Data engineering plays an extremely significant role in any data-driven business, because it can be used to optimize and harness large amounts of data in those businesses. In this article you will learn what data engineering is, how the data engineering process looks like and how it differs from data science and data analytics.
What is data engineering?
Data engineering is defined as the task of making raw data from different sources usable for data scientists and other stakeholder groups within an organization. The data engineering process involves creating data pipelines that combine data from multiple sources, processing and transforming data, and storing it behind the correct access and security layers so that it is easily available to the end-user with proper security and access levels.
Depending on the platform used within organization data engineers typically work with data warehouses, data lakes, data lakehouses or similar. Data engineering plays a significant role in supporting companies to make better use of their data assets because it represents an important preparation step. Using data engineers is recommended as a key pillar for a data-driven company to enable complex data use cases such as predictive maintenance, assurance, personalization and many more.
How does the data engineering process look like?
- Requirements Gathering Stage: Multiple exchanges with customers or internal stakeholders are done to gather requirements for the system, which is then broken down into working items. The result of this stage is a plan for all subsequent data-related processes.
- Design Stage: After an in-depth analysis of requirements, data engineers brainstorm and propose a solution. Workflows and processes that observe best practices in terms of security, compliance, infrastructure, and development standards are created. Furthermore, it is identified which tools, technologies, and processes are best suited.
- Data Modelling Stage: The data-oriented structures are explored and visualized in this stage. The main goal of this step is to visualize and illustrate the relationships within the data. This step is crucial for detecting where scalability will be of importance and how to ensure absolute efficiency when retrieving and processing data.
- Access and Permissions Stage: This stage runs parallel with the design stage. All the access and permission requirements are identified at this point. Proper procedures are followed to request above identified accesses and permissions.
- Data ingestion stage: In this stage systems and pipelines are developed to bring data from the source to the data lakes (or similar). Depending on the use case some key actions at this stage include: Data cleaning, data lake design and development, ingestion, pipeline development, failure handling and retry systems (if required), archival of raw data (if required), testing and documentation.
- Data Transformation Stage: Here systems and pipelines are developed to bring data into the data warehouses and to make it available to the end users. Some key actions at this stage include: Data cleaning, Data standardization and transformation, data warehouse design and development, ELT/ETL pipeline development, data governance and management system implementation, failure handling and retry systems (if required), archival of processed data (if required), testing and documentation.
- Automation Stage: The goal of this stage is to automate the pipeline by making use of containerization and virtualization.
- Quality Control Stage: Data and pipelines from end-to-end is intensively tested by creating test cases to verify and validate the entire data sets. This serves to deliver a high-quality data architecture.
- User Acceptance Test (UAT) Stage: Project outcomes are shared with key stakeholders and proper sign offs are taken to confirm completeness and correctness.
- Deployment Stage: Last but not least: Deployment to production. In this stage the pipeline goes live.
How does data engineering differ from data science and data analytics?
Data engineering, data science, and data analytics are all critical components of data management. While data engineering, data science, and data analytics all deal with data management, they each have distinct roles and responsibilities within an organization.
Data engineering focuses on building and maintaining the infrastructure for data storage and processing. In contrast, data science aims at extracting insights from data and creating predictive models. Data analytics, on the other hand, focuses on using data to drive business decisions and optimize operations (e.g., providing management dashboards, optimizing return-on-advertising-spend (ROAS) for digital marketing campaigns). Simply put, the performance of the Data Scientist’s and sometimes also Data Analyst’s output is directly dependent on the performance of the Data Engineer’s output.
Data engineers typically have a strong background in computer science, programming, and database management. Data science is about the use of statistical and computational methods to extract insights and knowledge from data. Therefore, data scientists typically have a strong background in mathematics, statistics, and programming, as well as expertise in specific domain areas. Data analysts on the other hand typically have a strong background in statistics, data visualization, and business intelligence. If focused on web and app they also have a strong background in web and app analytics tooling (e.g., as Google Analytics 4, Mapp, Adobe Analytics) and visualization tooling (e.g., Looker Studio, Tableau, PowerBI).
What are common tools and technologies used in data engineering?
Data engineering is a complex and rapidly evolving field that requires specialized tools and technologies to build and maintain data pipelines, warehouses, and lakes. Here are some of the most common tools and technologies used by data engineers:
- Apache Hadoop: Hadoop is a popular open-source software framework that is widely used for distributed storage and processing of large data sets.
- Apache Spark: Spark is a fast and flexible data processing engine that is often used in conjunction with Hadoop to perform data analysis and machine learning tasks.
- Apache Kafka: Kafka is a distributed messaging system that is used for real-time data streaming and processing.
- SQL Databases: Structured Query Language (SQL) databases like MySQL, PostgreSQL, and Oracle are commonly used for storing and querying structured data.
- NoSQL Databases: NoSQL databases like MongoDB, Cassandra, and DynamoDB are often used for storing and querying unstructured or semi-structured data.
- Cloud Platforms: Cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) provide scalable and cost-effective infrastructure for data storage and processing.
- ETL Tools: Extract, Transform, Load (ETL) tools like Talend, Informatica, and Apache NiFi enable to automate the process of extracting data from various sources, transforming it into a usable format, and loading it into a data warehouse or lake.
- Workflow Managers: Workflow managers like Apache Airflow, Luigi, and Oozie are used to schedule and manage complex data processing workflows.
- Version Control Systems: Version control systems like Git and Subversion are used to manage code and configuration changes in data engineering projects.
- Monitoring Tools: Tools like Prometheus, Grafana, and ELK stack monitor the health and performance of data pipelines, data warehouses, and data lakes.
In conclusion, data engineering requires a wide range of specialized tools and technologies with the goal to manage data effectively.
What are the benefits of data engineering for businesses?
Data engineering provides businesses with the infrastructure and tools necessary to store, process, and manage data efficiently. This helps businesses to make better decisions based on accurate and reliable data, improve their operations, and enhance the customer experience by providing new products and services. With the rise and broad adoption of artificial intelligence in society and work data engineering is becoming increasingly important as it completes the data operations process.