Data Engineering

TL;DR

Prepares data from multiple sources so it is cleaned, organized, and structured for analysis.
Common techniques include ETL (extract, transform, load) and data mining.
Results are stored in a central repository (e.g., a data warehouse) for querying and visualization.

Definition

Data engineering is the process of extracting, transforming, and loading data from various sources into a central repository, such as a data warehouse, for further analysis and reporting. This process uses various techniques and tools to ensure the data is cleaned, organized, and structured to enable effective analysis and decision making.

Explanation

Data engineering involves acquiring data from multiple origins and preparing it so analysts and data scientists can derive insights. The work typically includes cleaning and transforming raw inputs — for example, by filtering, aggregating, and converting data types — and loading the processed data into a central repository. Once loaded, the data can be queried and analyzed using tools like SQL or visualization software. Data engineering also overlaps with analytical techniques such as data mining, where algorithms and statistical methods are applied to reveal patterns and insights in large datasets; these techniques often use machine learning algorithms and natural language processing.

Examples

ETL (Extract, Transform, Load)

In the ETL process, data is extracted from various sources, such as transactional databases, flat files, or web APIs. This data is then transformed and cleaned using techniques such as filtering, aggregation, and data type conversion to ensure consistency and readiness for analysis. Finally, the transformed data is loaded into the data warehouse, where it can be queried and analyzed using tools such as SQL or visualization software.

Data mining

Data mining involves using algorithms and statistical methods to uncover hidden patterns and insights in large datasets. Data scientists and analysts use tools such as machine learning algorithms and natural language processing to extract valuable insights from the data. For example, a data scientist may use data mining techniques to identify trends in customer behavior, or to predict future demand for a product or service.

Use cases

Enabling organizations to make informed decisions based on prepared and accessible data.
Unlocking the value of organizational data to support analysis and strategic advantage.
Supporting reporting, analytics, and predictive tasks by supplying cleaned and structured datasets.

ETL (Extract, Transform, Load)
Data warehouse
Transactional databases
Flat files
Web APIs
SQL
Visualization software
Data mining
Algorithms and statistical methods
Machine learning algorithms
Natural language processing