What Is Data Engineering?
Companies are increasingly using data to create value for their organizations, and they rely on data engineering to accomplish this. So what is data engineering, exactly? In this article, we explore not only its definition, but also how data engineers are crucial to a company’s ability to gather actionable insights. We’ll wrap up with examples of the types of projects that call for data engineering.
What is data engineering?
Data engineering describes the process of designing and building various systems for collecting, storing, transforming and enriching large amounts of data from a variety of sources. While data engineers aren’t typically the ones who interpret data (that task lies in the realm of the data scientist and analysts), they enable the data team to prepare and access relevant data needed to support business goals. Data engineers also serve as data quality experts, ensuring the data they manage is accurate, complete, reliable and relevant.
Data engineering vs. data science
Data engineers and data scientists have much in common. It can be easy to confuse the two, especially in smaller organizations where one professional may be asked to perform some of the tasks traditionally performed by the other.
However, the data engineer and the data scientist each play a distinct role. Data engineers collect and manage all the relevant data required to meet a specific business need, including building, testing and maintaining data pipelines. Data scientists then aggregate, optimize, test, analyze and interpret the data before presenting their findings to key stakeholders.
Careers in data engineering and data science both require an extensive background in computer programming and mathematics. Data engineers often enter the profession with strong skills in computer science and computer engineering, as well as experience creating complex systems. Careers in data science require extensive training in math, statistics, and the creation of artificial intelligence and machine learning models.
Fundamentals of data engineering
Data engineers are vital members of the data team. Their specialized skills are essential for helping businesses successfully implement their data strategy. Here are four core capabilities data engineers bring to the process of translating raw data into actionable insights.
Building data pipelines
Data pipelines transport raw data from various sources to a final destination, typically a cloud data warehouse, data lake, or data lakehouse. As the data makes its way through the pipeline, it’s transformed and optimized into a usable format. Data engineers design and create data pipelines customized to the business needs of the organization they support.
Optimizing data processing performance
Data engineers are responsible for enhancing an organization’s data processing operations. Factors such as the time it takes to transform the data, how often new data is received and how quickly updates to the data’s target destinations can be made are all factors that influence decisions on how to best allocate compute and storage resources to meet the business objective.
Building a CI/CD pipeline
Today, fast-evolving business requirements, data governance policies and data security requirements create a dynamic production environment. A continuous integration / continuous delivery (CI/CD) pipeline combines code building, testing and deployment into a single, seamless flow. The CI/CD process is commonly used in data engineering.
Creating and implementing a data recovery plan in the event of a system failure
Data engineers help their organizations plan for unexpected data loss or compromise. They are responsible for ensuring that the data pipelines, databases and data warehouses they create and manage are in alignment with the company’s data recovery plan.
Data engineering Projects
For those interested in a career in data engineering, a variety of projects provide an excellent opportunity to hone your skills and become part of a portfolio you can present to prospective employers. These data engineering projects are a great place to get started.
Construct a data repository
A data warehouse is a data management system critical for supporting data analysis and other business intelligence tasks. These systems typically contain very large amounts of historical data, drawn from numerous sources, and are optimized to support complex data analysis activities.
Perform data modeling
Creating a new database structure requires understanding how data will move into and out of the database. Data modeling is used to visually represent this process by diagramming data flows. It gives definition to the characteristics of data formats, structures, and database handling functions.
Build a data pipeline
Data pipelines move data from multiple sources to a final destination—a single source of truth that’s most often a cloud data warehouse. As the data is ingested, it is transformed and optimized for more efficient storage and analysis.
Create a data lake
Data lakes have become an increasingly important tool for data engineers. A data lake is a centralized repository used to process, store and secure near-limitless amounts of data in its original format. They can accommodate structured, semi-structured and unstructured data at scale, making them a useful tool for cybersecurity and large-scale analytics applications.
Create a data warehouse
With a cloud data warehouse, data engineers can help organizations manage their data effectively and ensure that data is governed, accessible and secure. With ETL, ELT and streaming data ingestion into the data warehouse, engineers can build the data pipelines business stakeholders need to access actionable insights.
Set yourself apart with Snowflake certifications and live trainings
Snowflake’s importance in supporting the data engineering workflow continues to grow. That’s why we’ve created Snowflake certifications and live training to provide the knowledge and industry validation you need to advance your career in this fast-evolving field. Snowflake’s live training offers access to top-level Snowflake experts in live, virtual labs. Our hands-on approach to instruction ensures you’ll leave with actionable skills that have real-world application. With topics geared for everyone from entry-level beginners to industry-tested experts, Snowflake live training will help you get the most out of Snowflake.
Looking to distinguish yourself as a Snowflake expert? Our Snowflake certifications are designed for data engineers who want to transform their hard-earned expertise working in Snowflake into an industry-recognized credential. With two credential tracks—the SnowPro Core Certification and SnowPro Advanced Certification—Snowflake users with either core knowledge or role-based expertise have the opportunity to earn certification.
The SnowPro Core Certification demonstrates proficiency in applying core expertise implementing and migrating to Snowflake. The SnowPro Advanced Certification caters to data professionals with time-tested industry experience working in Snowflake. This advanced-level certification series consists of five role-based credentials spanning Architect, Administrator, Data Engineer, Data Scientist and Data Analyst (available in late 2022) roles. Learn new skills or demonstrate your existing ones with Snowflake live training and certifications.