Data pipeline

What’s a data pipeline?

What we commonly call the data pipeline is a set of processes and technologies used to move data from one stage to another. It is a must-have tool for managing the data flow and data lifecycle. 

The data pipeline is a succession of several steps:

  • Data ingestion: data is collected from various sources. 
  • Data transformation: a series of operations are performed to process data and change its format to match the one required by the destination data repository.
  • Data storage: the transformed data is stored in a data repository, where it becomes ready for various analyses to be performed.

What purpose does it serve? Making the data actionable and accessible to data teams. It is a vital step to prepare data before starting analyzing it. As much as a time saver, it increases the data’s quality, reliability and consistency!

The components of a data pipeline

Data pipelines are made of several components: a source, a destination, and several processing steps in between. Let’s go into more detail: 

  • Data sources are basically where the data is extracted from: these can be databases, but also applications, APIs, sensors, or even social media feeds. 
  • Data storage is – as its name suggests – the place where the data is stored: depending on the type, volume and intended use of said data, it can be a data lake, data warehouse, or a NoSQL database.
  • Processing tools are designed to transform, clean and enrich the data before it is loaded into the storage system; ETL tools are some of them.
  • Integration methods, finally, are the various ways we connect all these components. 

Keep in mind that, even if data is generated in a single source system or application, it can end up feeding several Data Pipelines – which can in turn have multiple pipelines or applications depending on their outputs. Besides, various Data Pipelines can have the same source and the same destination: in that case, the data pipeline is only used to alter the dataset. 

Types of data pipelines

The most common types of Data Pipelines include:

  • the Batch Data Pipeline, which processes data in batches and proves very useful when dealing with large volumes of data;
  • the Real-time Data Pipeline, which processes data as it is generated, without delay, and is particularly suitable for applications where near-instantaneous access to data is necessary;
  • the ETL Pipeline, where the data is extracted from a database, and then transformed before being loaded into the target database;
  • the ELT Pipeline, where the data is extracted, loaded, and then transformed .

All in all, choosing the right Data Pipeline is a key step if you want to become a data-driven company and improve your decision making!

Husprey Logo

Start using Husprey for free

Connect to your data or to our sample dataset and start writing your first report in less than 5 minutes.