Modern examples of data integration approaches that actually work

Real-world examples of data integration approaches in action

When people ask for examples of examples of data integration approaches, they usually want more than “use ETL” or “buy an iPaaS.” They want to know what other teams are actually doing: which tools, which patterns, and which trade-offs.

Let’s walk through several examples of data integration approaches you’ll see in modern data stacks, using concrete scenarios instead of abstract theory.

Example of batch ETL from SaaS apps into a cloud data warehouse

One of the best-known examples of data integration approaches is the classic batch ETL pipeline feeding a cloud data warehouse.

Imagine a mid-size ecommerce company:

• Orders live in Shopify
• Marketing data sits in Google Ads and Meta Ads
• Customer support tickets live in Zendesk
• Finance uses NetSuite

The data team sets up nightly pipelines that:

• Extract data from each SaaS app via APIs
• Transform it into a common schema (customer, order, campaign, ticket)
• Load it into a warehouse such as Snowflake, BigQuery, or Amazon Redshift

This ETL example of integration supports use cases like:

• Revenue and margin reporting by channel
• Cohort analysis for marketing
• Support volume and SLA tracking

Why teams still use this approach:

• Predictable, scheduled loads work fine when “yesterday’s data” is timely enough
• Warehouses handle large volumes at reasonable cost
• SQL skills are widely available

Typical 2024–2025 tools for this example include Fivetran, Airbyte, dbt, and managed warehouses from the major cloud providers. The pattern is mature and boring in the best way: it’s stable, well-documented, and easy to hire for.

CDC-based replication: example of near real-time integration from OLTP to analytics

When executives want “up-to-the-minute” dashboards, simple nightly ETL stops being enough. A common example of data integration approaches for this problem is change data capture (CDC) from transactional databases into analytics systems.

Picture a health-tech platform with a multi-tenant PostgreSQL database. Product usage and patient engagement reports need to be updated every few minutes, not once a day. Instead of hammering the app database with heavy queries, the team:

• Reads the database write-ahead log (WAL) using CDC tools
• Streams inserts, updates, and deletes into Kafka or a similar message bus
• Applies transformations in-flight
• Writes the results into a warehouse or lakehouse

This example of integration gives:

• Fresh analytics without overloading production systems
• A clear audit trail of changes
• Better alignment between app events and analytics events

Modern stacks often combine Debezium or cloud-native CDC services (like AWS DMS) with Kafka, Snowflake, or Databricks. For regulated industries such as healthcare, this pattern also supports stronger observability and lineage, which aligns with guidance from agencies like the U.S. Office of the National Coordinator for Health Information Technology (ONC) on data interoperability (healthit.gov).

API aggregation layer: example of integrating multiple systems into a single interface

Not all examples of data integration approaches are about analytics. Sometimes the goal is to give applications a single, consistent way to talk to many back-end systems.

Take a logistics company that needs to pull:

• Shipment status from a legacy on-prem system
• Carrier updates from external APIs
• Customer data from a CRM

Instead of letting every app call everything directly, the engineering team builds an API aggregation layer:

• A gateway (often GraphQL or an API gateway) exposes a unified schema
• The gateway fans out requests to underlying REST, SOAP, or gRPC services
• The layer handles auth, throttling, and response normalization

Examples include:

• A React web app querying a GraphQL endpoint that behind the scenes calls Salesforce, SAP, and an internal tracking system
• A mobile app hitting a single /customer endpoint that aggregates identity, orders, and support history

This example of integration prioritizes developer experience and security. Instead of teaching every team the details of every system, the API layer becomes the contract. In 2025, this pattern is especially common with microservices and B2B SaaS platforms.

Event streaming: examples include clickstream and IoT data pipelines

If you’re dealing with high-volume, time-sensitive data, streaming is often the right answer. Some of the best examples of data integration approaches today are built around event streaming.

Consider two scenarios:

Clickstream analytics for an online media site

• Every page view, click, and scroll event is published to Kafka or Amazon Kinesis
• A stream processing engine (like Apache Flink) enriches events with user attributes and campaign metadata
• The enriched stream is written to both a real-time serving store (for personalization) and a warehouse (for BI)

IoT monitoring for industrial equipment

• Sensors on machines publish telemetry (temperature, vibration, error codes) to an MQTT broker or Kafka
• A streaming job flags anomalies and forwards alerts to an incident system
• Aggregated metrics feed a time-series database and a data lake

These examples of streaming integration show why event-driven patterns are so popular:

• Low latency from event to insight or action
• Natural fit for time-series and behavioral data
• Easier to scale horizontally compared to batch-only jobs

Organizations in energy, manufacturing, and transportation often pair these patterns with public-sector or research data. For example, smart-city projects may integrate local sensor data with environmental datasets from agencies like the U.S. Environmental Protection Agency (epa.gov).

Data virtualization: example of “query where it lives” without heavy movement

Sometimes copying data everywhere is not desirable or even allowed. A different example of data integration approaches is data virtualization, where you query multiple sources as if they were one, without physically consolidating them.

Imagine a multinational bank:

• Customer data is scattered across regional databases due to regulatory constraints
• Some systems are on-prem, others in multiple clouds
• Moving everything into one warehouse would create legal and operational headaches

A data virtualization layer:

• Connects to each source (databases, warehouses, files)
• Exposes a single logical catalog
• Pushes down queries when possible and stitches results together

Examples include:

• A risk analyst running a single SQL query that joins European and U.S. customer exposures, even though those tables live in different regions and engines
• A compliance officer querying both transactional systems and archival stores through one interface

This example of integration trades raw performance for governance and flexibility. It’s particularly attractive in regulated sectors like finance and healthcare, where data residency and privacy rules matter. Guidance from organizations such as the National Institutes of Health on protecting personal health information (nih.gov) often pushes teams toward approaches that minimize unnecessary data duplication.

Reverse ETL: examples include syncing warehouse data back into SaaS tools

For years, data flowed in one direction: from apps into the warehouse. One of the newer examples of data integration approaches flips that flow. Reverse ETL takes modeled data from the warehouse and syncs it back into operational tools.

Picture a B2B SaaS company that has:

• A well-modeled warehouse with customer health scores
• Salesforce for CRM
• HubSpot for marketing
• Intercom for support

The data team defines a “customer health” model in dbt, then uses reverse ETL to:

• Push health scores into Salesforce as a custom field
• Sync product usage segments into HubSpot lists
• Feed churn-risk tags into Intercom for proactive outreach

Now the warehouse stops being a reporting-only graveyard and becomes a source of operational intelligence. This example of integration is a big reason why the “modern data stack” conversation in 2024–2025 includes not just ingestion but activation.

iPaaS workflows: examples of low-code integration for business teams

Integration platform as a service (iPaaS) tools give non-developers a way to wire systems together using visual workflows. These are some of the most relatable examples of data integration approaches for business teams.

Consider a marketing ops scenario:

• Leads arrive via a web form
• The team wants to enrich them with firmographic data
• Qualified leads should go to Salesforce; unqualified ones to a nurturing campaign

With an iPaaS tool, the workflow might:

• Trigger on a new form submission
• Call an enrichment API
• Apply routing rules based on company size and industry
• Write records into the right systems and send alerts in Slack or Teams

Examples include HR workflows that sync new hires from an HRIS into identity providers and payroll, or finance workflows that move invoice data from billing platforms into ERP systems.

This example of integration trades raw performance and full customizability for speed of implementation and accessibility. It’s popular in small and mid-size organizations where IT headcount is limited, or where the priority is automating repetitive tasks rather than building a high-throughput data platform.

Data lakehouse and ELT: example of schema-on-read integration at scale

As data volumes grow and formats multiply (JSON, Parquet, images, logs), many teams move beyond traditional warehouses toward lakehouse architectures. A modern example of data integration approaches here is ELT into a data lakehouse.

Picture a media streaming service:

• Raw events (plays, pauses, device info) land as files in cloud object storage
• Batch and streaming jobs load this data into a lakehouse engine (like Databricks or Snowflake with external tables)
• Transformations happen inside the lakehouse using SQL and notebooks

This example of integration leans on:

• Cheap storage for raw data retention
• Schema evolution and late-binding schemas
• A mix of batch and streaming ingestion

In 2025, this approach is common where organizations want both BI and data science on the same foundation. It supports machine learning workflows, experimentation, and long-term historical analysis without constant schema redesign.

Choosing between these examples of data integration approaches

Seeing multiple examples of data integration approaches side by side helps clarify that there isn’t a single “right” pattern. The better question is: which example of integration best fits your constraints?

A few practical guidelines:

• If your main need is reporting and dashboards, batch ETL/ELT into a warehouse or lakehouse is usually the first move.
• If latency matters (fraud detection, personalization, operational monitoring), look hard at event streaming and CDC.
• If governance and data residency dominate your world, data virtualization and regionalized architectures are worth the complexity.
• If you’re trying to empower business teams, iPaaS and reverse ETL provide fast wins without deep engineering.

Most mature organizations end up with a hybrid of these examples: batch and streaming, warehouse and lakehouse, APIs and files. The key is to be explicit about which example of integration you’re using for which use case, instead of letting a random mix of patterns accumulate over time.

FAQ: common questions about examples of data integration approaches

What are some common examples of data integration approaches in modern data stacks?

Common examples of data integration approaches include batch ETL into a cloud data warehouse, CDC-based replication from OLTP systems, event streaming with platforms like Kafka, data virtualization for cross-source querying, reverse ETL from warehouses into SaaS tools, iPaaS workflows for low-code integration, and ELT into a data lakehouse.

Can you give an example of real-time data integration?

A straightforward example of real-time integration is using CDC on a PostgreSQL database to stream changes into Kafka, enriching events, and then updating both a dashboard and a cache that powers in-app metrics. Users see their data update within seconds, while the transactional database stays protected from heavy analytical queries.

Which examples of approaches work best for regulated industries like healthcare?

In healthcare and similar regulated sectors, examples of data integration approaches that emphasize governance tend to work best. Data virtualization, regionalized warehouses, and tightly audited CDC pipelines are common. Organizations often align their strategies with guidance from bodies like the Office for Civil Rights at HHS and research-driven best practices from institutions such as the National Institutes of Health (nih.gov) and academic medical centers like Mayo Clinic (mayoclinic.org).

How do I decide between ETL and ELT?

If your transformations are stable and your sources are relatively structured, ETL can simplify operations by loading only modeled data. If your sources are messy, semi-structured, or evolving fast, ELT into a warehouse or lakehouse lets you keep raw data and iterate on models later. Many teams use both patterns, depending on the system.

Are low-code tools good examples of enterprise-grade integration?

They can be, with caveats. iPaaS tools are strong examples of integration for workflow automation, SaaS-to-SaaS sync, and department-level projects. For very high-volume, low-latency, or deeply customized pipelines, engineering-led approaches with code, streaming platforms, and orchestration frameworks are usually a better fit.