What Is Data Integration?

Data integration is the process of combining data from different systems, formats, and locations into a single, unified view that teams can actually use. A retailer pulling order data from Shopify, inventory from a warehouse system, and customer records from Salesforce into one place for reporting is doing data integration.

TL;DR:

Data integration combines data from multiple systems into one coherent, usable view. It's the broader discipline that ETL, ELT, replication, and API sync all serve. Most of the manual schema-mapping work inside it is now shifting to AI agents, with people reviewing the result rather than building it field by field.

How Data Integration Works

Most integration work happens through one of four methods, and the right one depends on data volume, how fresh the data needs to be, and where the transformation logic should live.

If you're unclear which method fits, the difference between ETL and ELT is a good place to start.

Data Integration vs Data Ingestion

These get used interchangeably, but they're not the same scope of work. Data ingestion is the act of pulling data in from a source. Data integration is the broader discipline of making that data coherent and usable once it has landed: schema mapping, deduplication, and reconciling conflicting values across systems. Ingestion is one step inside integration, not a synonym for it.

Common Data Integration Patterns

Beyond the method used to move data, two structural decisions shape how an integration layer actually behaves in production.

Hub-and-spoke vs point-to-point. Point-to-point integration connects each source directly to each destination it feeds. It's fast to set up for one or two connections, and it becomes unmanageable fast: ten sources feeding five destinations is up to fifty separate connections to build and maintain, each one breaking independently. Hub-and-spoke routes everything through a central point, usually the warehouse, so each source integrates once and every destination reads from the same place. Almost every team that starts point-to-point ends up migrating to hub-and-spoke once the connection count gets past a handful.

Unify in the warehouse vs virtualize in place. Most integration work consolidates data physically, copying it into a warehouse where it can be queried fast and joined cheaply. The alternative, data virtualization, queries sources live without copying them first. Virtualization trades setup speed for query performance at scale, which is why it tends to suit smaller or infrequent queries rather than being the backbone of core reporting.

Where Data Integration Breaks Down

The integration itself usually isn't the hard part. What breaks it is what happens after it's built:

• Schema drift – a source system adds, renames, or removes a field without warning, and the mapping built against the old shape silently produces wrong results instead of failing loudly. See schema drift for how teams catch this before it reaches a report.
• Conflicting sources of truth – two systems disagree on the same customer's email address or the same order's status, and nobody defined which one wins.
• The manual-mapping backlog – every new source or field change generates a mapping task, and in most teams that queue only grows, because writing and testing a schema mapping by hand takes real engineering time regardless of how simple the change looks.

What's Changing in Data Integration

Most integration work is still manual. Someone maps a schema, writes the transform logic, tests it, then fixes it when a source system changes something upstream without warning. That's the part AI agents are starting to take over. Instead of an engineer writing every schema mapping by hand, an agent can read the source and target structures, propose the mapping, and flag exceptions for a person to review.

This doesn't make the discipline less important. If anything, the more AI-driven decisions a business makes, the less tolerance there is for integration work that's slow or brittle. Full detail on what actually changes when agents take this on is in what actually changes when agents do the work.

How Maia Handles Data Integration

Maia's agents read source and target schemas directly and propose the mapping, rather than requiring an engineer to write it field by field. When a source changes shape, agents flag the drift and adjust the mapping under the approval checkpoints you've set, instead of letting the pipeline fail silently or waiting for someone to notice. Maia's connector library covers the sources most teams integrate against out of the box, with agentic support for building a custom connector where it doesn't, so the integration layer keeps up with the business instead of becoming the thing that's always a step behind it.