems can be used to solve data engineering problems.