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 single or multiple sources 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?
Data ETL Pipeline is described as a set of processes that involve the extraction of data from a source, its transformation, and then loading into the target ETL data warehouse or database for data analysis or any other purpose. This target destination could be a data warehouse, data mart, or a database.
ETL is a process in the data warehouse. ETL stands for Extraction, Transformation, and Loading. As the name implies, the ETL process is used in data integration, data warehousing, and to 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 raw 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 target ETL data warehouse or some database.
The aim or main purpose behind building an ETL Pipeline is to acquire the right data, prepare it for reporting, and save it for quick, easy access and analysis. An ETL tool helps business-users and developers free-up their time and focus on other important business activities. There are different strategies used to build ETL pipelines depending on an enterprise’s unique requirements.
Use Case Examples of ETL Pipeline
There are various business scenarios where ETL pipelines can be used to deliver faster, superior-quality decisions. Data ETL pipelines are useful for centralizing all data sources, which help the company view a consolidated version of the data. For instance, CRM department can use an ETL pipeline to pull customers’ data from multiple touchpoints in the customer journey. This can further allow the department to create detailed dashboards that can act as a single source for all customer information from different platforms. Similarly, a lot of times there is a need to internally move and transform data between multiple data stores. For example, if data is scattered around different information systems, then it is hard for a business user to analyze it and make sense of it.
What is 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. For example, an AWS data pipeline allows users to freely move data from between different AWS on-premises data and other storage resources.
Use Case Example of Data Pipeline
Data pipelines are helpful for accurately fetching and analyzing data insights. The technology is useful for individuals who store and rely on multiple, huge amounts of siloed data sources, requre real-time data analysis and have their data stored on the cloud. For example, data pipelines are used to perform predictive analysis to understand the most likely future trends. A production department can use predictive analytics to know when the raw material is likely to run out and it could also help forecast which supplier could cause delays. The insights can help the production department streamline its operations.
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 pipelines, as well as ETL pipelines, are both responsible for moving data from one system to another; the key difference is in the application for which the pipeline is designed.
- Understanding the terminology differences between ETL pipeline VS. data pipeline: 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.
- Purpose of ETL pipeline VS data pipeline: Precisely, the purpose of a data pipeline is to transfer data from sources, such as business processes, event tracking systems, and data banks, 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 ETL data warehouse.
- Differences in how ETL and data pipeline run: 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 ETL tools are used for data extraction, transformation as well as loading, the latter may or may not include data transformation.
Both methodologies have their pros and cons. Shifting data from one place to another means that various operators can query more systematically and correctly, instead of going through diverse source data. 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.