Over the past few years, several characteristics of the ETL pipelines 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. The amount of accessible data is also growing annually by leaps and bounds.
Data engineers refer to the end-to-end route as etl data ‘pipelines’ where every pipeline has single or multiple sources and target systems to access and manipulate the available data.
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.
Let’s explore the ETL and data pipelines in-depth and the key differences between the two.
What is an ETL Pipeline?
ETL data pipeline is a set of processes that include extracting data from a source and transforming it. The data is subsequently loaded into the target ETL data warehouse or database for analysis or other purposes. This target destination could be a data warehouse, data mart, or database.
ETL is a process in the data warehouse. It stands for Extraction, Transformation, and Loading. As the name implies, the ETL process is used in data integration, data warehousing, and transforming 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, where the raw data is converted into a format that various applications can use. Lastly, the data accessible in a consistent format gets loaded into a target ETL data warehouse or some database.
The primary purpose behind building an ETL pipeline is to acquire the correct 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 essential business activities. ETL pipelines are built using different strategies depending on an enterprise’s unique requirements.
Use Case Examples of ETL Data Pipeline
There are various business scenarios where ETL data pipelines can be used to deliver faster, superior-quality decisions. ETL data pipelines are useful for centralizing all data sources, which helps the company view a consolidated version of the data. For instance, the CRM department can use an ETL data 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, there is often a need to move and transform data between multiple data stores internally. For example, it is hard for a business user to analyze and make sense of data scattered around different information systems.
What is a Data Pipeline?
A data pipeline refers to the 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 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 with data pipeline tools, a data pipeline can offer companies access to consistent and well-structured datasets for analysis. Data engineers can consolidate information from numerous sources and use it purposefully by systematizing data transfer and transformation. For example, an AWS data pipeline allows users to freely move data between 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 helpful for individuals who store and rely on multiple siloed data sources, require real-time data analysis, or have their data stored on the cloud. For example, data pipeline tools can perform predictive analysis to understand potential future trends. A production department can use predictive analytics to know when the raw material is likely to run out. Predictive analysis can also help forecast which supplier could cause delays. Using efficient data pipeline tools results in insights that 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 and ETL pipelines are responsible for moving data from one system to another; the key difference is in the application.
Understanding the terminology differences between ETL pipeline VS. data pipeline:
ETL pipeline includes a series of processes that extract data from a source, transform it, and load it into some output destination. On the other hand, a data pipeline is a somewhat broader terminology that 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. In contrast, the data is extracted, transformed, and loaded into a target system in an ETL pipeline. 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. This is done by accumulating, cleaning, and then transforming the data. Ultimately, the resulting data is 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 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 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 ongoing flow 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 loading data to a databank or a data warehouse. You can load data to any number of destination systems, such as 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, and loading, data pipeline tools 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 respond to a query systematically and correctly instead of going through diverse source data. A well-structured data pipeline and ETL pipeline improve the efficiency of data management. They also make it easier for data managers to quickly make iterations to meet the evolving data requirements of the business. View the demo or book a personalized demo with our technical team about your data pipeline use-case for free.