7 Best Data Integration Techniques In 2023

In this article, we’ll look at:

1. 7 different data integration techniques (with their tradeoffs and best practices when deploying them)
2. The best data integration platforms and tools to turn your chosen integration strategy into reality. 

7 types of data integration 

The data integration process allows us to connect to different data sources, collect data, clean it, and load data into a unified view (e.g. data warehouse or business intelligence tool) for business decision-making.

But not all data integration solutions are made the same. Pick the data integration approach that offers the best tradeoffs for your data management strategy.

Let’s look at the 7 different data integration methods, their advantages, common pitfalls to avoid, and best practices. 

Data integration technique #1: Manual data integration

What is it?

Data engineers hand-code scripts that manually integrate data from different data sources into your data store (typically a database).

Pros:

• Quick start. Hand-coded solutions are usually easy to launch since they can be prototyped fast.
• Customizable. The manual data integration process can be suited to your particular data integration needs and gives you full freedom to code the solution as you wish.

Cons:

• Time-consuming. When working with complex data sources or data storages, it can be time-consuming to write even the first integration script. Expect additional time eaters to knock on your door when it comes to maintenance and other overhead.
• Decreased scalability. Manual data integration is not made to scale. As the number of data sources grows and your data engineers need to re-tweak the manual scripts for different business use cases, the hand-coded solution hinders your company’s scaling.
• Increased chance of human error. Manual data integration scripts often introduce pesky bugs that eat into your engineering hours and lower data quality and security. 

Best practice: Use manual data integration for small projects, prototyping, or even just to test your solution against a shortlisted data integration platform provider. But don’t use this data integration approach for enterprise data or critical business processes.

Data integration technique #2: Enterprise application integration

What is it?

Enterprise application integration unifies data across all the apps that an enterprise uses. It usually involves sharing the data across applications to keep data consistent irrespective of the application (CRM, ERP, supply chain management, payroll, human resources apps, etc.).

Pros:

• Business oriented. This data integration approach is highly focused on enabling your non-data experts with the insights they need to create their best work. Enriching data across your application gives your workforce a competitive advantage.
• Customer-centered view. Enterprise application integration is especially advantageous for creating a holistic customer view. For example, by integrating your transaction datasets into your Salesforce CRM, your sales reps can see all the customer purchases in the CRM as they are upselling leads. 

Cons:

• Limited data access. Data is stored in the applications, not in a database or a data warehouse. The access to the data will be limited by the access to the specific apps.
• Overhead for non-popular sources. Data integration tools rarely cover all the apps you’re using in-house. Causing manual overhead to build an integration for less popular apps.

Best practice: Pick data integration platforms that offer connectors for the specific application your company is using. Not all data integration tools cover all the disparate sources your workforce relies on. As an advanced feature, check for general API integrations. 

For example, Keboola’s Generic Extractor and Generic Writer can collect data from any API source and send it to any API source. Making Keboola a universal enterprise application integration solution, irrespective of your specific applications.

Data integration technique #3: Data virtualization

What is it?

Data virtualization doesn’t move data from where it’s stored. Instead, it creates a virtual database across all the multiple data sources for data federation. This virtual layer acts as a single data access point for applications, queries, and reporting tools, providing a unified view of the data.

Pros:

• Simplified data access. Unifying all access through the data virtualization solution simplifies access and discoverability. All data is made available to the users, without needing to consolidate it first.
• Lower data transfer and storage costs. Because data isn’t collected from various sources and moved to a different data storage, your networking and storage costs are a fraction of the other data integration methods. 

Cons:

• Incomplete solution for data retrieval and usage. Data virtualization technology allows you to view the data, but not to move it, cleanse it, or perform any type of complex data transformation. You’ll need a separate solution for these use cases.
• No data consolidation. Data virtualization presents data “as-is”. There is no data cleansing or unification of data across data silos. This can introduce duplicate and erroneous data into your data analytics and business processes.
• Data governance hiccups. Data virtualization doesn’t land itself in data management and compliance. Without additional role-based access controls, security features, and user management functionalities, your data can be quickly exposed to unwanted eyes.

Best practice: Modern data integration methods do not use data virtualization as a standalone technique. Instead, data virtualization solutions are packaged as one of the features of data integration platforms. 

A sophisticated example would be Keboola’s Data Catalog. Use the Data Catalog to virtualize your data assets, give data access to collaborators (while keeping full governance), and document the data assets as well as the process that produces them in a single shareable experience.

Data integration technique #4: Middleware data integration

What is it?

Middleware data integration uses middleware software to transfer data from multiple source systems into a central data store. It’s commonly used to migrate pesky legacy systems to new systems. For example, customer data from an old CRM to a new data store.

Pros:

• Data integrity. Middleware data integration is highly specific by design - it validates and reformats data from the source systems to the target systems before integrating it. 
• Data streaming. A well-executed middleware data integration allows for real-time data streaming between the source systems and data storage. Keeping your information fresh for decision-making.

Cons:

• High skillset required. To integrate and map the various sources to the middleware, administrators of the middleware integration require specialized data engineering knowledge.
• Engineering overhead. Middleware data integrations need to be continuously monitored, deployed, and maintained by the data engineering team. Causing an opportunity cost for your engineering talent.
• Limited functionality. Middleware data integrations are usually highly-bespoke solutions, designed for specific use cases, and are hard to use across multiple data integration requirements.

Best practice: Middleware data integration methods are best suited for specific enterprise data needs. This method will mostly be the preferred choice for migrating between legacy systems and new systems, but not for other data integration needs. 

For example, middleware data integration is useful when an enterprise migrates from on-premise to cloud data warehouses and modern data integration platforms don't offer connectors for their legacy systems.

Data integration technique #5: Common storage integration (also called data warehousing)

What is it?

Common storage integration focuses on integrating data for business intelligence. Instead of piping data into any data storage, the data is integrated into a data warehouse, adhering to a predefined data model that is optimized for querying and data insights.

Pros:

• Faster data insights. Data warehouses are designed around business needs. Their data model streamlines the data analysts’ queries to provide insights faster.
• Increased data quality. Common storage integration sanitizes and validates data before integrating it into the data warehouse. Thus avoiding duplicate data, corrupted data, or otherwise invalid data, and keeping the data quality high.

Cons:

• Greater integration latency. The data transformations needed to cleanse and reshape the data before integrating it into the data warehouse introduce additional processing time that causes an integration latency. Thus this technique is often avoided for real-time data streaming but is more commonly used for on-demand business intelligence. 
• Data warehousing overhead. The data model needs to be constantly updated and re-defined according to changing business needs. Causing a domino effect, where you’ll need to change your common integration storage solutions to adhere to the data model changes.

Best practice: Pick tools that automate the data pipelines needed to extract, clean, and load data into the data warehouse. Even though you’ll still experience data warehousing overhead by managing and updating the data model, the integration of various data sources into the data model will be automated. Check the list below for the best-in-class data integration platform providers.

Data integration technique #6: Change Data Capture (CDC)

What is it?

Change Data Capture (CDC) is a data replication technique that makes a copy of the source data into your data storage. 

Pros:

• Faster data replication. CDC is optimized for streamlining data replication. It only moves the data that has been altered (added, deleted, updated) since the last data integration. Hence saving you networking costs during data movement and speeding up the overall data replication process. 
• Event-driven. CDC can be configured to fire at every source data change event. Making it a great ally for keeping data consistent between your source systems and data storage.

Cons:

• Limited to SQL sources. CDC is primarily designed to replicate data from SQL databases and data warehouses. It’s hard to generalize it to other data sources.
• No data transformation. CDC does one thing (data replication) and it does that one thing well. But it cannot be used to sanitize and cleanse data, or to provide more complex data transformation (e.g., prepare data for data analysis). 

Best practice: Use CDC as one of the many data integration approaches in your data stack arsenal. This data integration method is especially suited for big data sources, where the size of the data is a limiting factor for your integration operations. For example, Keboola’s CDC analyzes the binary log to determine events that changed the data since the last replication and extracts the new rows during replication for a lightweight, fast, and zero-maintenance data replication.

Data integration technique #7: ETL

What is it?

ETL is a data integration approach that develops specific and advanced techniques for each stage of this data integration process (extract, transform, load).

Pros:

• Customizable data extractors. ETL is not limited to any data source. From applications to SQL databases, ETL can integrate data from (theoretically) any source.
• Customizable data transformations. ETL tools are the most common leaders in the data transformation space. Offering solutions for advanced data transformations (aggregations, complex SQL or Python queries, machine learning filters, etc.), that are not often present in simple data integration platforms.  
• Customizable data storage. Unlike common integration storage (data warehousing), ETL tools can integrate data into one or more different data storages: from data lakes for unstructured data to BI tools directly for reverse ETL.

Cons:

• No data consolidation guarantee. Because ETL tools offer more customizability - the freedom to specify the source, transformations, and destinations yourself - there is no predefined data model or unified data view. To guarantee data quality, you’ll have to impose data management and governance rules alongside this technique. 
• Greater integration latency. Unlike CDC or middleware data integration, ETL paradigms suffer from the same latency as common data integration storage. The data transformation layer introduces latencies that make them poor candidates for real-time data integration. 

Best practice: ETL tools often offer all the functionalities of common integration storage. Pick this integration paradigm if you envision your data model changing and adapting to your business needs. It is easier to adapt ETL integrations to a data warehouse than the other way around. For example, Keboola’s ETL is designed with plug-and-play components, that can easily be swapped and customized, empowering you to pick the best-performing architecture for your data integration needs. 

Streamline your data integration techniques with the right data integration tools

Irrespective of your data integration strategy, the right data integration tool can streamline your data integration initiatives. 

All data integration tools focus on the concept of connectors - prebuilt solutions that extract data from different data sources and load it into your data storage.

But data integration tools differ drastically in how they integrate data (which data integration technique(s) they use) and how well they automate your work. 

Some offer only CDC or enterprise data integrations without the possibility of transforming data before ingesting it into your data storage.

Other data integration platforms, like Keboola, empower your users with every data integration technique as well as advanced features like no-code transformations that help your non-technical experts integrate data without IT help.

Check the best data integration tools on the market right now and pick yours. Or simply go with Keboola, the user’s number #1 choice for data integration. 

Bring your data together with Keboola  

Keboola is the best all-in-one Data Stack as a Service platform on the market right now. With Keboola, you can service implement your data integration techniques:

• Integrate data across all your sources and data storages with a couple of clicks. With 250+ pre-built connectors, integrating a new data source takes minutes. The Generic Writer and Generic Extractor help you integrate any API endpoint, covering any new data sources and empowering you to perform enterprise application integration at scale.
• Share virtualized data with the Data Catalog. Record, virtualize, and share data from a single point using the Data Catalog, without sacrificing observability or governance. 
• Speed up data replication with CDC. CDC is a native feature in Keboola that streamlines your data replication across any SQL data source.
• Build ETL pipelines with a couple of clicks. Build and orchestrate your data integration pipelines faster with Keboola’s ETL functionalities.
• Empower every employee. In Keboola, your data engineers have the freedom to write high-performant integration scripts that are highly customizable using low-code solutions. What’s more, your non-technical experts can build their own data integration solutions without any coding knowledge using no-code transformations and the drag-and-drop visual flow builder. 
• Automate at scale. From CDC to dynamical backends, Keboola is designed to automate all your data operations processes, irrespective of scale. You can rely on Keboola to support your data integration initiatives as you grow.

Give us a call and let’s discuss how Keboola can help you streamline your data integration processes today. 

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