Issues in combining data from multiple different sources have always remained. Hence, scientists at the University of Minnesota designed the first data integration system in 1991. This big data integration platform technique used the ETL approach that extracts, transforms, and loads data from multiple systems and sources into a unified view.
This blog will discuss the data integration process and the various data integration techniques and technologies.
What is Data Integration?
The process of consolidating data from multiple applications and creating a unified view is known as data integration. Data integration becomes an important strategy as companies store information in different databases. It helps business users integrate data from multiple sources.
For example, an e-commerce company wants to extract customer information from multiple data streams or databases, such as marketing, sales, and finance. In this case, data integration would help consolidate the data from various departmental databases. Data analysts can use the resulting unified data for reporting and analysis.
Data integration is a core component of several important data management projects. Such projects include:
- Building an enterprise data warehouse.
- Migrating data from one or multiple databases to another.
- Synchronizing data between applications.
As a result, businesses use data integration tools with a variety of applications, technologies, and techniques to integrate data from disparate sources and create a single version of the truth. Now that you understand the data integration process let’s dive into the different data integration approaches, techniques, and technologies.
What are Data Integration Techniques?
Data integration techniques are processes for combining data from multiple sources in a single destination. Common data integration techniques are:
- Data consolidation.
- Data Federation.
- Middleware Integration.
- Data Propagation.
Data integration approaches arise when data is coming in from various internal and external sources. This is achieved using one of the four types of data integration techniques. The approach will depend on the disparity, complexity, and number of data sources involved.
Let’s look at these data integration techniques individually and see how they can help improve business processes.
As the name suggests, data consolidation combines data from different sources to create a centralized data repository or data store. Data analysts can use this repository for various purposes, such as reporting and data analysis. In addition, it can also perform as a data source for downstream applications.
Data latency is a key factor differentiating data consolidation from other data integration techniques. Data latency is the 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 for business intelligence and analysis in the data store.
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. This latency can vary depending on the data integration technologies and the business’s specific needs. However, with advancements in integrated big data technologies, it is possible to consolidate data and transfer changes to the destination in near real-time or real-time.
Data federation consolidates data and simplifies access for consuming users and front-end applications. In the data federation technique, distributed data with different models are integrated into a virtual database with a unified data model.
There is no physical data movement happening behind a federated virtual database. Instead, data abstraction creates 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 the real-time data integration approach, where data is integrated to build a separate centralized data store.
Middleware integration techniques refer to the methods used to facilitate smooth data exchange between different systems. These software act as a bridge between different systems, allowing them to communicate and share information effectively. Common techniques include message-oriented middleware (MOM), service-oriented architecture (SOA), enterprise service bus (ESB), extract, transform, load (ETL), and application programming interfaces (APIs). These techniques enable seamless communication, data transformation, and integration between disparate systems.
Data propagation is another technique for data integration. It involves transferring data from an enterprise data warehouse to different data marts after the required transformations. Since data continue to update in the data warehouse, changes are propagated to the source data mart synchronously or asynchronously. The two common data integration technologies for data propagation include enterprise application integration (EAI) and enterprise data replication (EDR). Let’s discuss these data integration technologies 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 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)
The best-known data integration technology, ETL or Extract, Transform, Load, is a data integration process that involves extracting data from a source system and loading it to a target destination after transformation.
The primary use of ETL is for data consolidation. It can be conducted in batches or near-real-time using change data capture (CDC). The main use case for batch ETL bulk movements of large amounts of data is during data migration. On the other hand, the CDC is a more suitable choice for transferring changes or updated data to the target destination.
The ETL process involves extracting data from a database, ERP solution, cloud application, or file system and transferring it to another database or data repository. The transformations performed on the data vary depending on the specific data management use case. However, common transformations include data cleansing, quality, aggregation, and reconciliation.
Enterprise Information Integration (EII)
Enterprise Information Integration (EII) is a data integration technology that delivers curated datasets on demand. 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 multiple source systems having different formats, interfaces, and semantics. In other words, EII is a data integration approach that allows developers and business users to treat a range of data sources as if they were one database. This technology enables them to present incoming data in new ways.
Unlike batch ETL, EII can easily handle real-time data integration and delivery use cases, 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. It involves moving data from one storage system to another. In its simplest form, EDR consists of moving a dataset from one database to another with the same schema. Recently, the process has become more complex, involving different source and target databases. Data is also being replicated at regular intervals, in real-time, or sporadically, depending on the needs of the enterprise.
While EDR and ETL involve bulk movement of data, EDR is different because it does not involve any data transformation or manipulation.
In addition to these three key data integration technologies, enterprises with complex data management architectures also use 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.
Data Integration With Astera Centerprise
Are you looking to implement an automated data integration platform for your business? Learn in detail about how Astera can help you take advantage of these data integration techniques and create an agile data ecosystem, get in touch with our support department at [email protected] and find out which data integration approach works for your use case, or download a free trial of Centerprise and get started right away!