Data exists in a lot of places: customer relationship management (CRM) systems, finance platforms, product analytics tools, support software, and data warehouses. While each system does its job well, it can’t access the others’ information, which leaves a gap in analysis. That gap has become harder to ignore as organizations rely more heavily on shared data: in a 2025 study, 68% of surveyed chief executive officers (CEOs) said an integrated enterprise-wide data architecture is necessary for cross-functional collaboration and innovation.

Data integration is the process of consolidating data into one consistent and easily referenced place. Below, we’ll discuss the common data integration methods, their use cases, and how to think about governance once data starts flowing across systems.

Key takeaways

• Data integration combines data from multiple source systems into a consistent view. The real work is reconciling differences in field names, identifiers, and business logic.

• Your choice of integration method depends on how fresh your data needs to be, how complicated the transformations are, and what the destination environment looks like.

• When payments data is synced directly from your payment provider, you avoid third-party connectors’ security and maintenance tradeoffs.

What is data integration?

Data integration is the process of combining data from multiple source systems into a single, consistent view that teams can use for reporting, analysis, and decision-making.

What are the common methods for data integration?

The right data integration pattern depends on the data you’re moving, how fresh it needs to be, and what you’re doing with it once it arrives. These are the approaches you’ll encounter most often.

Extract, transform, load (ETL)

ETL is the most traditional data integration method. It pulls data from the source, reshapes it to fit your destination schema, then loads it. It works well when transformations are complex, but transformation logic lives outside the warehouse, which makes it harder to audit and change.

Extract, load, transform (ELT)

ELT makes it so raw data lands in the warehouse first, and transformations happen there using Structured Query Language (SQL). This has become the dominant pattern for modern analytics pipelines because warehouses such as BigQuery, Snowflake, and Redshift are affordable enough to store raw data and powerful enough to transform it at scale.

Streaming integration

With streaming integration, events flow continuously from source to destination, sometimes within seconds, instead of moving in scheduled batches. Payment events, clickstreams, and fraud signals are common candidates.

Data virtualization

In a data virtualization model, rather than moving data, a unified query layer sits above multiple sources, so data remains where it is but behaves like one dataset. It’s useful for ad hoc analysis but less suited to heavy, repeated workloads.

API-based integration

Application programming interface (API)-based integrations call an API to pull records and push them elsewhere. It’s flexible but requires custom work to reliably handle pagination, rate limits, schema changes, and failures.

What are the main use cases for data integration?

Data integration projects tend to cluster around a handful of high-value problems. Here are the ones that often come up.

Analytics and business intelligence (BI) reporting

Teams need data from multiple systems in one place to build dashboards, run ad hoc queries, and generate reports. A finance team that reconciles revenue across regions, a product team that tracks activation funnels, and a growth team that analyzes cohort retention will all need to access data from more than one system.

Data replication

Copying production database tables to a separate analytics environment protects production performance and lets analysts query without locking rows or degrading the systems customers depend on. This is often necessary in operations even before it becomes an analytics strategy.

Data warehousing

Rather than mirroring production tables, a warehouse is purpose-built for analytics, denormalized, optimized for reads, and typically stores years of historical data across many source systems. Mature analytics stacks are typically built around a central warehouse that integrates data from customer relationship management (CRM), enterprise resource planning (ERP), and payment systems.

Artificial intelligence (AI) and machine learning (ML)

Training a churn model requires customer history, product usage, and payment behavior in one dataset. Fraud models need transaction patterns, device signals, and account activity together. The quality of an ML model is often limited less by the algorithm and more by how complete and clean the training data is.

How does data integration work in practice?

While the mechanics vary by method, pipelines often share a common structure. Here’s how it works.

Source connection

First, you establish a connection to the source through direct database access, an API, a webhook, or a file export. The source system determines what’s available: some expose rich, real-time APIs, while others offer only nightly comma-separated values (CSV) dumps.

Extraction

Batch pipelines typically query for records that changed since the last run. They use a timestamp or change data capture (CDC) to avoid pulling everything each time. CDC tracks changes (e.g., inserts, updates, deletes) at the database level, which is more reliable than relying on application-level timestamps that processes might miss.

Transformation

Field mapping, deduplication, type casting, and business logic are applied at this stage. It’s also where integration often breaks. A schema change upstream, an unexpected null value, or a new record type the pipeline wasn’t built to handle can all corrupt downstream reports.

Loading

Incremental loads append or upsert new records, whereas full refreshes replace the entire dataset. Incremental loads are usually preferable for performance and cost, but they require the source data to be reliable enough to trust that no historical records were changed silently.

Orchestration

Tools such as Airflow, data build tool (dbt), or Prefect schedule runs, manage dependencies between jobs, handle retries, and alert when something fails. At scale, orchestration becomes as important as the pipeline logic itself.

What is the difference between application integration and data integration?

Application integration is the process of connecting systems so they can work together in real time to improve operations. For example, when a customer completes a purchase, your CRM automatically creates a contact, your fulfillment system receives a new order, and your email platform sends a confirmation. Flows are transactional, event-driven, and often bidirectional.

Data integration moves data for analytical purposes, typically in one direction: from operations systems into an analytics environment. It’s optimized for query performance rather than transactional responsiveness, and prioritizes completeness, historical depth, and consistency across sources.

A Kafka stream (i.e., a continuous flow of event data between systems) that feeds both a real-time operations dashboard and a data warehouse enables simultaneous application and data integration. The distinction matters most when you’re choosing a direction: if you need two systems to coordinate on a live transaction, that’s an application integration problem. But if you need to analyze three years of data from five source systems, you need to focus on data integration.

How should you think about data governance in integrated environments?

When data lives in one system, the system’s own access controls and audit logs handle most of it. But when you integrate across systems, you inherit each system’s inconsistencies and expose data to more people and processes than it was originally designed for. Here’s what you need to be aware of.

Consistent definitions

Revenue recognized on the invoice date versus the payment date is a definition problem. Integrated environments need agreed-upon definitions that are documented, enforced in transformation logic, and visible to anyone querying the data. Without consistent definitions, different teams produce different numbers from the same dataset.

Access controls

Integration often means sensitive data (e.g., personally identifiable information, financial records, health information) moves into environments with broader access than the source systems. Row-level security, column masking, and role-based access need to be designed into the warehouse from the start.

Data lineage

When a metric looks wrong, you need to trace it back through every transformation to find where it broke. Lineage tooling that’s built into platforms such as dbt or that’s available via standalone tools makes this possible without reconstructing the pipeline from memory.

Auditability

You must be able to locate where a number comes from, how it was calculated, and who changed it. That is especially important in regulated industries, but it also supports day-to-day reporting, reconciliations, and internal accountability.

Refresh transparency

Stale data that looks current is worse than clearly labeled stale data. If your warehouse updates once a day, that needs to be visible to anyone building reports from it.

How does a payment provider fit into a data integration strategy?

A payment provider uses its own system and data model to handle charges, refunds, disputes, payouts, customers, and subscriptions. Getting that data into a warehouse requires work.

Here are a few options for how to get it done.

Custom connector

You build and maintain this yourself, which means you’re responsible for API pagination, rate limits, schema versioning, and incremental syncing. While it’s flexible, it can be expensive to maintain, and any upstream API change can cause problems you might not detect immediately.

Third-party ETLs

These are faster to set up, but add another vendor with access to sensitive financial data, which creates both a security surface and a compliance consideration worth taking seriously.

Stripe Data Pipeline

Stripe Data Pipeline syncs Stripe data directly to a destination warehouse or cloud storage. A few things distinguish it from the alternatives for this specific use case:

• Data completeness: Historical records are included from the start, so you’re not limited to data from the integration date forward. The sync also includes additional Stripe-specific datasets and prebuilt financial reports that aren’t always available through third-party connectors.

• Reduced security exposure: Data moves directly from Stripe to your warehouse, so sensitive financial records don’t pass through an additional intermediary.