Data engineering is a software engineering approach to building data systems that enable the collection and usage of data. This data is typically used for subsequent analysis and data science, which often involves machine learning. Making data usable requires substantial computing, storage, and data processing. Data engineers design and maintain the infrastructure, pipelines, and tools that allow organizations to transform raw data into actionable insights.
The field emerged from the need to handle massive volumes, velocity, and variety of data, often referred to as big data. It combines principles from software engineering, database management, and distributed systems to create reliable, scalable, and cost-effective data platforms.
History
The roots of data engineering trace back to the 1970s and 1980s with the concept of information engineering methodology (IEM), which focused on database design and software for data analysis. Australian Clive Finkelstein, often called the "father" of IEM, wrote influential articles between 1976 and 1980 and co-authored a Savant Institute report with James Martin. Charles M. Richter later helped revamp IEM and design the user data software product from 1983 to 1987.
In the early 2000s, data and data tooling were typically managed by information technology (IT) teams, with limited overlap between business units. The early 2010s saw a dramatic shift with the rise of the internet and big data. Companies like Facebook and Airbnb began using the term "data engineer" to describe a new role focused on data infrastructure. Major firms such as Google, Facebook, Amazon, Apple, Microsoft, and Netflix moved away from traditional ETL and storage techniques, embracing cloud computing and a broader, more integrated approach to data across the organization.
Core Components
Compute
High-performance computing is essential for processing and analyzing data. Dataflow programming, where computation is represented as a directed graph of operations and data flows, is a widespread approach. Popular implementations include Apache Spark and TensorFlow, the latter being deep learning specific. More recent systems like Differential/Timely Dataflow use incremental computing for greater efficiency.
Storage
Data storage choices depend on how data will be used. Data engineers optimize storage and processing to reduce costs using compression, partitioning, and archiving.
- Databases: For structured data requiring online transaction processing, relational databases with ACID guarantees and SQL queries are common. NoSQL databases gained popularity in the 2010s for horizontal scaling, sacrificing ACID. NewSQL databases aim to provide both horizontal scaling and ACID guarantees.
- Data warehouses: For structured data needing online analytical processing, data warehouses support large-scale analysis, mining, and AI. Data often flows from databases into warehouses, accessible via SQL or business intelligence tools.
- Data lakes: Centralized repositories for storing, processing, and securing large volumes of structured, semi-structured, and unstructured data. They can be on-premises or cloud-based.
- Files: Less structured data may be stored as files in file systems, block storage, or object storage, the latter using metadata and keys like UUIDs.
Management
Workflow management systems like Airflow help specify, create, and monitor data tasks, often represented as directed acyclic graphs (DAGs).
Lifecycle
Business Planning
Business objectives are captured in strategic, tactical, and operational plans. Data engineering supports these plans by providing transparent data systems that enable feedback and early correction of miscommunication.
Systems Design
Designing data systems involves architecting data platforms and designing data stores, considering scalability, reliability, and cost.
Data Modeling
Data modeling produces an abstract model describing data and relationships, essential for structuring data for analysis and application.
Roles
Data Engineer
A data engineer is a software engineer who creates big data ETL pipelines to manage data flow through an organization. They build and maintain the infrastructure for data collection, transformation, and storage, enabling data scientists and analysts to derive insights. Data engineers work with tools like Apache Spark, TensorFlow, and cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud.
Data engineering is closely related to machine learning and artificial intelligence, as it provides the data foundation for training and deploying models. It also intersects with data science and business intelligence, ensuring that data is accessible, reliable, and secure.
Tools and Technologies
Data engineers use a variety of tools for compute, storage, and management. Popular compute frameworks include Apache Spark and TensorFlow. Storage solutions range from relational databases like PostgreSQL to NoSQL systems like MongoDB and cloud object storage like Amazon S3. Workflow orchestration tools like Apache Airflow manage complex pipelines. Cloud providers offer managed services such as AWS Trainium for specialized compute, and Azure and Google Cloud for scalable storage and processing.
Challenges and Best Practices
Data engineering faces challenges such as data quality, scalability, and cost management. Best practices include implementing robust data validation, using incremental processing, and designing for fault tolerance. Data governance and security are critical, especially with increasing data privacy regulations. As data volumes continue to grow, data engineering remains a dynamic field, evolving with new technologies and methodologies.