Data pipeline
A data pipeline is a set of automated processes that moves data from one or more sources to a destination, applying the cleaning and transformation needed along the way. It removes manual exports and copy-pasting, and gives teams reliable, up-to-date data.
What is a data pipeline?
Most companies store data in many places: a CRM, a website, an advertising platform, a billing tool, a spreadsheet. A data pipeline connects these systems. It extracts records from the sources, reshapes them into a consistent format and loads them where they are needed, for instance a database, a data warehouse or a reporting dashboard.
The goal is trust and speed: the same figure should mean the same thing everywhere, and it should arrive without someone rebuilding a spreadsheet each week.
The main stages of a pipeline
- Ingestion: data is pulled from sources through APIs, webhooks, file exports or database connections.
- Transformation: records are cleaned, deduplicated, validated, enriched and mapped to a common schema.
- Loading: the result is written to the destination.
- Orchestration and monitoring: scheduling, retries, alerts and logs keep the flow reliable.
A minimal example in pseudo-code:
fetch(crm_api) -> remove_duplicates() -> convert_currency() -> insert(database)
ETL vs. ELT
| Criterion | ETL | ELT |
|---|---|---|
| Order | Extract, transform, then load | Extract, load, then transform |
| Where transformation runs | In a separate processing layer | Inside the destination database |
| Best for | Strict validation before storage | Large volumes and flexible analysis |
Batch vs. streaming
A batch pipeline processes data at intervals, for example every night, which is simple and cost-effective for reporting. A streaming pipeline processes events continuously, which suits use cases such as fraud alerts, live inventory or real-time personalisation. Many teams start with batch and move to streaming only where latency really matters.
Best practices
- Make steps idempotent: running a step twice should not create duplicates.
- Validate early: reject or flag malformed records at ingestion.
- Monitor and alert: a silent failure is worse than a loud one.
- Document the schema: know what each field means and where it comes from.
- Respect privacy rules: limit personal data and apply retention policies.
Data pipeline at BeBranded
Our team designs pipelines that fit your stack, from simple no-code flows between your CRM, website and database to more robust architectures built on APIs and webhooks. We focus on reliability, clear ownership and measurable time saved. Explore our automation service.
FAQ
What is a data pipeline?
What is the difference between ETL and ELT?
What is the difference between batch and streaming pipelines?
Do I need code to build a data pipeline?
How is a data pipeline different from a workflow automation?
How do I keep a pipeline reliable?
