What is data engineering, and what are the typical steps involved?
💡 Model Answer
Data engineering is the discipline of designing, building, and maintaining the systems that collect, store, process, and make data available for analysis. The typical steps include: 1) Data ingestion – pulling raw data from sources such as databases, APIs, or streaming platforms. 2) Data transformation – cleaning, enriching, and converting data into a consistent format using ETL/ELT pipelines. 3) Data storage – choosing appropriate storage layers (raw lake, curated warehouse, or data marts) and technologies (e.g., Google BigQuery, Snowflake, Redshift). 4) Data orchestration – scheduling and monitoring workflows with tools like Airflow, Prefect, or Cloud Composer. 5) Data cataloging and governance – ensuring metadata, lineage, and security policies. 6) Data consumption – exposing data to analysts, data scientists, or downstream applications via BI tools or APIs. Google BigQuery is often used as a serverless data warehouse in this pipeline, providing fast SQL analytics on large datasets. The goal is to create reliable, scalable pipelines that deliver high‑quality data to stakeholders.
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