What Is an ETL Pipeline? Definition, Example & Tools

Data extraction is the process of identifying, retrieving and replicating raw data from various sources into a target repository. It is the first step in both ETL and ELT processes, gathering data for deeper analysis and insights.

Data transformation adapts raw data into useful, comparable formats for analysis (and other purposes). It involves cleaning, normalizing, aggregating, joining and filtering data.

Data loading is the final step of the ETL process, which involves loading the transformed data to its final destination. The destination can range from a simple database to a massive data warehouse.

Depending on your internal capabilities, your desired data pipeline complexity and the technical skillset of your team, determine if ETL data pipeline creation will be conducted internally or using an ETL tool.

Internal ETL pipeline orchestration (via custom coding) is best-suited for organizations in need of simple orchestrations and/or those with a highly skilled team.

ETL tools are the better option for most businesses since they can run complex ETL processes, reduce data engineer bottlenecking and are highly scalable. Many ETL pipeline tools also offer user-friendly, no-code setup, which is ideal if you lack in-house technical capabilities.

Organizations should also consider the use of cloud-based ETL pipeline tools since they offer significant benefits in scalability and cost-effectiveness.

Depending on your chosen framework, map out a detailed data flow diagram to visualize the entire pipeline.

Specify the data extraction logic, including how data will be retrieved from different sources. Define the data transformation rules, such as cleaning, filtering and aggregation. And, determine the loading mechanisms to transfer transformed data to the target system.

Many ETL pipeline tools offer pre-built templates and customizations to speed up this process. They can also help non-technical users accomplish more without coding or consulting a team of data engineers.

Establish connections to your data sources using the ETL tool's connectors. When doing this manually, extensive coding is often required.

Configure the extraction process to retrieve data from the source systems. Apply necessary transformations to clean, filter and aggregate data. Transfer the transformed data to the designated target system.

While real-time ETL data pipelines can be created, they are not always necessary and can overload your systems.

Schedule batch processing at regular intervals or after a specific event has occurred.

In most cases, batch processing should be performed during off hours to ensure your data is up-to-date – without interfering with other systems. Where possible, automate data collection, extraction, transformation and loading.

Thoroughly test individual components and the entire pipeline to ensure correct functionality.

Implement robust monitoring to track performance metrics and identify potential issues. Define error handling mechanisms to address failures and prevent data loss.

Deploy the pipeline to a live production environment and schedule its execution.

Continuously monitor its performance, addressing any issues promptly. Optimize the pipeline for efficiency to ensure smooth and timely data processing.

The ETL process is the set of steps to extract, transform and load data.

The ETL pipeline is the background orchestration of this process, which may involve many components such as automations, schedulers and monitoring – all of which enable the ETL process to run.

Structured query language (SQL) is not an ETL tool, but rather, a language that can be used alongside an ETL pipeline, especially when communicating with and manipulating databases.

To better understand the relationship, think of SQL as a skilled chef. It selects the right data (or “ingredients”), cleans and prepares it, cooks it into something digestible, and serves it in a well-received format.

Whether you're just “slicing and dicing” raw data or creating a gourmet meal, SQL is the language that dictates the final product.

The main difference between the ETL process and the ETL pipeline lies in their scope.

The ETL process refers to a specific type of data movement process in which data is extracted from a source system, transformed and then loaded into a target repository. Once the data is loaded, the “ETL process” is complete.

An ETL pipeline encompasses the ETL process, however, there may be additional steps and processes involved within the larger pipeline including data ingestion, enrichment and further analysis after loading.

ETL pipelines can work in real-time. However, the core processes may be slightly different from the standard ETL process (which is typically performed in batches).

Real-time ETL pipelines are more complex and often require the right ETL tool that can help you easily manage these processes.