The 8 Types of Data Integration

Data integration is crucial in today’s business world. Business data comes via many sources, from internal databases to clicks on a website. Being able to access all your data in one place helps your business make better, faster decisions. But how do you integrate all your data, and what’s the best way to do it? Here we discuss eight data integration methods, how they work and why businesses continue to choose them.

1. Manual Data Integration

The most basic — yet in some ways the most heroic — of data integration techniques is manual integration. Manual data integration requires that your business employ a dedicated data engineer who individually manages and codes data connections in real-time. A data engineer has to make the right connection to each data source, clean and reorganize the data as needed, and manually transfer it to a desirable destination.

Pros

• Absolute control over data integration and management
• If an organization already has a data engineer or data management team, there are no additional costs associated with manual data integration

Cons

• Other members of the business may find it hard to access or analyze data
• Scalability is difficult without hiring more staff
• Errors are likely with complex data connections or big data queries
• Data engineers must individually and manually code connectors to different data sources every time a new system needs to be accessed

2. Data Integration with Middleware

Middleware, as the name suggests, is software that sits between applications to transfer data. A middleware data integration solution transfers data from a range of applications to databases. Usually automated, middleware can often perform the transformations that make data compatible with new systems. This helps if your business is moving from older legacy systems to a range of modern applications, and it creates a network of information accessible by anyone in your organization.

Pros

• Your network systems should communicate better with effective middleware
• Enterprise data automatically transforms and transfers in a consistent manner

Cons

• Enterprises require a skilled developer to install and maintain middleware, which can increase operational costs
• Not all systems are compatible with middleware

3. Uniform Data Access Integration (UDAI)/ Data Virtualization

This method of integration focuses on displaying the data in a consistent format for ease of use while actually keeping the source data at its original destination. Think of it as a translation app for a multitude of languages that are actually dozens of unique sources of business data. Data replication occurs as you view it, but the data always remains at the original source.

Pros

• A simple and unified view of data
• Allows multiple systems or apps to connect to one central source
• No high-storage requirement

Cons

• Accessing data from within source systems causes frequent data access requests, which can put a strain on the data host systems, limit the functionality or lead to latency
• Having multiple data access points can compromise data integrity and data quality

4. Common Storage Data Integration

As business data becomes more complex and abundant, the common storage option is an integration approach that many enterprises take. Similar in principle to uniform access, the information undergoes data transformation before it's copied to a data warehouse, so your systems only have to access one data source, not hundreds. Because the data is in one place and accessible at any time, you can run analytics and business intelligence (BI) tools as-is and when you need to. Common storage integration is the basis for many data warehouses, the common name for most modern data storehouses.

Pros

• Less strain on data host systems, as it holds data sets in one destination
• Uniform data appearance to streamline analytics
• Higher data integrity
• More opportunity for effective analytics

Cons

• Costs for data storage could increase as data volumes bump up
• Unless you use a third-party data warehouse, there are maintenance costs

5. Application-Based Data Integration Tools

Application-based integration software effectively locates, retrieves, and transforms your data, and then integrates it into your desired destination. This often involves automation, pre-built connections to a variety of data sources, and the ability to connect to additional data sources when necessary.

Pros

• Seamless data transfer and exchange of information between different systems and destinations
• Easy on resources thanks to automation
• Scalable as the amount of data you consume increases
• Simple to use and doesn’t always require a technical expert

Cons

• If you are using on-premise software, you may need an expensive technical expert on-site

6. ELT (Extract, Load, Transform)

A modern take on the ETL model, ELT flips the script by loading raw data into a data warehouse first and transforming it there. This method is ideal for cloud-based environments where warehouses like Snowflake or BigQuery can handle large-scale data processing.

Pros

• Faster ingestion of raw data

• Takes full advantage of modern data warehouses

• Great for large-scale analytics

Cons

• Requires advanced SQL skills for transformations

• Dependent on the performance and cost structure of the warehouse

Now, let's go through the key differences between middle ware based integration and ELT as it can be confusing.

7. Change Data Capture (CDC)

CDC continuously monitors data sources for changes and only moves the new or updated data to your destination. This method is crucial for real-time analytics and use cases where up-to-date data is essential.

Pros

• Real-time data syncing and updates

• Reduces system strain by transferring only new data

Cons

• Requires detailed configuration and monitoring

• May rely on log access or triggers, which not all systems support

8. Data Federation

Similar to virtualization, data federation allows you to query and interact with multiple data sources as if they were a single source. It’s particularly useful when integrating heterogeneous systems without physically combining their data.

Pros

• Simplifies data access across multiple platforms

• No need for data duplication

Cons

• Can be slower than centralized data models

• Relies on real-time access, which can introduce latency

Integrate.io and Data Integration

Integrate.io offers an easy way to integrate and manage your business data. Our Extract, Transform, Load (ETL) data integration platform allows anyone, regardless of technical experience, to create effective data pipelines using our low-code platform.

Why Choose Integrate.io for Any Data Integration Need

Content:

• Integrate.io supports all major integration patterns—ETL, ELT, CDC, Reverse ETL

• 220+ low-code transformations for cleaning, masking, filtering, and joining data

• 200+ native connectors for SaaS apps, cloud storage, and databases

• Enterprise-grade security: SOC 2, GDPR, HIPAA, and field-level encryption

• Fully managed platform with white-glove onboarding and 24/7 support

The ETL process and the huge number of integrations we support make Integrate.io an essential part of your data stack for any number of use cases. Fully elastic and scalable, Integrate.io takes the hard work out of data integration and consolidation, leaving you free to work with high-quality, accurate data for better business analytics and profit-boosting insights. Schedule a conversation to discuss a 14-day demo, and let us show you how it works.