Best Data Integration Tools

Data integration tools are software solutions that facilitate the process of integrating data for organizations and allow individuals to organize and gather data. If a company employs developers, they can initiate data integration through scripts written in Structured Query Language (SQL), which is the programming language for relational databases. However, not all organizations have such specialists at their disposal. That is why ready-made data integration tools exist, assisting different types of companies and helping them quickly gather data from various sources for easier use.

Data integration tools not only initiate the process and provide the required information, but many come with additional benefits. Vendors offer platforms that also provide data replication, data cataloging, data governance, and data quality improvement. The development of technology has introduced cloud-based integration services through integration platform as a service (iPaaS). The additional features of data integration tools make them more beneficial to organizations for integration processes than relying on hand-coded scripts.

According to PeerSpot users, data integration is all about data and governance quality. Our members want to be able to transform, organize, review, and find all sorts of data (including unstructured data, flat files, etc.) quickly and easily. Data integration tools should be customizable with efficient processing, source agnostics, real-time monitoring and debugging, and provide good documentation. Reusability and connectivity were also ranked as important factors.

Data integration techniques differ depending on the number of sources where information is stored, how complex the data is, and the type of data that is being integrated. Three of the main techniques include:

• Data consolidation: This technique combines data from different sources to create a centralized data store or data repository. This store can be used for various purposes, including analyzing or reporting the information, as well as a source for downstream applications. In this technique, one of the crucial factors is the amount of time it takes to gather data from the different sources and transfer it to the data store. This process is called data latency and can take from seconds to hours, or even days, depending on the nature of the collected data.
  
  
• Data propagation: In this technique for data integration, data is taken from an enterprise data warehouse and transferred to different data marts after undergoing required transformations. The updates of data in the warehouse are reflected, or propagated, to the source data mart in either a synchronous or asynchronous manner, depending on the case.
  
  
• Data federation: This technique is used to consolidate the integrated data and make users' access to it simpler. In data federation, data is integrated into a virtual database which uses a unified data model. In this method, no physical data movement is initiated. Instead, data is abstracted to create a uniform interface for access and retrieval for its users.

The terms data integration and interoperability may be used interchangeably by mistake, however, the two refer to different processes. Data integration uses a hand-coded script or specialized tools to gather information from various sources. In its essence, the process relies on a third party that translates the data and utilizes it for users and applications to use.

Unlike integration, data interoperability is a direct exchange of information. The process does not rely on middleware; rather, it creates a real-time data exchange between systems for data. When systems utilize this process, they not only share information but also interpret incoming data and present it, preserving its original context.

The process of data replication is crucial for companies that want to organize their data and use it in the most efficient way possible. It has different advantages, however, if not facilitated properly, it may also have disadvantages. Below are listed some of the more common pros and cons of data integration an organization may encounter.

Advantages of Data Integration

The advantages of data integration include:

• The collected data in one location can help organizations for better decision-making.

• The process results in easier access to information and may improve the relationships between organizations and their customers and partners, as it allows everyone to see the required data for their work.
  
• Data integration may make a work process more efficient, as it provides employees across the organization with all required data, no matter the source where it is initially stored.
  
• If a company uses integration, it may save money and time as opposed to using inefficient methods for accessing and analyzing data; This solution offers it all in one place.

Disadvantages of Data Integration

The disadvantages of data integration include:

• If an organization is not prepared and does not utilize proper tools, it may become complicated and take more time than accessing the data at its separate sources.
  
• Every organization's data integration needs differ based on workplace specifics and there is no universal solution for all. A company may experience difficulties if it uses a solution that is not suitable for its needs.
• The new system that is created as a result of data integration needs continuous support and maintenance, which may be difficult for organizations that are unprepared.
• Even though data integration tools are not difficult to use, employees may need additional preparation and training on how to do it. Without preparation, even if the tools are efficient, they may not be used to their full capacity.