Common Data Integration Techniques and Technologies Explained

By |2021-10-27T07:34:10+00:00September 23rd, 2019|

Issues in combining data from multiple disparate sources have always remained, hence scientists at the University of Minnesota designed the first data integration system in 1991. This data integration technique used the ETL approach that extracts, transforms and loads data from different sources into a unified view.

In this blog, we will discuss what data integration is in general, the various data integration techniques and technologies.

What is data integration

Data Integration Explained (Source: Last Night Study)

What is Data Integration?

The process of consolidating data from multiple applications and creating a unified view of data assets is known as data integration. As companies store information in different databases, data integration becomes an important strategy to adopt, as it helps the business users to integrate data from different sources. For example, an e-commerce company that wants to extract customer information from multiple data streams or databases, such as marketing, sales, and finance. In this case, data integration would help to consolidate the data arriving from various departmental databases, and use it for reporting and analysis.

Data integration is a core component of several different mission-critical data management projects, such as building an enterprise data warehouse, migrating data from one or multiple databases to another, and synchronizing data between applications. As a result, there are a variety of data integration applications, technologies, and techniques used by businesses to integrate data from disparate sources and create a single version of the truth. Now that you understand what data integration is, let’s dive into the different data integration techniques and technologies.

Types of Data Integration Techniques

The need for data integration arises when data is coming in from various internal and external sources. This is achieved by using one of the three different types of data integration techniques, depending on the disparity, complexity, and volume of the data sources involved.

Let’s take a look at these data integration approaches one by one and see how they can help improve business intelligence processes.

Data Consolidation

As the name suggests, data consolidation is the process of combining data from different data sources to create a centralized data repository or data store. This unified data store is then used for various purposes, such as reporting and data analysis. In addition, it can also perform as a data source for downstream applications.

One of the key factors that differentiate data consolidation from other data integration techniques is data latency. Data latency is defined as the amount of time it takes to retrieve data from data sources to transfer to the data store. The shorter the latency period, the fresher data is available in the data store for business intelligence and analysis.

Generally speaking, there is usually some level of latency between the time updates occur with the data stored in source systems and the time those updates reflect in the data warehouse or data source. Depending on the data integration technologies used and the specific needs of the business, this latency can be of a few seconds, hours, or more. However, with advancements in integrated data technologies, it is possible to consolidate data and transfer changes to the destination in near real-time or real-time.

Data Federation

Data federation is a data integration technique that is used to consolidate data and simplify access for consuming users and front-end applications. In the data federation technique, distributed data with different data models is integrated into a virtual database that features a unified data model.

There is no physical data movement happening behind a federated virtual database. Instead, data abstraction is done to create a uniform user interface for data access and retrieval. As a result, whenever a user or an application queries the federated virtual database, the query is decomposed and sent to the relevant underlying data source. In other words, the data is served on an on-demand basis in data federation, unlike real-time data integration where data is integrated to build a separate centralized data store.

Data Propagation

Data propagation is another technique for data integration in which data from an enterprise data warehouse is transferred to different data marts after the required transformations. Since data continues to update in the data warehouse, changes are propagated to the source data mart in a synchronous or asynchronous manner. The two common data integration technologies used for data propagation include enterprise application integration (EAI) and enterprise data replication (EDR). These data integration technologies are discussed below.

Different Data Integration Technologies

Data integration technology has evolved at a rapid pace over the last decade. Initially, Extract, Transform, Load (ETL) was the only available technology used for batch data integration. However, as businesses continued to add more sources to their data ecosystem and the need for real-time data integration technologies arose, hence new advancements and technologies were introduced:

Here is a roundup of the most popular data integration technologies in use today:

Extract, Transform, Load (ETL)

Probably the best-known data integration technology, ETL or Extract, Transform, Load is a data integration process that involves the extraction of data from a source system and its loading to a target destination after transformation.

ETL is used for data consolidation primarily and can be conducted in batches or in a near-real-time manner using change data capture (CDC). Batch ETL is mostly used for bulk movements of data, such as during data migration. On the other hand, CDC is a more suitable choice to transfer changes or updated data to the target destination.

During the ETL process, data is extracted from a database, ERP solution, cloud application, or file system and transferred to another database or data repository. The transformations performed on the data vary depending on the specific data management use case. However, common transformations performed include data cleansing, data quality, data aggregation, and data reconciliation.

Enterprise Information Integration (EII)

Enterprise Information Integration (EII) is a data integration technology used to deliver curated datasets on an on-demand basis. Also considered a type of data federation technology, EII involves the creation of a virtual layer or a business view of underlying data sources. This layer shields the consuming applications and business users from the complexities of connecting to disparate source systems having different formats, interfaces, and semantics. In other words, EII is a technology that allows developers and business users alike to treat a range of data sources as if they were one database and present the incoming data in new ways.

Unlike batch ETL, EII can handle real-time data integration and delivery use-cases very easily, allowing business users to consume fresh data for data analysis and reporting.

Enterprise Data Replication (EDR)

Used as a data propagation technique, Enterprise Data Replication (EDR) is a real-time data consolidation method that involves moving data from one storage system to another. In its simplest form, EDR involves moving a dataset from one database to another database having the same schema. However, recently, the process has become more complex to involve disparate source and target databases, with data being replicated at regular intervals, in real-time, or sporadically, depending on the needs of the enterprise.

While both EDR and ETL involve bulk movement of data, EDR is different because it does not involve any kind of data transformation or manipulation.

In addition to these three key data integration technologies, enterprises with complex data management architectures also make use of Enterprise Application Integration (EAI), Change Data Capture (CDC), and other event-based and real-time technologies to keep up with the data needs of their business users.

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