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Architecture

Data Pipelines

A data pipeline is a series of automated steps that move data from one or more sources to a destination, transforming it along the way.

Data pipelines are the backbone of data engineering. They connect source systems — like application databases, third-party APIs, or event streams — to destinations such as data warehouses or lakes, applying transformation and validation logic in between.

Pipelines can run in batch, processing data on a schedule such as hourly or daily, or in streaming mode, processing events as they happen in near real time. Most organizations use a mix of both, depending on how fresh the data needs to be.

A well-designed pipeline is idempotent, observable, and resilient to failure — meaning it can be safely re-run, its status is easy to monitor, and it recovers gracefully from partial failures.

Pipeline Stages

The Five Stages of a Pipeline

Most pipelines share a common shape, regardless of the tools used.

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Ingestion

Pulling data from source systems into the pipeline.

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Storage

Landing raw data in object storage or a staging schema.

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Transformation

Cleaning, joining, and modeling data for downstream use.

Why It Matters

Traits of a Reliable Pipeline

What separates a fragile pipeline from a production-grade one.

  • Idempotent runs that don't create duplicate data
  • Clear logging and monitoring for every stage
  • Automatic retries for transient failures
  • Data quality checks before publishing results
  • Documented ownership and on-call response plans
FAQ

Data Pipelines — Common Questions

Quick answers to frequent questions on this topic.

What's the difference between batch and streaming pipelines? +
Batch pipelines process data in scheduled chunks (e.g., every hour), while streaming pipelines process events continuously as they arrive.
What is orchestration in a data pipeline? +
Orchestration is the coordination of tasks — deciding what runs when, handling dependencies, and retrying failures — often managed by tools like Airflow.
Do small teams need complex pipelines? +
Not always. Many teams start with simple scheduled scripts and adopt more sophisticated orchestration as data volume and complexity grow.
Keep Learning

Related Guides

Continue building context around this topic.

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ETL & ELT

Understand the transformation patterns used inside pipelines.

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Cloud Data

See how cloud storage and compute support modern pipelines.

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Data Quality Checks

Learn how to validate data as it flows through your pipeline.