What is a data pipeline? Explanation and examples for SMEs

Key Takeaways: A data pipeline is an automated process that pulls data from one or more sources, cleans and transforms it along the way, and writes it to a destination such as a data warehouse, dashboard or AI model. It replaces manual exporting, copying and merging with a reliable, repeatable flow. For SMEs, a data pipeline is the link that connects separate systems (CRM, accounting, webshop) into a coherent picture. This article explains how a pipeline works, the difference between ETL and ELT, when you need one, whether to build it yourself or use a tool, and what it costs.

How does a data pipeline work?

A data pipeline works in three main steps: extracting, processing and storing. In the first step, the pipeline pulls data from sources such as a CRM, accounting package, webshop or database. This happens via API connections or direct database connections, on a fixed schedule (for example every hour) or continuously in real time.

In the second step, the data is processed. Date formats are aligned, duplicate records removed, company names standardised and missing fields filled in or flagged. This transformation step largely determines the quality of the end result; raw source data is almost never directly usable.

In the final step, the cleaned data lands in a destination, usually a central data warehouse such as Google BigQuery or Snowflake. From there, dashboards, reports or AI models feed on reliable data. Good pipelines also include monitoring that alerts you the moment a source fails or the data deviates. If you want to understand more deeply who builds and manages this, read what data engineering actually involves.

ETL vs. ELT: two variants

ETL and ELT describe the order in which the steps take place. With ETL (Extract, Transform, Load), data is first transformed and only then loaded into the data warehouse. With ELT (Extract, Load, Transform), the raw data is loaded first and then transformed within the warehouse itself.

ELT has become more popular in recent years, because modern cloud data warehouses can handle the transformations themselves. The advantage is flexibility: you keep the raw data and can apply new transformations later without re-extracting. ETL remains useful when you want to clean or anonymise sensitive data before it lands somewhere central.

For SMEs, the choice is usually not ideological but practical. Which tools you use and how much data you process determines the approach. You can find a detailed overview of the cost side in our article on ETL pipeline costs for SMEs.

Why does an SME need a data pipeline?

The core of the problem is fragmentation. In a typical SME, customer data sits in the CRM, financial data in the accounting package, sales data in the webshop and operational data in an ERP or spreadsheet. Each system has its own truth, and nobody has a complete picture without hours of manual work.

A data pipeline solves this by automatically connecting those sources and merging them into a consistent, up-to-date dataset. Reports are always correct, decisions are based on facts instead of gut feeling, and your people spend their time on analysis instead of copying and pasting. You can read more about this in our explanation of connecting systems and data integration.

In addition, a pipeline is the precondition for further steps. Reliable AI applications, predictive models and automated dashboards all require a stable data flow underneath. Without a pipeline, every follow-up project keeps getting stuck on messy, incomplete data. We describe that foundation in detail in what a data foundation is.

Build it yourself or use a tool?

For most SMEs, a combination is the smartest approach. For extracting data from standard sources, there are off-the-shelf tools such as Fivetran or the open-source Airbyte, which offer hundreds of connectors. These take the maintenance of API connections off your hands, which saves considerable work when a source changes its API.

Building yourself pays off mainly at the transformation layer and for custom sources. A tool like dbt lets you capture transformations in a manageable and testable way, while an orchestration tool such as Apache Airflow runs the steps at the right moment. Building everything from scratch is rarely worth it; you would be rebuilding functionality that already exists in mature form elsewhere.

The rule of thumb: use existing tools where your situation is standard, and build custom where your business is unique. A good partner helps you determine that dividing line without saddling you with unnecessary licence costs or superfluous complexity.

What does a data pipeline cost?

A simple data pipeline that connects two to four sources to a data warehouse typically costs 5,000 to 15,000 euros to build, with a lead time of four to eight weeks. The monthly cloud and tooling costs afterwards usually range between 100 and 500 euros, depending on data volume and whether you use paid connectors.

As the number of sources, the data volume and the transformation complexity increase, the build investment rises to 15,000 to 40,000 euros or more. A significant part of that, often 30 to 40 percent, goes into cleaning and standardising source data. That is not a setback but a fixed part of every data project; investing in data quality pays off directly in reliability.

Development costs of a data pipeline often qualify as technical development, which means the WBSO subsidy can cover part of the labour costs. That lowers the threshold considerably, especially for a first project.

Stratalytic and data pipelines

• We build custom data pipelines for SMEs, from a first connection to a fully automated data platform.
• We deliberately choose between off-the-shelf tools and custom work, so you do not pay for unnecessary complexity or licences.
• Data quality and monitoring are built in as standard, so your reports and AI applications run on reliable data.
• We advise on the WBSO and other subsidies, so a large part of the development costs can be covered.
• You retain ownership of your pipeline and data; no vendor lock-in, but a foundation that grows with you.

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Frequently asked questions

What is a data pipeline in simple terms?

A data pipeline is an automated process that pulls data from a source, cleans and transforms it, and then writes it to a destination such as a data warehouse or dashboard. Think of it as an assembly line that processes raw data step by step into usable information, without anyone having to copy or paste manually.

What is the difference between a data pipeline and ETL?

ETL (Extract, Transform, Load) is a specific type of data pipeline. Every ETL flow is a data pipeline, but not every pipeline follows the classic ETL order. Some pipelines load first and transform afterwards (ELT), or process data in real time. Data pipeline is therefore the umbrella term; ETL and ELT are two widely used variants of it.

Does a small business need a data pipeline?

As soon as you combine data from multiple systems (CRM, accounting, webshop), a data pipeline pays off. Exporting and merging manually takes time, introduces errors and scales poorly. A simple pipeline automates that work and ensures your reports are always correct. For businesses with one or two sources, it may not be necessary yet.

What does building a data pipeline cost for SMEs?

A simple data pipeline with a few sources typically costs 5,000 to 15,000 euros to build, plus 100 to 500 euros per month in cloud and tooling costs. More complex pipelines with many sources and transformations run up to 40,000 euros or more. Subsidies such as the WBSO can cover part of the development costs.