In the previous chapters, we looked at the Extract, Transform, and Load stages as distinct operations. However, the real utility comes from linking these stages together into an automated sequence. This sequence, where data flows from one stage to the next, is what forms an ETL pipeline. Think of it not just as separate E, T, and L blocks, but as a connected system designed to move and refine data automatically.

The arrangement and execution order of these E, T, and L tasks (and potentially smaller sub-tasks within them) define the pipeline's workflow. A workflow is essentially the blueprint that dictates what happens and when it happens within the pipeline. In the simplest case, the workflow is linear: first extract, then transform, then load.

A fundamental aspect of any workflow is the concept of dependencies. A dependency means that one task must successfully complete before another task can begin. Imagine trying to load data into your data warehouse before you've even extracted it from the source, or trying to apply transformations to data that hasn't arrived yet. It simply wouldn't work.

The most basic dependency structure in an ETL pipeline is sequential:

Extract: Retrieve data from the source(s).
Transform: Clean, validate, and reshape the extracted data. This step depends on the successful completion of the Extract step.
Load: Insert the transformed data into the target system. This step depends on the successful completion of the Transform step.

We can visualize this simple linear workflow:

A simple pipeline workflow showing sequential dependencies. Data flows from Extract to Transform, and then from Transform to Load.

In this diagram, the arrows indicate both the flow of data and the dependency. The Transform task cannot start until Extract is done, and Load cannot start until Transform is done.

While many simple pipelines follow this strict linear path, real-world workflows can become more intricate. Consider a scenario where you extract customer data from one source and order data from another. You might perform some initial cleaning transformations on each dataset in parallel before combining (joining) them in a later transformation step.

Let's visualize a slightly more complex workflow:

A workflow with parallel transformation steps. Customer and Order data are extracted and cleaned independently before being joined and loaded.

In this example:

Clean Customers depends only on Extract Customers.
Clean Orders depends only on Extract Orders.
These two cleaning tasks can potentially run at the same time (in parallel) because they don't depend on each other.
However, the Join Customer & Order Data task depends on both Clean Customers and Clean Orders completing successfully.
Finally, Load to Warehouse depends on the Join Data step.

Understanding these workflows and dependencies is significant for several reasons:

Correctness: It ensures that operations happen in the logical order required to produce the desired outcome. You need the ingredients (extracted data) before you can prepare the dish (transform), and the dish needs to be ready before you can serve it (load).
Error Handling: If a task fails, understanding dependencies helps determine which subsequent tasks should not be executed. If Extract Customers fails, there's no point attempting Clean Customers or Join Data.
Efficiency: Identifying tasks that can run in parallel (like Clean Customers and Clean Orders above) can significantly speed up the overall pipeline execution time.
Troubleshooting: When a pipeline fails, tracing the dependencies backward from the point of failure is often the first step in diagnosing the root cause.

As you start designing pipelines, even simple ones, always think about the sequence of operations and how each step relies on the previous ones. Mapping out this workflow and identifying the dependencies is a foundational step in building reliable and effective ETL processes.