What Is Cloud Data Integration and How Does It Work?

Cloud data integration is the practice of connecting and moving data between cloud applications, databases, and on-premises systems through managed pipelines, so that every app, analytics tool, and AI agent works from consistent, trusted data. It fits organizations that have data spread across multiple SaaS apps, cloud warehouses, and legacy systems and need that data unified without building and maintaining every connection by hand. Informatica, now part of Salesforce, describes its platform as connecting, cleaning, and governing data across 50,000+ sources with 300+ connectors in a single pipeline.

The core mechanics

A cloud data integration pipeline has four moving parts. Understanding them helps you evaluate any vendor's claims.

1. Connectors — prebuilt adapters that read from and write to specific sources (SaaS apps, databases, warehouses, files). Informatica cites 300+ connectors for data integration.
2. Ingestion — pulling data from sources, either in bulk (batch) or continuously (change data capture / streaming).
3. Transformation — reshaping, joining, deduplicating, and cleaning data so it matches the target schema and business rules. This is where ETL and ELT differ (see below).
4. Loading — writing the result into a cloud target such as a data warehouse or lake, where analytics and AI workloads consume it.

The pipeline is "managed" when the vendor handles scheduling, scaling, error handling, and monitoring rather than your team operating servers.

ETL vs. ELT in the cloud

Informatica positions both cloud-native ETL and ELT as part of its platform, which matters if you expect to mix patterns across use cases rather than standardize on one.

Cloud-native vs. traditional on-premises integration

Traditional integration tools were built to run on servers you own, with capacity you provision ahead of time. Cloud-native integration runs as a managed service: capacity scales with workload, connectors are updated by the vendor, and pipelines are configured rather than coded from scratch.

Choose cloud-native when your sources and targets are predominantly cloud, your volumes fluctuate, and you want to avoid operating integration infrastructure. Traditional or hybrid approaches still fit when you have heavy on-premises systems, strict data-residency requirements, or existing investments you can't retire. Many organizations end up hybrid, which is why connector breadth across both worlds is a practical selection criterion.

Supporting capabilities that determine success

Integration alone moves data; it doesn't make it trustworthy. The capabilities below are what separate a pipeline that survives contact with production from one that doesn't. Informatica groups these as data quality and observability (automatic profiling, cleaning, and monitoring), governance (knowing what data you have, where it came from, and whether it's compliant), cataloging (tracking origin, movement, and usage), and master data management (one golden record for customers, products, and suppliers).

For a first project, prioritize quality checks and a catalog entry over advanced governance. You need to know a pipeline's output is correct before you need to prove it is auditable.

Planning a first integration use case

A workable sequence for a pilot:

• Pick one bounded use case with a clear consumer — for example, unifying customer records from a CRM and a support tool into a warehouse table that a dashboard reads.
• Inventory sources and targets, including schemas, refresh frequency, and data owners.
• Define the transformation rules in writing: which fields map where, how duplicates resolve, what "correct" looks like.
• Choose the pattern (batch ETL, ELT into the warehouse, or streaming) based on latency needs and where compute should live.
• Build quality checks into the pipeline, not after it — row counts, null rates, referential checks.
• Run in parallel with the existing process before cutting over, and compare outputs.
• Document lineage so the next pipeline reuses what you learned.

Common pitfalls

• Skipping the data model. Pipelines that "just move data" create duplicate, conflicting records downstream. Decide the target schema first.
• Underestimating source drift. SaaS APIs change; connectors need monitoring, and schema changes should alert rather than silently break loads.
• Treating governance as a later phase. Retrofitting lineage and access controls onto dozens of pipelines is far costlier than building them in from the start.
• Ignoring volume growth. A pilot on thousands of rows can behave very differently at millions; test at realistic scale.
• No ownership. Every pipeline needs a named owner for failures and schema decisions.

How to judge whether you need it

You likely need cloud data integration if data lives in more than a handful of systems, teams rebuild the same extracts repeatedly, or AI and analytics initiatives are blocked by inconsistent data. You may not need a full platform yet if you have one source and one target with a stable, simple relationship — a scheduled script can suffice until complexity grows.

When comparing options, use the same dimensions: connector coverage for your actual sources, support for both ETL and ELT patterns, built-in quality and observability, governance and cataloging, and how much operational burden the vendor absorbs. Informatica's own positioning — 300+ connectors, 250+ intelligent cloud data services, and leadership placement in multiple Gartner Magic Quadrant reports including Data Integration — gives you a reference point for what an enterprise-grade offering claims to cover; verify each claim against your specific source list and requirements before deciding.