Introduction to data pipelines

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.

Components of a data pipeline

Most data pipelines share a common set of building blocks:

• Sources: Where data originates. Common sources include APIs, databases, message queues, flat files, and IoT sensors.
• Destinations: Where processed data lands. Destinations are typically data warehouses, data lakes, analytics platforms, or downstream applications.
• Transformations: The operations applied to data between source and destination. Transformations can include filtering, joining, aggregating, reformatting, or enriching data.
• Orchestration: The system that coordinates when and how each step runs, manages dependencies between steps, and handles failures. Without orchestration, pipeline steps run independently with no awareness of each other.
• Monitoring and alerting: The mechanisms that track pipeline health and notify teams when something fails or degrades. Monitoring is critical for catching issues before they propagate downstream.
• Storage: The intermediate and final systems where data is persisted at each stage. Storage choices affect pipeline performance, cost, and data retention.

Common use cases

Data pipelines serve a wide range of use cases across data engineering, analytics, and AI:

• ETL/ELT: Extract data from source systems, transform it, and load it into a warehouse or data lake for analytics. This is the most common data pipeline pattern.
• Data integration: Consolidate data from multiple sources into a single system, resolving format differences and deduplicating records along the way.
• MLOps: Automate the end-to-end machine learning lifecycle, from data preparation and feature engineering through model training, validation, and deployment.
• AI and LLM workflows: Orchestrate retrieval-augmented generation (RAG) pipelines, embedding generation, fine-tuning jobs, and inference workflows that coordinate calls to AI services with data processing steps.
• Reverse ETL: Push processed data from a warehouse back into operational systems like CRMs, marketing platforms, or product databases.

Types of data pipelines

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

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:

• Data doesn’t need to be available within milliseconds.
• The source system produces data in bulk or at known intervals.
• Transformations are compute-intensive and benefit from processing large volumes at once.
• External events can trigger pipeline runs when new data is available.

Stream processing

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:

• Data must be available within seconds or less of being produced.
• Records arrive continuously and unpredictably from sources like event streams, message queues, or log systems.
• Downstream consumers depend on near-real-time updates, such as fraud detection, live dashboards, or alerting systems.

Best practices

Build incrementally

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.

Make pipelines modular

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.

Define dependencies explicitly

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.

Monitor and alert

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.

Use version control

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.

Use AI to accelerate development

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.

Data pipelines and Apache Airflow

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:

• Pipelines as code: You define pipelines in Python, which means you can use loops, conditionals, and variables to build dynamic workflows. Pipeline definitions live in version control alongside the rest of your codebase.
• Dependency management: Airflow enforces task execution order based on the dependencies you define. If an upstream task fails, downstream tasks don’t run.
• Scheduling: Airflow includes a built-in scheduler that can run pipelines on cron-based schedules, event-driven triggers, or manual execution.
• Visibility: The Airflow UI provides a visual representation of your pipeline structure and execution history, making it straightforward to monitor runs and debug failures.
• Extensibility: Airflow integrates with a wide range of external systems through a library of pre-built operators and hooks for services like AWS, Google Cloud, Snowflake, Databricks, and more.

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.