What is a Data Pipeline? And How It Works?
What is a Data Pipeline?
A data pipeline is a sequence of actions that moves data from a source to another destination. Data pipelines can help you transfer data from one source, like your website, to a destination, like a data warehouse, for analysis and interpretation.
Today, more companies harness the power of data to create effective marketing strategies that help them stand out from their competitors and drive more revenue. Odds are, if you aren’t using data to inform your campaigns, your competitors most likely are.
Data pipeline helps you organize and manage essential data and information about your marketing strategies, customers, leads, and more.
Without a data pipeline, your data won’t be stored or organized in one central place. If you don’t keep your data in the same place, it’s challenging and time-consuming to analyze your data and identify trends and actionable insights.
When you use a data pipeline, you can seamlessly transfer your data between multiple sources and combine it in a central location so that you can analyze and interpret it later. You can then use your insights to inform your marketing strategies.
Benefits of a Data Pipeline
When companies don’t know about what is Data Pipeline, they used to manage their data in an unstructured and unreliable way. But as they came to know about What is Data Pipeline and how it helps marketing companies save time and keep their data organized always. A few benefits of Pipeline are listed below:
- Data Quality: The data flows from source to destination can be easily monitored and accessible, and meaningful to the end-users.
- Incremental Build: Pipelines allow users to create dataflows incrementally. You can pull even a small slice of data from the data source to the user.patterns in a wider architecture.
- Replicable Patterns: It can reused and repurposed for new data flows. They are a network of pipelines that creates a way of thinking that sees individual Pipelines as examples of patterns in a wider architecture.
Data pipeline components
To understand how the data pipeline works in general, let’s see what a pipeline usually consists of.
Origin
Origin is the point of data entry in a data pipeline. Data sources (transaction processing application, IoT devices, social media, APIs, or any public dataets) and storage systems (data warehouse, data lake, or data lakehouse) of a company’s reporting and analytical data environment can be an origin.
Destination
The final point to which data is transferred is called a destination. Destination depends on a use case: Data can sourced to power data visualization. And analytical tools or moved to storage like a data lake or a data warehouse. We’ll get back to the types of storage a bit later.
Dataflow
That’s the movement of digital data from origin to destination, including the changes it undergoes along the way as well as the data stores it goes through.
Storage
Storage refers to systems where data preserved at different stages as it moves through the pipeline. Data storage choices depend on various factors, for example, the volume of data, frequency and volume of queries to a storage system, uses of data, etc.
Processing
Processing includes activities and steps for ingesting data from sources, storing it, transforming it, and delivering it to a destination. While data processing is related to the dataflow, it focuses on how to implement this movement. For instance, one can ingest data by extracting it from source systems. Copying it from one database to another one (database replication), or by streaming data. We mention just three options, but there are more of them.
Workflow
The workflow defines a sequence of processes (tasks) and their dependence on each other in a data pipeline. Knowing several concepts – jobs, upstream, and downstream – would help you here. A job is a unit of work that performs a specified task – what done to data in this case. Upstream means a source from which data enters a pipeline, while downstream means a destination it goes to.
Monitoring
The goal of monitoring is to check how the data pipeline and its stages are working: Whether it maintains efficiency with growing data load. Whether data remains accurate and consistent as it goes through processing stages. Or whether no information lost along the way.