Data Pipelines

What is a Data Pipeline?

Let’s start with the basics - what exactly is a data pipeline?

A data pipeline is a series of steps designed to process data from one or more raw sources into a usable format, typically for storage, analytics or machine learning.

Data pipelines play a crucial role in automating data processing. While some pipelines may be as simple as transferring data from one system to another, they can quickly become complex - especially when other pipeline depend on the output of another pipeline.

This is why when building data pipelines, there is much more to consider than simply moving data from point A to point B.

To build truly effective pipelines, you need to account for:

  • Scalability: Can the pipeline handle increasing data loads?

  • Reliability: Will the pipeline perform consistently under different conditions?

  • Observability: Can you easily monitor, debug, and understand what’s happening under the hood?

  • Maintainability: How easy is it to update or modify the pipeline?

  • Security: Does the pipeline adhere to data security and compliance standards?

That’s where pipeline-flow steps in. It’s designed to tackle these problems for you by providing a flexible, scalable framework for moving and processing data across different systems with minimal overhead and maximum reliability.

Types of Data Pipelines

pipeline-flow supports all three types of data pipelines: ETL, ELT, and ETLT. Each pipeline type has a unique structure and workflow, as described below:

Pipeline Types

Pipeline Type

Pipeline Definition

ETL

A traditional data processing pipeline that extracts data from source systems, transforms it into a usable format, and loads it into a target system like a data warehouse.

ELT

ELT differs from ETL by loading raw data directly into the target system before transformation, leveraging the power of modern databases for processing.

ETLT

ETLT extends traditional ETL pipelines by introducing an additional transformation step after loading to enable further refnements and data enrichment.

Next Steps

  • Check out the Concurrency guide to learn how pipeline-flow uses asynchronous programming to execute tasks concurrently.

  • Explore the Plugin Basics guide to learn more about plugins.

  • Check out the Pipeline Orchestration guide to learn how to manage pipeline dependencies and their execution order.