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Essential Guide to Data Integration

What is Data Integration?

Simply put, data integration is the process of combining disparate data sources and providing a unified view of the entire data. The process usually involves extracting data from different sources, transforming it into a standardized format, and finally loading it into a data warehouse or a database.

This is a prerequisite for accurate analyses and faster, more informed decision-making regardless of the use case. Therefore, data integration is one of the most critical steps in the entire data management process.

From being the responsibility of ETL developers writing codes to combine disparate data sources, data integration has evolved over the years and now involves self-service ETL tools that can automate most, if not all, tasks.

Best Data Integration Techniques

There are various data integration techniques today that businesses use depending mainly upon the use case and budget, among other factors such as the disparity, complexity, and the number of data sources.

Let’s go through some of the most common data integration techniques.

Manual Data Integration

As the name already suggests, this strategy involves manual supervision of all the aspects related to data integration. This is usually done by writing custom code without any provision for automation. Evidently, this technique has more downsides than upsides.

It can be tedious and time-consuming, then there’s greater room for error, the addition of any new data sources would again require writing code, and so on. The advantages include total control over the entire process and reduced costs in the one-off use case scenario.

Data Consolidation

This data integration technique involves combining data from different sources and loading it into a centralized data repository. The data that is loaded in this repository is consistent, so it is readily available for reporting or analytics purposes. However, it should be noted that there’s almost always some latency involved in this technique.

Data Federation

Somewhat similar to the concept of data consolidation, data federation combines data from different sources and provides easy access to it. However, there’s no real centralized data repository that is used as a single source of data. Instead, a data federation uses a virtual database to integrate data into a single source of truth.

Data Propagation

Data propagation is a data integration technique that replicates and transfers data from various source databases to local databases. Depending upon the requirements, the data can be copied both synchronously and asynchronously. Pertinent to mention here is that there are two technologies that support data propagation, Enterprise Data Replication (EDR) and Enterprise Application Integration (EAI).

Application-based Data Integration

In contrast to manual integration, application-based integration utilizes software applications, instead of writing custom codes, to locate, fetch, clean, and integrate data. While it simplifies the integration process via automation, the results can often be inconsistent because this approach is unstandardized and focuses more on communication between various systems.

Middleware Data Integration

The best way to understand this data integration technique is to think of it as an adapter that enables a legacy electronic device to communicate with a modern device. In essence, middleware is software that sits in the middle of an operating system (OS) and the applications that run on it enabling communication between them and databases.

Uniform Access Integration

This data integration technique involves accessing data from highly disparate sources and presenting it in a unified manner. It should be noted that all of this is done without moving data from its original location, thereby eliminating the need for additional data storage space. While it provides a unified view of the data, accessing such disparate sources may compromise data quality.

Common Storage Integration

This data integration strategy is sometimes also referred to as data warehousing. Common storage integration can be thought of as an advanced version of uniform access integration.

In addition to accessing disparate data sources, it includes creating and storing copies of data from these sources into a data warehouse. This allows businesses to manipulate data according to their requirements.

With the volume of data increasing exponentially and rapidly changing business requirements, it’s only a matter of time before even more data integration techniques come to light.