What is an ETL data pipeline?

In ETL, we talk a lot about building the ‘data pipeline’. A data pipeline is essentially a series of steps used to process a data set. It consists of a data source, one or more processing steps, and a destination.

Data is input at the source, moved, usually transformed, and then loaded into the destination. Some steps may occur in parallel, if the pipeline is amenable, to make it faster and more efficient.

So, how do you create a data pipeline? And is this the same as an ETL pipeline? Let’s take a look.

How do I make an ETL pipeline?

Let’s consider an ETL pipeline example that combines analytics data from different YouTube channels. You can implement an ETL data pipeline using Python, which is an excellent programming language for this purpose. In fact, Python ETL projects serve as useful learning exercises when it comes to building data pipelines.

The following steps can be used to build the pipeline required:

Extract:

1. Obtain the necessary credentials for YouTube Channel Reports
2. Access all of the video information from each channel
3. Use this video information to access metrics, such as views and ad revenue

Transform:

1. Join your video metadata to your metrics
2. Standardize the data, such as adjusting time zones and converting currencies
3. Merge the data into a final report

Load:

1. Update the data warehouse with the report from the transform phase
2. Update the data warehouse with the video metadata
3. Verify that the dataset has been loaded correctly

Is ETL the same as a data pipeline?

Sometimes the terms ‘ETL’ and ‘data pipeline’ are used interchangeably, but they are subtly different. So, is there such a thing as an ‘ETL pipeline vs. a data pipeline’?

ETL specifically means a set of processes which extract data sets from sources, transform the data, then load the completed dataset into a target system. A data pipeline is a more general term meaning any process that moves data from one system into another, and which may not include transformation.

What’s more, a data pipeline usually refers to a proven production-ready procedure that’s reliable and secure, and so has repeat usage, whereas an ETL pipeline is designed to be a bespoke process that elicits a major change in the nature of the data.

An analogy would be to compare a corporate delivery firm with a local moving company: A data pipeline is like a global courier with repeatable processes, monitoring, and scale, whereas ETL is like a moving truck used for a one-off transportation between premises.

What is ETL architecture?

Essentially, your ETL architecture is a step-by-step plan laying out how your ETL process works from the first stage to the last. This includes the methods used within the ETL project to move data from the source and to the target, and the rules applied at transformation.

It should also detail the programming languages used, such as SQL and Python, as well as file formats, such as CSV, JSON, and spreadsheets. The more detailed your ETL architecture, the better.

There’s also the question of ETL vs. ELT. Whereas ETL requires an intermediate location with processing capabilities to transform the data, ELT applies these transformations in the target destination itself. ELT, however, is a more modern and indeed mainstream concept which has become feasible thanks to the capabilities of cloud-based data warehouses to transform data as needed.

Whether you choose ETL or ELT, there are some common best practices that you should consider:

• Minimize the volume of data extracted by stripping out any unnecessary records, such as duplicate entries, as early as possible.
• Likewise, restrict databases to just the data you require to increase performance.
• It’s also important to maximize data quality – after all, GIGO (Garbage In, Garbage Out) – by cleaning data to reduce errors and inconsistencies.
• Finally, automation is your friend. Manually repeating operations is time-consuming and labor intensive, so it’s essential to automate as many processes as possible to make your ETL pipeline fast and efficient.