What is an ETL pipeline?

An ETL pipeline is a data processing workflow that extracts data from one or more source systems, transforms it into a structured and consistent format, and loads it into a target system (typically a data warehouse or data lake) where it can be queried for analytics and reporting.

ETL (Extract, Transform, Load) is one of the two dominant patterns for moving data between systems, alongside ELT (Extract, Load, Transform). The key distinction: in ETL, transformation happens before the data reaches the destination; in ELT, raw data is loaded first and transformed inside the warehouse. Both patterns serve different architectural needs, which we cover in detail below.

ETL pipelines underpin most enterprise data stacks, connecting operational databases, SaaS applications, and event streams to centralized analytics environments. Understanding how they're structured, when to use them, and how they compare to ELT is foundational for anyone working in data engineering.

What does an ETL pipeline do?

ETL pipelines offer several features that make them an efficient tool for data processing and management. The primary objective of an ETL pipeline is to simplify data processing and provide a more efficient way of moving data so it's prepared for data analytics and business intelligence.

• Continuous data processing: One of the main features of an ETL pipeline is its ability to provide continuous data processing. This means that as new data is created or updated, the pipeline automatically extracts, transforms, and loads it to the target system. This feature ensures that the data is always up-to-date, and insights can be gained in real-time.
• Controllable data access: Another key feature of an ETL pipeline is its controllable data access. ETL pipelines allow data engineers to define the source systems from which data is extracted, the target systems where the data is loaded, and the data transformation processes that take place in between. This control over data access ensures that the right data is processed and made available for analysis.
• Easy to set up and maintain: ETL pipelines are also easy to set up and maintain. They can be configured quickly, and changes can be made to the pipeline with relative ease.
• Agile and easy to manipulate: ETL pipelines allow data engineers to transform data quickly and efficiently, giving them the flexibility to adapt to changing data needs.

These features make ETL data pipelines an effective tool for data processing and management. Next, we will discuss how to build an ETL pipeline.

How to build an ETL pipeline: The six core steps

Building an ETL pipeline requires a step-by-step approach to ensure that the pipeline is created efficiently and effectively. The approach you take will depend on the method of ETL you use. Traditional batch processing ETL pipelines and modern stream processing ETL pipelines will require different approaches.

In general, building an ETL pipeline involves following steps:

1. Create reference data

The first step is to create a reference dataset, which involves creating a list of the possible values the data could contain. This is particularly useful for fields with limited values, such as country, gender, and product type. Creating reference data or schema helps ensure data integrity and consistency across the pipeline.

2. Extract and standardize

The second step is to extract data from different sources and convert it into a single format to ensure standardization. This involves identifying data sources and using connectors and APIs to extract the data from these sources. Standardization helps ensure that data from different sources can be combined and processed correctly. Data ingestion and data streaming tools can help with collecting this real-time data from business systems.

3. Validate data

Validating data is the next step in building an ETL pipeline. This step involves ensuring that the data is clean, complete, and accurate. Data validation includes checking for duplicates, missing values, and outliers. Validating data is important because it helps ensure the accuracy and reliability of the data.

4. Transform data

After validating the data, the next step is to transform it. This involves cleaning the data to remove duplicates, checking integrity, applying business rules, data governance, aggregation, and other transformations. Transforming the data helps prepare it for analysis and ensure it's in a format that can be easily understood.

5. Stage data

The transformed data doesn't immediately enter a data warehouse. Instead, it's staged in a staging database, which acts as a buffer between the source data and the target data warehouse. Staging data helps diagnose and repair potential problems before the data is loaded into the data warehouse. It also generates an audit report that can be used for analysis.

6. Load data

The final step in the ETL pipeline is to load the data into the target data warehouse. Each published batch will either overwrite or amend the existing information, depending on the chosen preferences. It's also essential to choose how often to load a new batch of data, picking a daily, weekly, monthly, or custom range with a timestamp to indicate when publishing occurred.

To summarize, building an ETL pipeline involves creating a reference dataset, extracting and standardizing data, validating data, transforming data, staging data, and loading data into a target data warehouse. These steps are critical for ensuring that data is processed accurately, consistently, and efficiently.

While it might seem like a lot of work, there are multiple ETL tools which make it easy to build ETL data pipelines.

ETL vs. ELT: Which Pattern Should You Use?

ETL and ELT both move data from source systems to a destination, but the order of operations—and the architectural implications—are meaningfully different.

The core distinction:

• ETL (Extract, Transform, Load): Data is transformed before it reaches the destination. Transformation typically happens in a dedicated processing layer or intermediate staging environment.
• ELT (Extract, Load, Transform): Raw data is loaded into the destination first. Transformations run inside the warehouse using its native compute (e.g., SQL-based tools like dbt).

When to use ETL:

• Your destination system has limited compute and cannot run heavy transformations efficiently.
• Regulatory or compliance requirements dictate that sensitive fields (PII, PHI) must be masked or removed before the data is stored anywhere downstream.
• You're working with legacy on-premise databases where a staging transformation layer is already part of the architecture.
• Your transformation logic is complex enough that it needs dedicated orchestration outside the warehouse.

When to use ELT:

• You're building on a modern cloud data warehouse (Snowflake, BigQuery, Databricks, Redshift) where compute is elastic and cost-efficient.
• You want to preserve raw data fidelity and re-transform on demand as business logic changes, without re-ingesting.
• Your team uses SQL-first transformation tooling (dbt is the dominant example) and wants transformations version-controlled alongside the rest of your data stack.
• You need faster time-to-load and can tolerate transformation latency post-ingestion.

The practical reality in modern data stacks: Most data teams don't operate in a pure ETL or ELT model. A common pattern is to use lightweight extraction and loading tools (Fivetran, Airbyte) to land raw data in a warehouse, then apply dbt models for transformation, which is technically ELT. However, certain upstream transformations (type casting, deduplication, PII masking) often happen at the connector or ingestion layer, blurring the line. Understanding why each pattern exists is more useful than treating them as mutually exclusive.

Note: RudderStack offers Reverse ETL, which is moving transformed data out of the warehouse and into operational tools. This is a distinct pattern from both ETL and ELT.

What Are the Benefits of an ETL Pipeline?

ETL pipelines have numerous benefits for businesses, such as:

1. Standardizing data: ETL pipelines allow data to be standardized and transformed into a consistent format. This enables businesses to analyze data from multiple sources and gain valuable insights into business operations.
2. Migrating to a data warehouse: ETL pipelines allow businesses to migrate data from multiple sources to a single data warehouse, making it easier to manage and analyze. This enables businesses to quickly access relevant information and make informed decisions.
3. Deeper analytics: ETL pipelines enable businesses to access deeper analytics and insights into business operations. This can help companies identify trends, patterns, and opportunities that might otherwise go unnoticed.
4. Improved efficiency: ETL pipelines automate the process of extracting, transforming, and loading data, making it more efficient and less prone to error. This frees up time and resources that can be used for other tasks.
5. Better decision-making: By standardizing data and making it easily accessible, ETL pipelines enable businesses to make better decisions based on accurate, up-to-date information.
6. Cost savings: By automating the process of data extraction, transformation, and loading, ETL pipelines can help businesses save time and money. This is particularly true for businesses that need to process large volumes of data on a regular basis.

Overall, ETL pipelines provide a more efficient and effective way for businesses to manage and analyze data, enabling them to make better decisions and achieve better results.

ETL pipeline in practice: An e-commerce example

Let’s understand the use case of ETL pipelines for an e-commerce company. An ETL pipeline is a crucial tool for e-commerce companies as it enables them to extract, transform and load their data into a centralized location for analysis. This allows businesses to gain valuable insights into their customer behavior, sales patterns, inventory levels, and more.

In this section, we will explore a real-world example of an e-commerce company that implemented an ETL pipeline to process its data. By the end of this section, you will have a better understanding of how an ETL pipeline can help an e-commerce company make data-driven decisions to improve its business operations.

This e-commerce company had data from various sources, including:

• Sales data from an online store’s database, its online marketplace listings (e.g. Amazon), and CRM tool: order history, customer information, product information, etc.
• Website analytics: traffic, clicks, bounce rates, etc.
• Social media analytics: engagement, followers, etc.
• Inventory management system data from all storefronts inventory management software and csv files uploaded by managers: stock levels, reorders, etc.
• Marketing campaign data: clicks, impressions, conversion rates, etc.

Now, let’s cover how to start building an ETL pipeline for this e-commerce business:

1. Extract:

To extract the raw data from these various sources, we can use a combination of APIs, web scraping, and direct database connections. For example, we can connect to the database or csv file of the inventory management system to extract data on stock levels and reorders, and use the Google Analytics API to extract website analytics data. We can also use web scraping tools to extract social media analytics data from various platforms.

2. Transform:

Once the data is extracted, it needs to be transformed into a format that is standardized and ready for analysis. This involves cleaning the data, removing duplicates, and converting data into a consistent format. For example, customer data from the online store may need to be standardized to ensure consistency across all sources. We can also apply business rules and data governance policies to ensure the data is accurate and reliable.

3. Load:

After the data is transformed, it needs to be loaded into a data visualization tool for analysis. We can use tools like Tableau, Power BI, Grafana, Plotly, etc. to create visualizations and reports that can help us understand trends and patterns in the data. For example, we can create a dashboard that shows sales performance over time broken down by product category or geographic region. We can gather valuable business insights from those visualizations, like demography of your high-value customers. We can also create reports that show the effectiveness of marketing campaigns, or the impact of changes in inventory levels on sales.

Using an ETL pipeline in this way can help an e-commerce company gain valuable business insights into their business operations, customer behavior, and marketing effectiveness. By standardizing data and bringing it into a centralized location (such as any data lake or data warehouse), we can gain a more comprehensive view of the business and make data-driven decisions.

Additionally, by automating the ETL process, we can save time and reduce errors associated with manual data entry and manipulation.

Further reading

If you want to dive deeper into the world of ETL pipelines, we recommend checking out the following pages of the Rudderstack Learning Center:

• ETL Pipeline vs Data Pipeline: This page compares and contrasts ETL pipelines with data pipelines, providing a clear understanding of the differences between the two.
• Three Stages of the ETL Process: This page goes into detail about the three stages of the ETL process: extraction, transformation, and loading. You’ll learn about the challenges of each stage and how to overcome them.

By implementing ETL pipelines, companies can make better business decisions based on accurate data analysis. So, it's worth investing time to understand this essential process.