Managed data
What is Data as a Service (DaaS) and why it matters
A clear definition of Data as a Service (DaaS): what it includes, how it differs from building your own infrastructure and why more companies adopt it.
Read articleKey takeaways
- A data pipeline automates the movement and transformation of data from source to destination.
- Its phases are ingestion, transformation, validation and loading.
- It can run in batch or near real time (streaming).
- Its reliability directly determines the reliability of the data the business receives.
- Maintaining it is the costly part, not building it.
Every time a dashboard updates itself or a report arrives on time without anyone preparing it, a data pipeline is working behind the scenes. It is one of the most important — and least visible — components of any modern data architecture.
What it is
A data pipeline is an automated sequence of steps that moves data from one or more sources to a destination, transforming it along the way, so it reaches the business in the right format, with the right quality, at the right time.
The phases
Ingestion
Capturefrom source
Transformation
CleanApply rules
Validation
Qualitychecks
Loading
Deliverto destination
- Ingestion: capturing data from the source.
- Transformation: cleaning, normalising, joining, applying business rules.
- Validation: quality checks before publishing.
- Loading: delivery to the destination (warehouse, lake, API or dashboard).
Batch vs. streaming
Pipelines can run in batches (at defined intervals) or in streaming (events as they happen, very low latency). Batch is simpler and cheaper and covers most reporting; streaming is needed when a decision depends on the moment’s data.
Why reliability is everything
A pipeline that fails silently is dangerous: the business keeps looking at a dashboard that no longer updates or shows incomplete data. That is why modern pipelines include monitoring, alerts and retries, and rely on data observability practices.
A pipeline that fails silently leaves the business deciding on stale or incomplete data.
In summary
A data pipeline automates moving and transforming data from source to destination through ingestion, transformation, validation and loading, in batch or streaming. Its reliability determines the data’s — and maintaining it (sources change, pipelines break) is the costly part a managed service takes on.
Sources & further reading
Frequently asked questions
What is the difference between a batch and a streaming pipeline?
Batch processes data at defined intervals and is simpler and cheaper; streaming processes events in near real time and is needed when immediacy matters.
Is a data pipeline the same as ETL?
ETL is a type of pipeline focused on extract, transform and load. "Pipeline" is broader and also includes streaming, validation and publishing.
What happens if a pipeline fails?
Without monitoring, it can fail silently and leave stale or incomplete data. Reliable pipelines include alerts, retries and quality checks.
What are the phases of a pipeline?
Ingestion, transformation, validation and loading — from capturing the data to delivering it to the destination.
What is the costly part?
Maintenance, not building: sources change format, volumes grow and edge cases appear. A managed service takes on that burden.
Where does the data end up?
In the destination the business needs: a warehouse, a lake, an API or a dashboard.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.