Over the past few years, several characteristics of the data landscape have gone through gigantic alterations. Due to the emergence of novel technologies such as machine learning, the data management processes of enterprises are continuously progressing, and the amount of accessible data is growing annually by leaps and bounds.
When it comes to accessing and manipulating the available data, data engineers refer to the end-to-end route as ‘pipelines’, where every pipeline has a single or multiple source and target systems.
Within each pipeline, data goes through numerous stages of transformation, validation, normalization, or more. Two of these pipelines often confused are the ETL Pipeline and Data Pipeline.
What is an ETL Pipeline?
An ETL Pipeline is described as a set of processes that involve extraction of data from a source, its transformation, and then loading into target database. This target destination could be a data warehouse, data mart, or a database.
ETL is an acronym for Extraction, Transformation, and Loading. As the name implies, it is a three-step process used to integrate and transform data from disparate sources.
During Extraction, data is extracted from several heterogeneous sources. For example, business systems, applications, sensors, and databanks.
The next stage involves data transformation in which the data is converted into a format that can be used by various applications.
Lastly, the data which is accessible in a consistent format gets loaded into a data warehouse or some database.
What’s a Data Pipeline?
A data pipeline refers to the series of steps involved in moving data from the source system to the target system. These steps include copying data, transferring it from an onsite location into the cloud, and arranging it or combining it with other data sources. The main purpose of a data pipeline is to ensure that all these steps occur consistently to all data.
If managed astutely, a data pipeline can offer companies access to consistent and well-structured datasets for analysis. By systematizing data transfer and transformation, data engineers can consolidate information from numerous sources so that it can be used purposefully.
Difference between ETL Pipelines and Data Pipelines
Although ETL and Data pipelines are related, they are quite different from one another. However, people often use the two terms interchangeably. Data pipeline as well as ETL pipeline are both responsible for moving data from one system to another; the key difference is in the application for which the pipeline is designed.
ETL pipeline basically includes a series of processes that extract data from a source, transform it, and then load it into some output destination.
On the other hand, a data pipeline is a somewhat broader terminology which includes ETL pipeline as a subset. It includes a set of processing tools that transfer data from one system to another, however, the data may or may not be transformed.
Precisely, the purpose of a data pipeline is to transfer data from sources, such as business processes, event tracking systems, and databanks, into a data warehouse for business intelligence and analytics. Whereas, ETL pipeline is a particular kind of data pipeline in which data is extracted, transformed, and then loaded into a target system. The sequence is critical; after data extraction from the source, you must fit it into a data model that’s generated as per your business intelligence requirements by accumulating, cleaning, and then transforming the data. Ultimately, the resulting data is then loaded into your data warehouse.
Another difference between the two is that an ETL pipeline typically works in batches which means that the data is moved in one big chunk at a particular time to the destination system. For example, the pipeline can be run once every twelve hours. You can even organize the batches to run at a specific time daily when there’s low system traffic.
Contrarily, a data pipeline can also be run as a real-time process (such that every event is managed as it happens) instead of in batches. During data streaming, it is handled as an incessant flow which is suitable for data that requires continuous updating. For example, to transfer data collected from a sensor tracking traffic.
Moreover, the data pipeline doesn’t have to conclude in the loading of data to a databank or a data warehouse. And, it is possible to load data to any number of destination systems, for instance an Amazon Web Services bucket or a data lake. It can also initiate business processes by activating webhooks on other systems.
Although used interchangeably, ETL and Data Pipelines are two different terms. While the former involves data extraction, transformation as well as loading, the latter may or may not include data transformation.
Shifting data from one place to another means that various operators can query more systematically and correctly, instead of going through a range of diverse sources. A well-structured data pipeline and ETL pipeline not only improve the efficiency of data management, but also make it easier for data managers to quickly make iterations to meet the evolving data requirements of the business.