What Is an ETL Pipeline? How It Works, Types, and Best Practices

What is an ETL pipeline?

ETL is an acronym that stands for extract, transform, load. It is commonly used to represent the complex process of ingesting data from source systems, cleaning it for business use, and outputting it into an analytics system such as a BI tool or data warehouse. ETL data pipelines allow businesses to combine data from different systems, clean and standardize it, and make it available for analysis. 

To support this goal, an ETL pipeline extracts raw data from various sources while maintaining its original format. The data is then transformed to support further analysis so it can be cleaned, corrected, formatted, or all of the above to be ready to use. Once it’s transformed, the data is brought together and stored in a data warehouse, a database, or a BI tool. 

ETL pipelines can improve data quality by ensuring your data is clean and accurate. These critical data tools make it possible to centralize data from multiple sources, creating easily accessible information for your end business users. And by automating these data processes, ETL pipelines can save time and reduce manual work. When your company is ready to scale, your ETL pipelines can help you scale your data, ensuring you can manage growing data volumes to support data-driven decision-making.

ETL vs. ELT (What’s the difference and when to use each)

ETL (extract, transform, load) transforms data before it lands in the destination. It’s a strong fit when you need strict governance, complex reshaping, or curated analytics-ready data for a BI tool or warehouse.

ELT (extract, load, transform) loads data first—often into a cloud data warehouse or data lake—and performs transformations there. ELT is faster for very large or unstructured data sets because it takes advantage of scalable cloud compute power.

How to choose:

• ‍‍Use ETL when upstream quality and governance requirements are strict, when latency can be scheduled in batches, and when you need trusted data models for reporting.
• Use ELT when you’re handling large, diverse datasets and need faster landing of raw data that will be transformed later in-platform.
• Many companies use a hybrid approach: ELT for raw landing and light transformations, ETL for governed and business-ready data marts.

How does an ETL pipeline work?

Let’s dive into some of the back-end details of how an ETL pipeline works. 

1. Extracting data. The first step involves gathering data from various sources like databases, files, software tools, data warehouses, or other places. The ETL pipeline needs to connect to these sources, which is typically done through APIs, file loads, or database connections like ODBC or JDBC, where the ETL tool can use SQL queries to extract the correct data. Data is extracted in its original state and can be structured, semi-structured, or unstructured.
2. Transforming and cleaning data. After extraction, the data typically undergoes cleaning and transformation.
   
   • Data cleaning: The cleaning process involves removing duplicates, correcting errors, handling missing values, and standardizing data formats. This step ensures the data is accurate and consistent.
   • Data transformation: This involves more complex operations like aggregating data (e.g., summing up sales figures), filtering out irrelevant information, and enriching the data by combining it with other data sources. Data might also be normalized or denormalized, depending on the requirements of the analysis.
3. Loading data into target systems. The final step in the ETL process is loading the transformed data into a target system for analysis and reporting. Depending on your business needs, this can be done in batches or in real-time. For instance, a company might load sales data into a data warehouse so analysts can generate reports and dashboards to gain insights into sales trends and customer behavior.

ETL pipeline vs. data pipeline (Broader category)

An ETL pipeline is a specific type of data pipeline that always includes transformation.

A data pipeline is any system that moves data between sources and destinations—streaming, replication, event-driven, or batch—with or without transformation.

ETL pipelines are built for analytics and business intelligence. Broader data pipelines can also support operational workflows, application syncs, or real-time event streaming.

Batch, real-time, and change data capture (CDC)

ETL pipelines can run in different modes depending on latency needs.

Batch ETL runs on a schedule, collecting and processing data in groups. It’s commonly used for daily or hourly reports.

Real-time (streaming) ETL processes data continuously as it’s generated. This mode powers live dashboards, alerts, and operational analytics that need instant updates.

Change Data Capture (CDC) captures inserts, updates, and deletes from source systems and applies them incrementally. This keeps data current without the need for full reloads.

Common challenges with ETL

While ETL pipelines can be one of the best ways to make data usable and available, they can come with their own set of challenges. These can amplify problems that already exist in your data or, when not set up or managed correctly, can introduce new problems into your data. Common challenges with ETL can include: 

• Data quality and consistency. Combining data from multiple sources often leads to inconsistencies, duplicates, and errors. Different formats and naming conventions can complicate data integration, requiring time-consuming validation and cleaning to ensure accuracy.
• Managing large volumes of data. ETL pipelines can slow down as data grows, making scalability a major challenge. Large datasets demand more resources and infrastructure, so you need more resources to manage and support that data, adding cost and complexity.
• Ensuring data security and compliance. Protecting sensitive data during the ETL process is vital. Teams do this through encryption, developing access controls, and adhering to government regulations. However, data entering the pipeline will include sensitive information. Companies can violate regulatory requirements if strict security controls aren’t consistently maintained. 
• Observability and lineage. As pipelines multiply, troubleshooting issues becomes harder without clear lineage. Tracking job status, data freshness, and field-level lineage helps teams trace and fix problems quickly.
• Cost control. Cloud compute and storage can grow fast. Use incremental loads, pushdown processing, and partition pruning to manage costs effectively.

Best practices for building an ETL pipeline

Choosing the right ETL tools

Whether your company has been using ETL and needs to develop new processes or is starting to create more efficient ways to ingest data, you can follow the same guidelines for building an ETL pipeline. 

As always, your choice is based on your company’s specific needs. Think through how you’ll need to use your data, what types of data you’ll need to transform, and what sources you’ll need to connect your ETL pipeline. Choosing the right tool will depend on the 

• data volume, 
• complexity, 
• and processing speed your company requires. 

Once you’ve identified tools that meet those basic needs, you can narrow down your options by analyzing the data fluency across your organization. Are many people in your company familiar with data, SQL, and development tools? Then, you can probably get a more complex ETL tool that allows for customizations. If data literacy is low at your company, look for tools with user-friendly features like drag-and-drop interfaces. 

Then, look at which tools will integrate best with your systems. Some tools have libraries of connectors that allow you to easily and quickly connect your data with your ETL pipeline.

To design a scalable and efficient pipeline, consider the following steps: 

1. Understand where the data is coming from. Knowing the source systems you want to extract data from is essential when starting a data pipeline. To be effective, make sure you fully understand the pipeline requirements, such as what data is needed, from what systems, and who will be using it.
2. Practice good data hygiene and transformation. Pulling data from different systems can become quite messy. Data hygiene is the collective process to ensure the cleanliness of data. Data is considered clean if it is relatively error-free. Dirty data can be caused by a number of factors including duplicate records, incomplete or outdated data, and the improper parsing of record fields from disparate systems. Data may also need to be transformed to meet business requirements. These transformations can include joining, appending, creating calculations, or summarizing the data.
3. Know where you’re storing the data. Every ETL pipeline needs a defined destination where data can land once imported, cleaned, and transformed. Storing data is critical to any ETL process because it ensures the data can be used when needed. Common data storage methods include data lakes, data warehouses, cloud storage, and modern BI tools.
4. Schedule updates. After completing the initial set-up of your ETL pipeline, it’s important to understand how often you’ll need it to run and which stakeholders will need access to the data. Many data pipelines run on chron jobs, which is a scheduling system that lets a computer know what time a process should be kicked off. Modern ETL tools have a range of scheduling options from daily to monthly to even every 15 minutes.
5. Monitor and troubleshoot the ETL processes. Once an ETL pipeline is created, it is never truly finished. Creating a data pipeline is an interactive process, and small changes will need to be made over time. For example, a new field could be introduced from the source system that will need to make its way into the BI tool downstream. Small changes can be rapidly expedited through good documentation and training.

ETL Best-Practice Checklist

• Use source-aligned staging and curated models (often called bronze, silver, and gold layers).
  
• Validate early by checking schema, datatypes, and nulls.
  
• Prefer incremental or CDC loads instead of full reloads.
  
• Centralize transformation logic so it’s reusable and well-documented.
  
• Enforce governance with role-based access, data masking, and audit logs.
• Monitor jobs, record counts, freshness, and error rates to keep data pipelines healthy.

Core ETL Components (At-a-Glance)

• ‍Connectors: APIs, file imports, and database connections that extract data from source systems.
• ‍Transform engine: Cleans, deduplicates, and standardizes data for consistency.
• ‍Orchestration: Handles scheduling, dependencies, and alerts.
• ‍Storage targets: Data warehouses, data lakes, or BI platforms that store the transformed data.
• ‍Governance and quality: Catalogs, lineage, and validation rules that maintain trust.
• ‍Observability: Logs, metrics, and monitoring that keep teams informed about pipeline performance.

ETL pipeline examples and use cases

ETL pipelines are valuable for businesses because they bring data together. Companies that establish ETL pipelines get a view into their operations, sales, marketing, or a critical combination of all data to ensure they’re able to see the big picture. By taking time to connect data from a variety of sources, these companies use ETL pipelines to improve business outcomes: 

• A retail company uses ETL pipelines to connect data from critical tools like Quickbooks, Google Drive, and Google Analytics. By bringing in data across the organization, the CEO no longer has to wait for individual employees to build reports and share them up the chain. They can see all the data they need to make business-forward decisions in one place, automatically updated through the ETL pipelines, and easily combined with other important data. 
• A technology company provides software solutions for companies needing payment processing. Before bringing their data into one place, it was a manual process to track down disparate and siloed data sources, and many people questioned the accuracy of the data. By connecting data through ETL pipelines into a BI platform, the company built trust in the data and got insights within a matter of weeks, eliminating the need for costly and time-intensive manual reports. 
• A global supply chain management company had a problem with data sources and siloed data across departments, regions, and tools. Often, teams wanting to analyze data were spending massive amounts of effort trying to track it down without knowing what data was available. Useful data was spread out across hundreds of different systems. In one warehouse, teams spent 90 minutes twice a day downloading files and organizing them to help determine daily priorities. By building ETL pipelines that automatically ingested, analyzed, and formatted data into useful dashboards, this company increased productivity and saved massive amounts of time. 

These companies are not outliers. Using ETL pipelines to bring in data, transform it into functional and usable information, and load it into a platform or tool for further analysis can benefit organizations across industries. Consider the following examples: 

Sales data from CRM

An extremely common use case for ETL pipelines is automating the data in customer resource management (CRM) systems. CRM tools regularly update vast amounts of data about customers. 

An ETL pipeline can automate the reporting for customer accounts and opportunities in the sales pipeline. Once data is taken from the CRM, it can be combined with finance, customer success, or marketing data. Then, teams can load the data into a BI tool for further analysis. 

Logistics data from ERP system

Enterprise resource planning (ERP) software remains a huge use case for ETL pipelines. These transactional databases can contain info about your business, such as orders, shipping, procurement, and financial data. Understanding this data can be critical to your company’s success.

A key consideration when working with data from ERP systems is the data modeling relationships between tables. Oftentimes, these can be complex for systems that process inventory and orders. ETL pipelines can use automation to remove this complexity by creating a data model once, and then running the data through that model for subsequent jobs.

Product data from back-end databases

Data is also stored in large quantities in databases used by businesses. These databases can contain information about products, employees, customers, and many other things. A great example is software companies that use back-end databases to store information about their users and the software’s configuration.

Databases can be massive in size and complexity. A robust ETL pipeline tool can create scalable processes even when billions or trillions of rows are added to the database. The power of automating this much data can provide massive insights into your business. This data can also be surfaced through a BI tool for easy analysis by business users.

Benefits of ETL Pipelines

• Clean, trustworthy data that fuels analytics and machine learning.
  
• Faster time to insight through automation and reduced manual work.
  
• A single, consistent view of business data across systems.
  
• Stronger compliance and governance through standardized processes.
• Scalability to handle growing data volumes and new sources.

What to look for in an ETL pipeline tool

Data is the best tool for empowering businesses and individuals to accomplish difficult tasks. As you begin looking for and using an ETL pipeline, your business can reap the benefits of your data to produce more actionable insights. Consider the following tips as you look for the right tool for your business:

• Cost. Make sure you understand the total cost of ownership, including licensing fees, infrastructure costs, ongoing charges for feeds, and potential hidden expenses like support or additional features. 
• Scalability. You’re only going to produce more data and will need your data to support more complex processes. Your ETL tool needs to be able to grow with your data needs. Don’t just focus on what you need ETL pipelines to do today; try to understand your future capabilities for your data. 
• Ease of use. Democratizing data across your company to experienced and non-technical users will have a dramatic impact. While the initial set-up may be done by an IT team, look for tools that can be used by non-technical team members. 
• Integration. Your ETL pipeline won’t do you much good if you have to spend a ton of resources connecting every system manually. Find a tool that has pre-built connectors to your most important software solutions, data warehouses, and databases so you can keep your ETL pipeline relevant. 

Quick Evaluation Rubric for ETL Tools

• ‍Must-haves: the right connectors, incremental load or CDC support, data lineage tracking, and strong security.
  
• ‍Nice-to-haves: visual design options, SQL and scripting support, version control, and cost monitoring.
  
• ‍Team fit: choose drag-and-drop simplicity for less technical users or developer-friendly platforms with advanced customization for technical teams.

See how Domo simplifies ETL

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