A data pipeline is a series of processes that move data from one or more sources to one or more destinations, applying transformations along the way. Pipelines range in complexity from a simple extract-and-load script to multi-stage workflows that clean, validate, enrich, and route data across systems.
Data pipelines exist because raw data rarely lives where it needs to be, in the format it needs to be in. Customer events arrive from APIs, sensor readings land in object storage, and transactional records sit in operational databases. Turning that raw data into something useful (a dashboard, a machine learning model, an analytics table) requires moving and transforming it through a defined sequence of steps.
Most data pipelines share a common set of building blocks:
Data pipelines serve a wide range of use cases across data engineering, analytics, and AI:
Data pipelines are commonly categorized by how they process data. These categories aren’t mutually exclusive; a single pipeline can combine batch and event-driven stages.
Batch pipelines process data in discrete chunks. A batch pipeline might run every hour to pull new records from a database, transform them, and load them into a warehouse.
Batch pipelines don’t have to run on fixed schedules. Modern orchestration tools support event-driven batch processing, where a pipeline runs in response to an external event such as a message in a queue, the arrival of a new file, or an update to an upstream dataset. The pipeline still processes data in a batch, but the trigger is an event rather than a clock.
Batch processing works well when:
Stream processing handles data continuously as individual records or small micro-batches arrive. Unlike batch processing, where data accumulates before being processed, stream processing systems ingest and act on each event as it occurs.
Stream processing works well when:
Batch and stream processing are not an either-or choice. Many architectures use stream processing for low-latency needs and batch processing for heavier transformations, aggregations, or historical reprocessing, sometimes within the same pipeline.
Start with a minimal pipeline that handles the core data flow, then add complexity as requirements become clearer. Trying to account for every edge case upfront leads to over-engineered pipelines that are harder to debug and maintain.
Break pipelines into discrete, reusable steps rather than writing monolithic scripts. Modular steps are easier to test individually, debug when they fail, and reuse across different pipelines.
Each step in a pipeline should declare what it depends on. Explicit dependencies ensure steps run in the correct order and that failures in upstream steps prevent downstream steps from running on bad data.
Track pipeline runs, execution times, and data quality metrics. Set up alerts for failures, unexpected delays, and data anomalies. A pipeline that fails silently causes more damage than one that fails loudly.
Treat pipeline definitions as code. Store them in version control, review changes through pull requests, and maintain a history of what changed and why. This is especially important for pipelines defined programmatically rather than through a graphical interface.
AI-assisted tools can speed up pipeline development by generating boilerplate code, suggesting operators and connections, and helping debug failures. Tools like the Astro IDE, and Astronomer’s open source AI Agent tooling, which can be used with any AI you choose, provide context-aware code generation trained on Airflow best practices, so the generated code follows your project’s patterns and is aware of your existing connections and configurations.
Apache Airflow is an open source platform for building and orchestrating data pipelines as code. In Airflow, a data pipeline is defined as a Dag.
Airflow is well-suited for data pipeline orchestration because:
Astro is a managed platform for running Airflow in production. Astro handles infrastructure management, provides Astro Observe for monitoring pipeline health and observability, and includes the Astro IDE for AI-assisted Dag development.
To get started with Airflow, see Introduction to Apache Airflow. To learn how Airflow represents pipelines as Dags, see Introduction to Dags.