What is Data Engineering?

Data engineering builds robust data systems. It is a software engineering approach. These systems help collect and use crucial data. People use this data for analysis and data science. This often includes machine learning. Making data usable requires significant computing and storage. It also demands careful data processing.

The History of Data Systems

The term Information Engineering Methodology (IEM) appeared around the 1970s. It described database design. IEM also involved using software for data analysis. Database administrators (DBAs) used these techniques. Systems analysts also adopted them. They needed to understand organizational processing needs. Clive Finkelstein was a key contributor. Many call him the “father” of IEM. He co-authored an important report with James Martin. Finkelstein focused on a business-driven direction. Martin continued a data processing focus.

The Rise of the Data Engineer Role

IT teams generally held all data tools in the early 2000s. Other teams used the data for reporting. Data skillsets rarely overlapped across the business. The internet brought massive data increases in the 2010s. This included huge volume, speed, and variety. The term “big data” described this shift. Companies like Facebook started using “data engineer.” Traditional ETL methods no longer worked. Major firms moved away from old techniques. They created data engineering. This focused on infrastructure, warehousing, and security. Cloud computing drove much of this change. Data became important for sales and marketing teams too.

Processing and Storing Data

High-performance computing is vital for data analysis. Dataflow programming is a popular approach. This represents computation as a directed graph. Nodes are the operations. Edges show the data flow. Apache Spark is a popular example. TensorFlow is specific to deep learning. Newer systems use incremental computing. This makes data processing much more efficient.

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Data engineers optimize storage systems. They reduce costs through compression and partitioning. The intended usage dictates how you store the data. Structured data often requires online transaction processing (OLTP). In this case, people use databases. Relational databases were originally common. They guarantee ACID transaction correctness. They mainly use SQL queries. Data growth in the 2010s popularized NoSQL databases. They scale horizontally more easily. They trade ACID guarantees for scalability. Newer NewSQL databases try to maintain ACID guarantees. They still allow horizontal scaling.

Data Warehouses and Data Lakes

If you need online analytical processing (OLAP), use data warehouses. Data warehouses enable large-scale data analysis. This includes mining and AI. Data often moves from databases into warehouses. Analysts and data scientists access these warehouses. They use SQL or business intelligence tools. A data lake is a centralized storage spot. It handles huge volumes of secured data. A data lake holds structured and unstructured data. You can build data lakes on-premises. Many use cloud services from Amazon or Google. Unstructured data is often stored simply as files.

Workflow and Modeling

The number of data processes can overwhelm users. Workflow management systems help handle this complexity. Tools like Airflow specify and monitor data tasks. Tasks are often specified as a directed acyclic graph (DAG). Designing data systems involves several parts. This includes architecting platforms and designing stores. Data modeling represents data requirements. A data model organizes business concepts. It shows the relationships and constraints. These models guide communication. They also inform the final database design.

Data Engineer vs. Data Scientist

A data engineer is a software engineer. They build big data ETL pipelines. They manage the data flow organization-wide. This translates data into actionable insights. They focus on production readiness. They worry about formats, resilience, and security. Data engineers usually have a software engineering background. They know languages like Python or Java. They understand architecture and cloud computing.

Data scientists focus on analysis. They know mathematics and algorithms well. They are experts in statistics and machine learning. You can learn more about these roles when you Read Our Blog.

Reference: Inspired by content from https://en.wikipedia.org/wiki/Data_engineering.