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!