Two decades ago, most businesses found that data integration techniques helps make simpler operation. But nowadays, data integration has evolved into the connective tissue which holds the modern IT environment together. Many businesses have implemented a variety of ERP systems, CRM, marketing automation, point-of-sale systems, mobile applications, and other technologies. As a result, it is simple to see how data integration has evolved into a critical competency for business success. Here are some data integration techniques and best practices to consider as you plan your roadmap. Let’s go with AHT TECH JSC to explore it right now.
What is data integration?
The combination of technical and business processes used to merge data from multiple sources into meaningful and useful information is data integration. Because businesses store information in various databases, data integration becomes an important strategy to implement. Therefore, it enables business users to integrate data from various sources. For instance, an e-commerce company that wants to obtain customer information from different data streams or databases, such as marketing, sales, and finance. So in this case, data integration aids in the consolidation of data from multiple departmental databases for reporting and analysis.
Types of Data Integration Techniques
When data comes in from multiple internal and external sources, the demand for data integration increases. This is done by employing one of three types of data integration techniques. So let’s look at each of these data integration approaches in turn to see how they can help improve business intelligence processes.
Data consolidation is the process of combining data from various data sources. Therefore, you can build a centralized data repository or data store. This unified data store is used for a variety of purposes, including reporting and data analysis. Moreover, it can also serve as a data source for downstream applications.
Data latency is one of the key factors. It distinguishes data consolidation from other data integration techniques. The amount of time required to retrieve data from data sources and transfer it to the data store is data latency. The shorter the latency period, the more recent the data in the data store for business intelligence and analysis.
This latency can range from a few seconds to hours or more. Moreover, it depends on the data integration technologies used and the specific requirements of the company. However, thanks to integrated data technologies, you can consolidate data and transfer changes to the destination in real-time or near real-time.
Data federation is a data integration technique. It is suitable for consolidate data. Besides that, it also make more accessible to end users and front-end applications. Furthermore, the data federation technique integrates distributed data with different data models into a virtual database.
Behind a federated virtual database, there is no physical data movement. Instead, you can use data abstraction to create a standardized user interface for data access and retrieval. As a result, when a user or an application queries the federated virtual database, the query is decomposed and routed to the appropriate underlying data source. In other words, data federation serves data on an as-needed basis, as opposed to real-time data integration, which integrates data to create a separate centralized data store.
Data propagation involves transferring data from an enterprise data warehouse to different data marts after the necessary transformations. Because data in the data warehouse is constantly being updated. So changes are propagated to the source data mart in a synchronous or asynchronous way. Moreover, enterprise application integration (EAI) and enterprise data replication are 2 common data integration technologies for data propagation (EDR).
Different Data Integration Technologies
Over the last decade, data integration technology has progressed at a rapid pace. Firstly, the only available technology for batch data integration was Extract, Transform, and Load (ETL). But, after businesses added more sources to their data ecosystem and the demand for real-time data integration technologies increased, new advancements and technologies were released:
So here is a list of the most popular data integration technologies in use today:
Extract, Transform, Load (ETL)
ETL, or Extract, Transform, Load, is a data integration process. It involves extracting data from a source system and transforming it before loading it to a target destination.
ETL is mainly suitable for data consolidation. Moreover, it also can be performed in batches or in near-real-time using change data capture (CDC). Furthermore, batch ETL is suitable for large-scale data movements, such as during data migration. However, CDC is a better choice for transferring changes or updating data to the intended destination.
Information is retrieved from a database, ERP solution, cloud application, or file system and transferred to another database or data repository during the ETL process. The data transformations conducted differ depending on the data management use case. Data cleansing, data quality, data aggregation, and data reconciliation are all common transformations.
Enterprise Information Integration (EII)
Enterprise Information Integration (EII) is a data integration technology. It allows the delivery of curated datasets on-demand. EII is also a type of data federation technology. It entails the creation of a virtual layer or a business view of underlying data sources. This layer protects consuming applications and business users from the complexities of connecting. In other words, EII allows developers and business users to treat a variety of data sources as if they were a single database.
Enterprise Data Replication (EDR)
Enterprise Data Replication (EDR) is a real-time data consolidation method. It involves transferring data from one storage system to another. EDR entails moving a dataset from one database to another with the same schema. Nevertheless, the process has recently become more complex, involving disparate source and target datasets, with data replicated at frequent intervals, in real-time, or depending on the enterprise’s needs.
Furthermore, to meet the data needs of their business users, businesses with complex data management architectures can use Enterprise Application Integration (EAI), Change Data Capture (CDC), and other event-based and real-time technologies
Take business into the next level with AHT JSC
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