Follow Datanami:
March 31, 2021

The Best Techniques for Data Integration in 2021

Catherine Palmer

For most businesses, 2020 brought a lot of changes, but one thing hasn’t changed: Data volumes are still growing like crazy. Most businesses use multiple applications, such as CRMs, accounting applications, and asset management systems. Data is locked in silos in each of these applications, which can result in disconnects and miscommunications between departments or processes. If important decisions are based on misinformation resulting from these disconnects, the results will be less than optimal or even detrimental to a business. Techniques for data integration can help break down silos and make data more accessible.

Data integration is now something that nearly every organization must do. Data integration involves carefully combining all of the different data sources from around your organization so that analysts, users, and applications can have access to a complete picture of the entire organization. Performing data integration is a necessary skill in today’s data-driven world. Tackling this important task has numerous benefits, including eliminating data silos, improving collaboration, supporting better decision-making, and improving data quality and integrity.

Seven Types of Data Integration Techniques


One of the most basic methods for integrating data is hand-coding, or manual data integration. Realistically, this method is only feasible for integrating a small number of data sources. In this case, it might be effective to write code to collect the data, transform it if necessary, and consolidate it. While hand-coding may not require investing in any software, it can take a considerable amount of time, and scaling the integration to include more data sources may be difficult.

Data warehousing

Data warehousing is a type of data integration that involves using a common storage area, often a data warehouse to cleanse, format, and store data. This type of data integration is also sometimes referred to as common storage integration. Data from all of the different applications throughout an organization is copied to the data warehouse, where it can be queried by data analysts.

Querying data on the warehouse rather than on the source applications means that analysts don’t have to worry about impacting application performance. Plus, analysts can view all of the data from the entire organization in a single, central location, which means they can check for data completeness, accuracy, and consistency.

Potential issues with data warehousing include the costs of storing data in multiple locations, plus the maintenance costs required to create and maintain the data warehouse. This is why warehousing data in the cloud can be much more cost-effective and simpler.

Middleware data integration

Middleware data integration is a data integration system that involves using a middleware application as a go-between that moves data between source systems and a central data repository. The middleware helps to format and validate data before sending it to the repository, which could be a cloud data warehouse or a database.

This approach can be particularly helpful when integrating older systems with newer ones, because the middleware can help with transforming the legacy data into a format that’s usable by the newer systems.

Potential issues with middleware data integration include maintenance. The middleware must be deployed and maintained by knowledgeable developers. Another potential issue is limited functionality, since many middleware applications have limited compatibility with source applications.

Data consolidation

Data consolidation involves combining data from multiple systems to create a single, centralized data source, which can then be used for reporting or analytics. ETL software is often used to support data consolidation. ETL applications can pull data from multiple sources, transform it into the necessary format and then transfer it to the final data storage location.

There may be some latency involved in data consolidation, because it can take time to retrieve the data from the source and transfer it to the central data source. The latency period can be shortened by more frequent data transfers.

One of the benefits of data consolidation is that because the data is transformed before it is consolidated, it is in a consistent format on the central data source. This can give data workers the chance to improve data quality and integrity.

Data virtualization

Data virtualization is interesting because while all of the data remains in its separate systems, users can still gain a unified view of it. Data virtualization is essentially logical layer that integrates data from all of the source systems and delivers it to business users in real time.

A benefit to data virtualization is that you don’t actually have to move your data around. Data stays in the source systems, so you don’t have to worry about the increased storage costs associated with maintaining multiple copies of your data.

Data federation

Data federation involves creating a virtual database that consolidates data from disparate sources. Users can then use the virtual database as a single source of truth for all of the data in the organization. When a user queries the virtual database, the query is actually sent to the relevant underlying data source, which then serves the data back. So essentially, data is served on an on-demand basis, rather than other techniques for data integration, where the data is integrated before it can be queried. With data federation, data is given a common data model, even though the different data sources may have vastly different data models.

Data propagation

Data propagation entails using applications to copy data from one location to another on an event-driven basis. Enterprise application integration (EAI) and enterprise data replication (EDR) technologies can be used for data propagation. EAI can provide a link between two systems, for purposes such as business transaction processing. EDR is more frequently used to transfer data between two databases. Unlike ETL, EDR does not involve data transformation. The data is simply extracted from one database and moved to another one.

Data integration tools

With nearly all of the above data integration approaches, you’ll need a data integration tool, such as an ETL application or a data loader, to support your efforts. Choose a tool that can integrate with all of the applications you have now, or that allows you to easily create a connector if a pre-built one doesn’t exist. Ideally, a data integration tool is also flexible enough that it will support any applications that you adopt in the future as well.

Another feature to look for is an intuitive interface. You want to make sure that your data integration tools are easy to learn and use, so your team can get up and running quickly. The last thing you need in your organization is a clunky application that’s hard to use.

Want to Learn More About Data Integration?

Given the increasing volume and complexity of data, and the speed and scale needed to handle it, the only place you can compete effectively—and cost-effectively—is in the cloud. Matillion provides a complete data integration and transformation solution that is purpose-built for the cloud, with products for Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse Analytics, and Delta Lake on Databricks. Matillion products are highly rated across the AWS, GCP, and Microsoft Azure Marketplaces.

Request a demo to learn more about how you can unlock the potential of your data with Matillion’s cloud-based approach to data transformation.