ETL pipeline

An ETL pipeline is a system that extracts, transforms, and loads (hence ETL) data from one or more data sources into a data destination such as a data warehouse, data lake, database, or other storage system such as Amazon’s S3. ETL pipelines are fundamental to data engineering and analytics, enabling organizations to collect, clean, and consolidate data for storage, analysis, and decision-making.

The acronym ETL stands for Extract, Transform, and Load and refers to the three stages of the data pipeline process.

The three stages include the following processes:

• Extract: The first step of the ETL pipeline is to pull data from a database such as an SQL or NoSQL database, an API, or a cloud platform that holds data such as a CRM.
• Transform: The second step is to transform the data into a more usable format or structure. Transforming refers to the process of converting the structure and format of the original data to fit the target repository.
• Load: The third and final step is the process of placing the transformed data into a target repository such as a database, a data warehouse, or a cloud-based data warehouse.

It’s important to note that while ETL processes have been critically important, recently more modern systems have an ELT structure: they extract data from a source, load it into a destination, and then either transform it, or allow data scientists or engineers or even business analysts to transform it at a later date.

Transforming the data later means you capture and store all the raw data, preserving flexibility for future analytics needs by not throwing any of it away.

ETL pipelines enable the process of converting raw data to match a target system in a systematic and accurate process.

With the raw data loaded into the target repository, it can then be used for other purposes such as data analysis to drive business decisions. With an functioning ETL pipeline in place, data teams can save time and create a more efficient process of transferring data from one system to another. 

A few common use cases of ETL pipelines include:

• Centralized data from various sources into a single data lake
• Pulling data from APIs to a database
• Extracting data from flat files and loading them into a data destination
• Taking data from one system and enriching it with data from another
• Migrating data from a legacy system to a new data repository
• Transforming and cleaning data so it can be used for data analysis and insights

In short, ETL pipelines help organizations consolidate data silos, creating a single source of truth for the company. By feeding BI tools and data visualizations, they are a key part of building a more data-driven organization.

As a leader in marketing analytics, Singular provides a marketing ETL pipeline that clients use to capture and automatically transfer data to their own databases, or a data warehouse without a single line of code.

With Singular’s marketing ETL, your data team saves time by transferring all your aggregate and user-level data automatically through one single process.

Here’s an example of how Singular’s ETL pipeline can help BI analysts from our guide on the subject:

You can create a replica of your marketing data in your own data warehouse. You can directly connect Tableau to that data. You can build custom reports or dashboards on top of it. You join sensitive or proprietary data to your marketing data within your safe and secure internal infrastructure.

By cutting costs and providing a stable pipeline that requires essentially zero maintenance, Singular’s ETL Pipeline eliminates the expense and wasted human resources required to manage or build your own data pipelines.

In short, with this increased efficiency, you’re able to remove manual errors and make faster and more accurate data-driven decisions.