What is Data Pipeline?
Data Pipeline — A set of automated processes that extract, transform, and load data from one system to another.
A data pipeline automates the flow of data from source systems through transformation stages to final destinations — databases, data warehouses, or AI training datasets. Reliable pipelines are the foundation of production AI because models are only as good as the data they receive.
Frequently Asked Questions
What does a typical AI data pipeline look like?
Extract data from sources (databases, APIs, files), clean and transform it, compute features, validate quality, store in a feature store or training dataset, and trigger model training or inference.
What tools are used for data pipelines?
Apache Airflow, Prefect, Dagster, and dbt for orchestration and transformation. Apache Kafka and AWS Kinesis for real-time streaming. Spark for large-scale processing.
How do I know if my data pipeline is healthy?
Monitor data freshness, completeness, schema consistency, and volume. Set up alerts for anomalies. Data observability tools like Monte Carlo and Great Expectations automate this monitoring.