ETL is a common term when it comes to data processing. ETL pipeline is an assembly of processes to extract data from a system, convert it, and load it onto another destination, such as a data warehouse or a specific database. This final process is done for data reporting, analysis, and synchronization. It is an acronym for extract, transform and load. To make things even smoother for your organization, you can outsource your requirements for big data ETL pipeline, data automation, tool, etc., to third-party service providers. They, with their highly-skilled teams, will help you out with your data systems.
However, you must have a fair understanding of these processes to integrate them into your systems and operations. The sections below will discuss the processes involved in the ETL pipeline and how it is different from the data pipeline. Without further delay, let us take a look at it.
What Is ETL?
As mentioned earlier, ETL stands for extract, transform and load. This process is automated that grabs on to raw data and extracts whatever are essential for analysis. Subsequently, it transforms the information in a format to serve your organization’s needs and then loads it further into a database. In other words, it reduces the size of your data to offer you better performance for different variants of analysis.
Whenever you plan to construct an ETL foundation, it will be best to amalgamate information from various sources. Following it, you must sketch out your plan and test accordingly so that you can transform data appropriately. The whole process is slightly complex and takes time to quite an extent. Let us browse through the three processes individually.
Extract
It is the first process in an ETL pipeline. In this phase, the raw information is extracted from different sources such as sensor data, marketing tools, business systems, transaction databases, and APIs. It is evident that a section of the data types is presumably structured outputs of the systems that are widely used, and the remaining are semi-structured server logs.
Transform
The next stage in the whole process is transforming the data to make it compatible with various applications. In simple terms, the information is changed in format, which you can use for your business requirements. After your data is extracted correctly, it converts information into a type you can use for standardized processing.
Load
Here comes the final stage. It is where you can load the data available in the format that you can use. It will help you compare any type of data with other varieties, and you can choose to update your database manually or set it off automatically. It will depend on the needs and requirements of the type of your data warehouse. As part of the process, the information is stored in a minimum of one staging table on a temporary basis.
What Is The Difference Between An ETL Pipeline And A Data Pipeline?
Although the two terms, namely ETL pipeline and data pipeline, can be used interchangeably, the difference lies in the intent of building the pipelines. Otherwise, they basically perform the same task of transferring information from one platform to the other while making it compatible with the latter. Let us now explore some of their significant differences.
- ETL Pipelines:
This set of processes is designed for data warehouse applications. It also acts as a solution for migration from old to new applications. When creating ETL pipelines, the tools used here adhere to industry standards that efficiently transform structured data. These systems are usually built either by business intelligence or data engineers.
- Data Pipelines:
These pipelines, on the other hand, are designed for applications that utilize information in bringing value. Data pipelines are useful for amalgamating information from various applications, creating data streaming applications in real-time, building data-driven products for the web, data mining activities, among many others. With the access of big data technology, its use has increased manifolds. It is the data engineers who contribute to building a data pipeline.
The Bottom Line
So, you see, the ETL pipeline is not that tough to understand, after all. It will help you in efficiently transferring your data from one platform to the other. We believe the above discussion has also helped you understand the fundamental difference between the ETL and data pipeline. We hope we have provided you with what you were looking for.