What is Data Engineering? - Explanation & Meaning

Definition

Data engineering is the discipline focused on designing, building, and maintaining systems and infrastructure for collecting, storing, processing, and making data available at scale. Data engineers build the foundations on which data analysis and machine learning become possible.

Technical explanation

Data engineering encompasses building data pipelines that extract data from sources (databases, APIs, files), transform it, and load it into target systems. Traditionally ETL (Extract, Transform, Load) was used, but the modern data stack shifts toward ELT (Extract, Load, Transform) where raw data is first loaded into a data warehouse and transformed there. Tools like Apache Airflow, Dagster, and Prefect orchestrate complex workflows. Streaming pipelines with Apache Kafka or Apache Flink process data in real-time. The modern data stack consists of components such as Fivetran or Airbyte for data ingestion, Snowflake or BigQuery as cloud data warehouse, dbt for transformations, and tools like Great Expectations for data quality. Data modeling with dimensional models or Data Vault 2.0 structures data for efficient analysis. Observability tools monitor pipeline health, data freshness, and schema changes. DataOps applies DevOps principles to data workflows with version control, CI/CD, and automated testing.

How MG Software applies this

MG Software helps organizations set up scalable data infrastructure. We build data pipelines that integrate data from diverse sources, transform it, and make it available for analysis and decision-making. Whether it is a simple ETL pipeline or a comprehensive real-time data architecture, we design solutions that grow with our clients' needs.

Practical examples

• A retail company building a data pipeline that combines sales data from 50+ stores, webshop events, and CRM data into a central data warehouse for unified reporting.
• A logistics company setting up a streaming pipeline with Apache Kafka that processes GPS data from trucks in real-time for route optimization and delivery predictions.
• A marketing agency building a self-service analytics platform with dbt and Snowflake where analysts can write their own queries on structured, reliable datasets.

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