API vs ETL: What’s the Difference and Which Should You Use?

So which is better: API or ETL? This question often trips up many companies. The truth is, there isn’t a straightforward answer—it really depends on your specific situation. Each approach has advantages and disadvantages. While both API and ETL help move data across systems and support analytics and reporting, they do so in different ways.

Choosing between API and ETL isn’t about one being universally better than the other—they each serve unique purposes. The right choice for your business depends on your data needs, infrastructure, and individual use case.

Here’s a breakdown of what each one does, how they differ, and examples of when to use them.

What is an API?

An API (Application Programming Interface) is a set of rules and protocols that allows two systems or applications to communicate with each other in real time. You can think of it as a messenger that takes a request from one system, sends it to another, and brings back the response in a format that both systems can understand.

Imagine you’re at a restaurant. The menu is the API, showing what’s available. You (the client) place an order with the waiter (the API), who takes your request to the kitchen (the server) and then brings your food back to you. You don’t need to know how the kitchen prepares the food—just like you don’t need to know the inner workings of the server to use an API.

What are some examples of API data integration?

APIs are commonly used to retrieve or send small pieces of data on demand. They’re lightweight, flexible, and often used when data freshness is critical. You likely use these on a daily basis without even realizing that you are interacting with an API and its data.

Here are some common uses of APIs and how they integrate and transfer data:

• Displaying live weather data. Dashboards and apps use weather APIs to pull current conditions and forecasts in real time.
• Syncing CRM and marketing platforms. APIs connect tools like Salesforce and Mailchimp to share customer data and automate campaigns.
• Fetching real-time stock prices or currency exchange rates. Financial apps use APIs to display the latest market data and conversions.
• Making a payment online. Many online payment options use a payment API (like Stripe or PayPal) to process transactions.
• Logging in with Google or Facebook. Many apps and websites let users log in using other accounts, which saves time and improves security.
• Booking flights or hotels. Travel aggregators like Expedia or Kayak use APIs to pull availability and prices from airline and hotel systems in real time.
• Sending text messages. Apps and services use APIs from providers like Twilio to send SMS alerts, authentication codes, or marketing texts.
• Streaming music or video. Platforms like Spotify and YouTube expose APIs so developers can create apps that search for media, control playback, or access user playlists.
• Tracking package deliveries. Shipping carriers, like UPS, FedEx, or DHL, provide APIs that let businesses track packages and update customers with real-time delivery statuses.
• Chatbots and virtual assistants. Chat apps (like Slack or Microsoft Teams) often use APIs to integrate bots that fetch data, schedule meetings, or automate tasks directly within the chat.
• Integrating analytics tools. Tools like Google Analytics or Mixpanel provide APIs to programmatically access website or app usage data, allowing for custom dashboards or deeper analysis.
• Updating inventory in real time. Retailers and marketplaces use APIs to sync product listings, availability, and pricing between warehouses, point-of-sale systems, and online storefronts.
• Automating social media posts. Social media management platforms (like Buffer or Hootsuite) use APIs from Twitter, Instagram, LinkedIn, and others to schedule and post content.

What is ETL?

ETL (Extract, Transform, Load) is a traditional data integration process used to move large volumes of data from one system to another, typically from transactional databases to data warehouses or data lakes. It’s often done on a scheduled basis, such as nightly, hourly, or at other regular intervals. It’s a foundational component in data engineering and analytics pipelines.

Here’s a straightforward explanation of each of the three steps in ETL:

Extract

This step involves pulling data from multiple sources, such as databases, APIs, files, or cloud platforms. These sources can be structured (like SQL databases) or unstructured (like log files or emails).

Transform

Once data is extracted, it’s cleaned and transformed. This may include removing duplicates, correcting formats, filtering, aggregating, or enriching data. The goal is to standardize and prepare the data so it can be effectively used for analysis.

Load

Finally, the transformed data is loaded into a target destination, such as a data warehouse or a data lake. From there, it’s ready for use in reporting, dashboards, business intelligence, or machine learning models.

ETL is crucial for data integration from multiple sources. Not only does ETL help handle massive amounts of data, but it also helps improve the quality of the data through transformation and validation. ETL also empowers effective analytics with clean, structured data. Additionally, ETL is important for creating a single source of truth across departments.

What are some examples of ETL data integration?

ETL is especially useful for handling complex, large-scale data integration tasks across systems. While it is a bit slower and more resource-intensive than API, it can handle huge data loads. ETL also has the data transformation step built in, which helps ensure data quality and consistent formatting.

Here are some real-life scenarios of when companies might opt to use ETL:

Customer data integration

Companies often collect customer data from multiple touchpoints, like websites, CRMs, support tickets, and email platforms. ETL is used to extract this data, clean and standardize it (e.g., deduplicating records, unifying formats), and load it into a central database or warehouse. This provides a 360-degree view of the customer, enabling better marketing, sales, and support.

Financial reporting

Finance teams use ETL to consolidate data from accounting systems, bank feeds, and expense platforms. They transform the data into a consistent structure (e.g., standardizing currencies or categorizing expenses) and load it into reporting tools or data warehouses. This allows for accurate and automated monthly, quarterly, or annual financial reporting.

E-commerce analytics

An online retailer may use ETL to combine order data, web traffic data, and product inventory into a single system. This helps analyze customer behavior, monitor best-selling products, manage stock levels, and forecast demand—all in one place.

Healthcare data integration

Hospitals and healthcare providers often have data coming from electronic health records (EHRs), lab systems, billing platforms, and more. ETL processes help unify patient records, ensure regulatory compliance (like HIPAA), and enable population health analytics.

Migrating legacy data

When an organization transitions from an old system to a new one (e.g., upgrading its ERP or CRM), ETL is used to extract data from the legacy system, reformat or clean it, and load it into the new environment—often with enhancements like field mappings or updated schemas.

Business intelligence and dashboards

Many organizations use ETL to populate dashboards and BI tools (like Tableau, Power BI, or Looker). For example, sales, marketing, and operations data can be brought together nightly or hourly so decision-makers can always access up-to-date, unified metrics.

IoT and sensor data aggregation

In industries like manufacturing, logistics, or agriculture, ETL can aggregate and normalize time-series data from sensors and IoT devices. This enables real-time monitoring, anomaly detection, and predictive maintenance.

Compliance and auditing

ETL processes help collect and prepare data for compliance audits—such as SOX, GDPR, or HIPAA—by ensuring that records are complete, consistent, and traceable across systems.

Key differences between API and ETL

While both API and ETL move data between systems, they do so in distinctively different ways. Neither is inherently better than the other; however, because they have different strengths and weaknesses, one may be more appropriate than the other in certain situations.

Take a look at these key differences between API and ETL:

Data Movement

• API: Transfers data in real-time or near real-time
• ETL: Moves data in scheduled batches

Volume

• API: Ideal for small to medium-sized datasets
• ETL: Best for handling medium to large volumes of data

Use Case

• API: Commonly used for on-the-fly data access and app integrations
• ETL: Suited for bulk data transfer, historical reporting, and centralized analysis

Transformation

• API: Typically involves minimal or external transformation
• ETL: Includes a built-in data transformation step

Complexity

• API: Can range from simple to complex depending on the integration
• ETL: Often more complex and infrastructure-intensive

Latency

• API: Low latency with fast access to data
• ETL: Higher latency, depending on batch schedules and system load

Keep in mind that because of these differences, API and ETL can be complementary. You don’t have to unilaterally choose API over ETL or vice versa.

In many modern data stacks, companies use both: APIs for real-time integrations and ETL for bulk data processing. They often work hand-in-hand.

For example, an API might bring in real-time data to a live dashboard, while an ETL process runs nightly to feed a broader analytics warehouse.

When would you use ETL over API?

You would typically choose ETL over an API when you need to move large volumes of data on a scheduled basis, especially for tasks like historical analysis, data warehousing, or complex data transformations.

ETL is ideal for consolidating data from multiple sources into a central location—like a data warehouse—where it can be cleaned, standardized, and structured for reporting and business intelligence.

For example, if a company wants to track monthly sales performance across different regions and product lines, ETL can pull that data from various systems, harmonize it, and load it into a single database for easy access and analysis.

ETL is also the better choice when data freshness isn’t required in real time and the focus is instead on data completeness and consistency. ETL is especially useful for compliance and auditing, where capturing full data sets in a reliable and repeatable way is more important than speed. Additionally, ETL supports complex logic and transformation steps that might be harder to implement via APIs alone.

If your data process involves joining multiple tables, aggregating large data sets, or applying business rules before loading the data, ETL offers a more structured, scalable approach.

When would you use API over ETL?

You would typically choose to use an API over ETL when you need real-time or near real-time access to data, especially in scenarios where freshness and speed are critical. APIs are great for pulling small, specific pieces of data on demand.

For example, they can be used for retrieving the latest weather forecast, syncing a CRM with a marketing automation platform, or displaying real-time stock prices in a dashboard.

APIs are also the go-to choice when integrating modern cloud applications that expose RESTful endpoints designed for fast, frequent interactions.

Another reason to favor APIs is when event-driven workflows are needed. This includes triggering actions based on user behavior, sending alerts when thresholds are crossed, or syncing data the moment it’s updated.

APIs allow systems to communicate instantly and bidirectionally, making them ideal for building connected applications, mobile experiences, or services where up-to-the-minute accuracy matters.

They’re also more flexible for lightweight, decentralized data needs, where setting up a full ETL pipeline might be overkill.

How do I know whether to use API or ETL?

If you’re still struggling to decide whether API or ETL is the right choice, here’s a quick guide on how to know which will be more useful.

Choose an API when:

You need real-time or near real-time data access

For example, pulling the latest transactions, stock prices, or sensor data as it happens.

You’re integrating cloud applications

Many modern platforms (like Salesforce, Slack, or Shopify) provide robust APIs for connecting and syncing data on demand.

Your use case is interactive or event-driven

APIs are ideal for user-facing applications where immediate responses matter (e.g., login, submitting forms, syncing content).

You’re working with small, frequent updates

APIs are lightweight and efficient for pulling specific records or sending real-time data, rather than processing huge data sets.

Choose ETL when:

You’re working with large volumes of data

ETL handles bulk data movement efficiently—perfect for databases, logs, or historical records.

You need to combine and transform data from multiple sources

ETL can join data sets, clean inconsistencies, and apply business logic before loading data into a warehouse.

Data freshness is less critical

If daily or hourly updates are enough (e.g., for business reports), a scheduled ETL pipeline works well.

You’re building a centralized analytics system or data warehouse

ETL helps consolidate information into one place for long-term storage, reporting, and historical analysis.

Ready to power smarter data workflows?

Whether you’re using API, ETL, or both, Domo powers smarter data workflows. Your data from all sources comes to life in Domo’s dashboards. With our platform, you can use data from both API and ETL to make smarter workflows, revenue-boosting decisions, and collaborative projects.

To see how you can get more value out of your transformed data, watch a quick demo today.