API integration vs data integration: how the two approaches differ

As you look to connect applications and sync data, you’ll probably consider a few different approaches. 

API integration and data integration likely top your list. 

To help you determine which is better for a given integration scenario, we’ll break down how each one works by defining it and sharing some examples. We’ll then compare the approaches directly.  

What is API integration?

It involves connecting applications via their APIs. Once connected, these applications can sync specific data on a predefined, time-based interval or in near real-time.

Types of API integration

You can implement two types of API integration: product integrations or internal integrations.

Internal API integrations are between the applications your teams use internally. 

For instance, if your marketing team relies on Hubspot as its marketing automation platform and your sales team uses Salesforce as its CRM, you can connect the two systems and build a flow that routes leads from the former to the latter any time a lead reaches a certain score.

An example of an internal integration

Product integrations, on the other hand, involve connecting your product with clients’ applications. 

Say you provide a sales automation solution that can identify and recommend leads to clients. You can allow clients to connect your product with their CRM system so that they can add a recommended lead to their CRM in near real-time.

Illustration of customer-facing integrations

Related: API integration examples

What is data integration?

Most equate it with the extract, transform, and load (ETL) process. This involves extracting data from various source systems, transforming the data to a specific data model, and then loading all of it into a data warehouse (e.g. Snowflake).

Data integration visualization

The data in the data warehouse can then be synced with BI tools so that your analysts can leverage an accurate and diverse set of information.

Related: Benefits of app integration

Types of data integration

Aside from ETL, you can associate data integration with reverse ETL and embedded reverse ETL.

Reverse ETL allows you to sync specific data from your data warehouse with individual downstream applications, allowing your employees to access the insights they need within the applications they already use to make better decisions. 

Reverse ETL visualization

Embedded reverse ETL lets your clients integrate their applications with your product via a 3rd-party integration tool, and build a sync that keeps specific data between their applications and your product consistent with one another.

Embedded reverse ETL visualization

This can benefit your product—and, in turn, your clients—in a number of ways, from enriching your product’s analytics capabilities to enhancing its workflow automations. 

Given all the forms that API and data integration can take, it can be difficult to determine how they differ—but we’ll attempt to do just that below.

Data integration vs API integration 

Data integration generally requires syncing data with a data warehouse, while API integration can involve syncing data with any type of system, so long as it offers the relevant API endpoint(s).

Related: Comparing a unified API solution with an embedded iPaaS

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