Glossary · Hosting & DevOps
Data Pipeline
Overview
What a data pipeline is
A data pipeline is the connected sequence of steps that carries data from where it is created, such as apps, databases, APIs, or event streams, to where it is used, such as a data warehouse, analytics dashboard, or model. Each step can extract, transform, load, filter, or enrich the data.
Common patterns include ETL (extract, transform, load) and ELT (extract, load, transform), which differ mainly in whether data is transformed before or after landing in the destination.
How a data pipeline works
Data is ingested from sources, then passed through processing stages that clean, reshape, join, or aggregate it before being written to a destination. Pipelines can run as scheduled batches (for example, nightly) or as streaming flows that process events in near real time using tools like Apache Kafka.
Robust pipelines include orchestration, monitoring, error handling, and data-quality checks so failures are caught and data stays trustworthy. They are often built with a mix of managed services, warehouses like BigQuery, and integration tools such as webhooks or platforms like Segment.
Why data pipelines matter
Organizations rarely have all their data in one place. Pipelines make it usable by consolidating and standardizing data so teams can report on it, power products with it, and train models on it, without manual copying and cleanup.
Well-designed pipelines improve reliability and speed of decision-making, while poorly designed ones create silent data errors and stale dashboards. The right approach, batch versus streaming and ETL versus ELT, depends on data volume, freshness needs, and cost.
Where we use it
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FAQ
Data Pipeline — common questions
What is a data pipeline used for?
A data pipeline is used to automatically move and transform data from sources like apps and databases into destinations such as warehouses, dashboards, or machine learning models, so the data is clean, consistent, and ready to use.
What is the difference between ETL and ELT?
ETL transforms data before loading it into the destination, while ELT loads raw data first and transforms it inside the destination. ELT is common with modern cloud warehouses that can handle large-scale transformation.
What is the difference between batch and streaming pipelines?
Batch pipelines process data in scheduled chunks, such as hourly or nightly, while streaming pipelines process events continuously in near real time. The choice depends on how fresh the data needs to be and cost.
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