Data integration is a process of combining data from different sources and presenting it in a unified and consistent manner. It is an essential task for any business that relies on multiple data sources to make decisions. There are various data integration techniques that can be used to accomplish this task. At Amqid.info discuss the most common data integration techniques.
6 Types of Data Integration Techniques and Strategies
You have to choose data integration techniques appropriate for your business use case when your company wants to process data from many internal and external sources. The following sorts of data integration approaches are available for you to pick from, depending on the diversity, complexity, and quantity of data sources:
1. Data Consolidation
Data consolidation is the process of merging data from numerous sources into a single, centralized data storage that serves as the organization’s only source of information. You can use it for all of your reporting and analytics use cases, as well as a data source for other apps, as it enables you to store data in a unified store.
Unfortunately, this data integration approach has some data latency. Between the time the data is changed in the original data source and the time it is updated in your central repository, there will be some lag time.
As data is transformed before it is consolidated, you get data on the central data source in a consistent manner, giving your data specialists a chance to enhance the quality and integrity of your data.
2. Data Federation
Data federation provides a virtual database, in contrast to the data consolidation technique, which involves moving all data to a single source of truth. This data integration technique performs data abstraction to produce a standard user interface for simple data access and retrieval, simplifying access for consuming users and front-end applications. The relevant data source receives your requests for information over the federated virtual database and responds with the information you requested. When opposed to other real-time data integration techniques, this is an on-demand data solution.
3. Manual Data Integration
Organizations can create bespoke code for organizing and integrating data using hand-coding to establish their data integration techniques. If you just occasionally need to duplicate data from apps to a specific destination or only need to integrate data from a small number of sources, this is an excellent alternative. Unfortunately, it is a time-consuming task that needs manual intervention, which frequently results in more mistakes. The manual method of data integration can be difficult to scale and integrate new data sources compared to the other methods. To continuously monitor the data pipeline and address any data leaks as soon as they occur, a sizeable portion of your engineering bandwidth is required.
4. Data Propagation
Applications are used in data propagation to send data on an event-driven basis from enterprise data warehouses to several source data marts. The corresponding data marts are updated either synchronously or asynchronously as data is still being updated in the warehouse. Enterprise data replication (EDR) and enterprise application integration (EAI) are two technologies that can be used for data dissemination.
5. Middleware Data Integration
The middleware data integration strategy, in contrast to other data integration techniques, employs a middleware application to move data from many applications and source systems into a main repository. This method forms and validates the data before transferring it to the data repository, thereby lowering the possibility of compromised data integrity or disorganized data. As the middleware may assist in transforming the legacy data into a format that the newer systems can understand, this is especially advantageous for connecting older systems with newer ones.
However, this method has a few drawbacks when compared to other data integration techniques. The technical team must constantly deploy, monitor, and manage middleware. Middleware data integration approaches may also have some functional limitations because they aren’t always fully compatible with all applications.
6. Data Warehousing
Data replication from the source and storage in a data warehouse, also known as Common Data Storage, is one of the most well-liked data integration approaches. This data integration method involves cleaning, formatting, and converting data before storing it in the data warehouse in order to consistently store all of your data. Because the data warehouse serves as a single source for all data information, it also encourages improved data integrity.
4 Popular Data Integration Tools
1. ETL (Extract Transform Load)
Organizations all around the world favor this data integration technology since it is the most adaptable and well-liked. The ETL approach handles everything, from extracting data to converting it and loading it into a data warehouse. Large data sets can be moved in bulk using batch ETL, incremental loading, or near-real-time replication utilizing the Change Data Capture (CDC) approach.
ETL enables you to carry out numerous transformations including data cleansing, quality, aggregation, and reconciliation to receive data in a format that is ready for analysis. Your engineering team can provide a special solution for one-time data replications or when there are only a few data sources. But, if your business users want analysis-ready data from many sources updated every few hours, you might want to explore adopting automated no-code cloud ETL technologies like Hevo Data.
2. EDR (Enterprise Data Replication)
Enterprise Data Replication (EDR), used as a data propagation mechanism, adopts a near-real-time data consolidation strategy. EDR gives you the ability to reproduce complicated data from many sources and load it to desired locations either instantly or on a regular basis depending on your business needs. Unlike ETL, which requires data transformation and manipulation, EDR only involves bulk data transport.
3. API(Application Programming Interface)
Many of your data sources will provide direct access to data via APIs. However to ensure a seamless integration, your engineering staff must spend a lot of effort connecting, testing, and monitoring these API connections.
4. EII (Enterprise Information Integration)
Enterprise Information Integration offers data that is available when needed and is regarded as a data federation solution. Basically, it develops a virtual layer, or business perspective, of pertinent data sources. Despite the fact that there are numerous connections to different sources with different formats, interfaces, and semantics operating at the backend, it provides business users with a straightforward user interface where they can enter their queries.
EII is more adept at handling real-time data integration and delivery use cases than typical batch ETL methods, enabling business users to access updated data for data analysis and reporting.
Data integration is an essential process for businesses that rely on accurate and up-to-date information to make decisions. There are many different data integration techniques available, each with its own advantages and disadvantages. The choice of technique will depend on the specific needs of the business. By understanding the different data integration techniques available, businesses can choose the right solution for their needs and ensure that they are able to make informed decisions based on accurate and up-to-date information.