In this data integration primer, we'll cover foundational concepts, common pitfalls, and real-world use cases.

For tool-specific reviews, check our Top Data Integration Platform Comparison & Decision Guide.

What is data integration?

Data integration connects disparate systems so teams can view, access, and act on accurate, unified data. Instead of chasing down numbers across tools, people get the data they need without writing custom scripts or waiting on someone from IT.

Most teams rely on a mix of systems:

• Sales logs activity in Salesforce
• Finance tracks billing in QuickBooks
• Marketing pulls performance data from Google Ads, LinkedIn, and email tools

Without integration, those systems stay disconnected. Sales has no idea how renewals are trending. Finance works off numbers that don’t match what marketing sees. Reporting takes longer, and no one’s confident the numbers are accurate.

Integrated data changes that. Teams can connect tools, sync updates automatically, and pull reports from one place. Marketing can see campaign results as they happen. Finance can compare forecasts to live revenue. Product managers can spot patterns in user behavior and support tickets without needing a spreadsheet wizard to make it all work.

This setup still requires planning, though.

The workflow behind data integration

Data integration is a structured process consisting of distinct steps. Each one converts raw, fragmented data into something usable across teams.

The diagram below walks through the core stages, from source to target. The goal isn’t just moving the data. It’s preparing it to answer real business questions without needing to be re-cleaned or re-verified downstream.

The phases below turn raw data into useful information. Each step addresses a specific challenge, such as cleaning formats, removing errors, or syncing updates.

• Find your data: Start by locating it across the business. You might have customer records in MySQL, ad spend in Google Ads, and an older purchase history in a finance system that no one has touched in years. Cloud storage buckets, forgotten spreadsheets, or SaaS exports all count.
• Pull it together: Match the method to the source. APIs work for many modern tools. Some databases connect through ready-made connectors, while others only allow flat file transfers. If only part of a table changes, change data capture (CDC) avoids reloading the whole thing.
• Map it: Make sure fields line up before loading. “cust_id” in a CRM needs to match “customer_id” in the order history, or you’ll end up with 2 records for the same person.

• Validate: Look for duplicates, missing values, or format mismatches before the data moves further.
• Transform: Rework raw inputs into something analytics-ready. That might mean converting dates into a single format, breaking apart nested JSON, or merging product codes from different regions into one standard set.
• Add metadata: Record the data source, owner, and last modified date. Teams need this context when tracking issues or making compliance checks.

• Load it: Send the processed data to its destination — maybe a cloud data warehouse like Snowflake, a shared data lake, or an application database.
• Sync it: Schedule regular updates or set up real-time replication so reports stay current.
• Secure it: Use encryption, access controls, and compliance safeguards like masking personal data before storage.
• Make it actionable: Hook the data into dashboards, reporting tools, or machine learning jobs so it informs day-to-day decisions, not just end-of-quarter reviews.

Data integration techniques and approaches

Data integration involves various techniques and technologies suited to different needs and environments. Businesses that understand these different data integration approaches are better equipped to choose the right strategy for effectively combining and using their data.

Batch-based processing

This approach moves data in scheduled chunks rather than continuously. It’s a go-to for use cases like nightly analytics, reporting, and data warehousing — and it comes in 2 models: ETL and ELT.

Data streaming integration

Data streaming integration processes data continuously as it arrives, rather than in batches. It’s key for analytics and monitoring systems that depend on current information. Businesses can react fast when something changes, whether a fraud attempt or a network issue. You’ll see this approach everywhere: fraud detection, live performance tracking, even personalized customer interactions.

Application integration

Application integration connects different software systems so they can share data automatically. APIs send updates between tools when events happen, like when a new customer is added in a CRM, and that record instantly appears in the billing platform. This reduces duplicate entries and prevents delays from manual updates. With the systems synced, teams can access the same data from within their own tools without switching between platforms or merging files by hand.

Data virtualization

Data virtualization allows users to access and query data from multiple sources without copying or transferring it. This approach provides a virtual view, enabling quick and efficient data access. It’s ideal for bringing together data from multiple systems without the overhead of data consolidation. Data virtualization helps businesses streamline data management and improve accessibility for analysis and decision-making.

Challenges of data integration

Integrating data from dozens of systems sounds like wiring things together. In reality, a mix of technical, process, and governance challenges can slow projects and erode trust in the results.

Data quality issues

Integrating poor-quality data just centralizes the problems. Inconsistent field formats, missing product IDs, or outdated contact details travel straight into the warehouse unless caught early.

Worse, decisions sometimes get made before anyone spots the issues. A sudden spike in revenue on a dashboard could look like a win — until someone realises it’s just duplicate transactions counted twice. That erodes confidence in every report, even the accurate ones.

Real-time processing strain

Keeping systems in sync often involves event streaming or change data capture (CDC), where every new transaction, update, or click is sent through the pipeline as it happens. This keeps analytics fresh but also means there’s no break in the flow. Without infrastructure that can handle constant throughput, queues start to build.

Dashboards and alerts often lag behind reality. When a problem finally becomes known, the quick response window might have passed.

Security and governance risks

Every additional integration point is another pathway into company data. As more users and applications connect, the chances of accidental exposure or misuse grow. Role-based permissions, encryption, and detailed access logs are essential, but keeping them consistent across dozens of systems is a constant effort.

Any gap — even in a single connector — can lead to compliance issues under GDPR, HIPAA, or other regulations. Beyond fines, a breach or policy violation can damage customer trust in ways that are much harder to repair.

Key benefits of data integration

Data integration offers a host of benefits that change how businesses run and make decisions. By combining data from different sources, companies get a clearer, more complete picture of their information.

The proper data integration solution can boost the quality and accessibility of data, making it easier to use. It can also help streamline operations and support smarter, more strategic decisions.

Let's explore some of the key benefits that data integration brings to the table:

When these benefits work together, that can mean automating routine reporting, spotting issues as they happen, or launching analytics projects that weren’t possible before. Next, we’ll look at implementation.

Real-world use cases for data integration

When companies connect data from different systems, there are measurable benefits — faster decisions, lower costs, and higher productivity.

Let’s examine examples of how a unified approach changes what’s possible.

Retail: From month-long data delays to live inventory visibility

Saks was running on dozens of custom ETL pipelines that required weeks to connect a new data source and often delayed reporting. This limited how quickly the business could respond to inventory trends or shifts in customer demand. After moving to Fivetran, Snowflake, and dbt, the team onboarded 35 data sources in 6 months — a pace that would have taken more than a year with their old setup.

Now, data refreshes every 5 minutes, so teams have visibility into stock levels. If a product starts selling faster than expected, marketing can adjust promotions immediately, and operations can plan replenishment before it sells out. The modern stack also cuts engineering workload by up to 80%, allowing the same small team to focus on building AI-powered customer service tools and vendor-facing data marts. These tools give brand partners direct access to metrics, reducing back-and-forth requests.

Healthcare: Cutting clinical trial data processing from hours to minutes

Pfizer’s clinical trial data was spread across IoT devices, EHR systems, and a 20-year-old warehouse that couldn’t deliver real-time access. The complexity of this setup slowed insights for manufacturing, quality control, and trial monitoring. Using Fivetran to replicate the warehouse to Snowflake, Pfizer reduced some processing jobs from hours to minutes.

Now, researchers can view all relevant trial data in one place without adding load to legacy systems. This speed allows teams to spot manufacturing issues sooner, ensure trial materials are in place, and make adjustments that keep trials on schedule. The result is a more responsive research pipeline, essential when working on life-saving treatments.

Financial Services – Halving data ingestion costs while boosting AI performance

National Australia Bank ran over 200 siloed data sources on costly, failure-prone legacy systems. By shifting to a Fivetran & Databricks lakehouse, ingestion costs dropped by 50% and machine learning models ran 30% faster. Real-time CDC feeds now power fraud detection systems, enabling suspicious transactions to be flagged as they happen.

The same pipelines drive AI-led document review, cutting trust deed processing from 45 to 5 minutes — saving roughly 10,000 hours annually. These changes give NAB a secure, scalable foundation for new AI initiatives while reducing operational overhead.

Data integration solutions: What to consider

We’ve already looked at what features to compare across data integration tools. Now it’s time to narrow the list to the one that fits your business best. These questions will help you make a confident choice.

• Are most of your sources cloud-based (SaaS apps, cloud databases)?
  
  
  
  • Yes → Consider a cloud-native ELT platform with prebuilt connectors.
    
    
  • No → Go to the next question.
    
    
• Do you rely on legacy or on-prem systems?
  
  
  
  • Yes → Consider an ETL tool with strong transformation logic and API flexibility.
    
    
  • No → Consider a hybrid ETL/ELT orchestrator for mixed environments

• Do you need real-time or near-real-time updates?
  
  
  
  • Yes → Consider a streaming ELT platform with change data capture (CDC)/event triggers.
    
    
  • No → Go to the next question.
    
    
• Are daily or scheduled updates sufficient?
  
  
  
  • Yes → Consider a batch ELT/ETL tool.
    
    
  • No → Reassess data latency requirements

• Does your team have limited engineering bandwidth?
  
  
  
  • Yes → Consider a fully managed, no-code solution.
    
    
  • No → Go to the next question.
    
    
• Do you need full control over pipeline logic?
  
  
  
  • Yes → Consider a self-hosted option.
  • No → Consider an open-source ELT toolkit with community support

Automate data integration with Fivetran

Instead of engineering custom pipelines to connect every internal system, most teams rely on off-the-shelf data integration tools that can reliably move and sync information across dozens of applications.

Fivetran is one of the more widely used options. It offers prebuilt connectors for platforms like Salesforce, NetSuite, Google Ads, and Snowflake. These connectors continuously pull in updated data and automatically adjust to schema changes. The pipeline works without manual intervention when a source table adds or renames a field.

Fivetran allows teams to:

• Monitor campaign performance, sales pipelines, or revenue trends in unified dashboards without exporting and merging spreadsheets.
• Track ad spend and lead volume in one place.
• Pull near-real-time CRM data for revenue analysis based on
• Maximize uptime and minimize engineering overhead
• Automatically logs sync activity and errors across connectors to pinpoint issues.
• Support strict data policies, security, and compliance efforts with data masking, role-based access controls, and audit logs.

When teams bring together data from marketing, sales, and support, they can pinpoint which campaigns drive the most valuable customers and shift budgets quickly to maximize returns.

Achieving that level of insight depends on accurate, up-to-date data from every source. Platforms like Fivetran can help by automatically moving data from hundreds of apps and databases into a central warehouse, where it’s ready for analysis.

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