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 (ML) and modern data pipelines, 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 this 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. This process of moving data from a source to a destination is crucial in any type of data pipeline.
Within each pipeline, data goes through transformation, validation, normalization, and other processes. ETL pipelines and data pipelines can both involve streaming data and batch processing. A data pipeline can include ETL and any other activity or process that involves moving data from one place to another.
So what is the difference between an ETL pipeline and a data pipeline? Let’s explore data pipeline vs ETL in-depth and the key differences between the two.
What is an ETL Pipeline?
ETL stands for extract, transform, and load. So, by definition, an ETL pipeline is a set of processes that includes extracting data from a variety of sources and transforming it. The data is subsequently loaded into the target systems, such as a cloud data warehouse, data mart, or a database for analysis or other purposes.
During extraction, the system ingests data from various heterogeneous sources, such as business systems, applications, sensors, and databanks. The next stage involves transforming the raw data into a format required by the end application.
Lastly, the transformed data is loaded into a target data warehouse or database. Additionally, it can be published as an API to be shared with stakeholders.
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. ETL tools help business users, and developers free up their time and focus on other essential business activities. Enterprises can build ETL pipelines using different strategies based on their unique requirements.
The ETL pipelines are used in various data processes, such as:
Examples of ETL Pipeline
There are various business scenarios where ETL pipelines can be used to deliver faster, superior-quality decisions. ETL pipelines are useful for centralizing all data sources, which helps the company view a consolidated version of its data assets.
For instance, the 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, there is often a need to move and transform data between multiple data stores internally, as it is hard for a business user to analyze and make sense of data scattered around different information systems.
Benefits of an ETL Pipeline
Efficient Decision-Making: With an ETL pipeline in place, end-users can quickly access the data they need, enabling faster decision-making and reducing the time required for data preparation and processing.
Scalable Data Processing: ETL pipelines efficiently handle large volumes of data, allowing end-users to scale their data processing capabilities without sacrificing performance.
Improved Data Accessibility: ETL pipelines make data easily accessible to end-users by integrating and centralizing data from various sources, eliminating manual data retrieval and aggregation.
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.
Examples 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 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. Both pipelines are responsible for moving data from one system to another; the key difference is in the application.
ETL vs. Data Pipeline – Understanding the Difference
ETL pipeline includes a series of processes that extract data from a source, transform it, and load it into the destination system. On the other hand, a data pipeline is a somewhat broader terminology that includes an 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.
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 purpose of ETL is to extract, transform and load data into a target system.
The sequence is critical. After extracting data from the source, you must fit it into a data model generated according to your business intelligence requirements. This involves accumulating, cleaning, and transforming the data. Finally, you load the resulting data into your data warehouse.
How the Pipeline Runs
An ETL pipeline typically works in batch processing, which means that the data moves 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.
On the contrary, a data pipeline can also operate as a real-time process, managing each event as it occurs instead of processing in batches. During data streaming, it handles an ongoing flow that is suitable for data requiring continuous updating. For example, to transfer data collected from a sensor tracking traffic.
Moreover, the data pipeline doesn’t have to end with loading data into 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.
Data Pipeline vs ETL Pipeline: Which One Should You Choose?
It goes without saying that choosing between a data pipeline and ETL pipeline depends largely on your specific data integration needs. ETL pipelines, being the traditional choice for many businesses, are suited for scenarios where regular, scheduled updates are sufficient. On the other hand, a data pipeline is a more versatile solution, encompassing not only ETL but also real-time data streaming and orchestration. If you require agility and adaptability, especially in handling diverse data sources and dynamic processing needs, a data pipeline might be more suitable.
Here is how you can decide between ETL pipeline and data pipeline:
Consider the nature of your data and the requirements of your business processes. ETL pipelines are well-suited for scenarios where data can be processed in batches, making them efficient for handling large volumes of historical data. On the other hand, data pipelines are more versatile, accommodating real-time data streaming for use cases that demand immediate insights and actions based on the most recent data updates.
For example, if you’re dealing with financial transactions or monitoring social media trends in real-time, a data pipeline might be the preferred choice to ensure timely decision-making.
ETL pipelines can handle unstructured or semi-structured data through the transformation phase. This process involves cleaning, enriching, and structuring data for analysis and storage. On the other hand, simple data pipelines, designed for continuous streaming, are more suitable for homogeneous data sources where a consistent format is maintained. They efficiently manage the constant flow of data but may not provide the same level of intricate transformation capabilities as ETL pipelines for complex, varied data structures.
As far as complexity is concerned, ETL pipelines involve more upfront design and development effort compared to data pipelines, especially due to the data transformation process. However, these efforts are significantly reduced as modern ETL tools do most of the heavy lifting.
Tools and Ecosystem
Speaking of tools, the tooling and ecosystem also play a role in the decision-making process. ETL pipelines have a well-established set of tools and frameworks, often tightly integrated with data warehouses and traditional business intelligence systems. This makes them a reliable choice for organizations with legacy systems and a structured data environment.
On the other hand, data pipelines leverage a broader ecosystem, incorporating technologies like Apache Kafka, Apache Flink, or Apache Spark for real-time data processing. They align well with the growing trend of big data technologies and cloud-based solutions, providing scalability and flexibility in choosing tools that best fit specific use cases. Ultimately, the choice between a data pipeline and ETL pipeline depends on the nature of your data, processing requirements, and the level of flexibility and real-time capabilities your integration demands.
Data Pipeline vs ETL: Key Takeaway
Although used interchangeably, ETL and data pipelines are two different terms. ETL tools extract, transform, and load data, whereas data pipeline tools may or may not incorporate 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.
The important thing to remember is that you might not actually have to choose between implementing a data pipeline or an ETL pipeline as they can be used together strategically. In many real-world scenarios, it’s a matter of leveraging them together to meet specific business needs. For example, you might use ETL pipelines to handle structured, batch-oriented data with well-defined transformations. This could be particularly useful when dealing with historical data or scenarios where periodic updates are sufficient. Meanwhile, the broader data pipeline can handle real-time data streaming, orchestration, and other tasks that go beyond traditional ETL.
So, if you’re comparing different data integration tools to execute your ETL or data pipelines, give Astera a try! You can also sign up for a demo or talk to our sales representative to discuss your use-case for free.