Modern data firms need data siloed away at different locations to be in an analysis-ready form in a unified data repository.
This approach of combining data from multiple applications into a single source of truth is called data integration and has become the need of the hour for many businesses. You can supercharge your decision-making progress by applying the right data integration techniques and strategies that cater to your business needs.
But what are these techniques? No need to worry! We have compiled a comprehensive list of data integration techniques, strategies, and technologies widely used in organizations worldwide.
6 Types of Data Integration Techniques and Strategies
Based on the disparity, complexity, and number of data sources, you can choose from the following different types of data integration techniques:
1. Data Consolidation
Data Consolidation refers to combining data from various sources into a centralized data store that acts as a single source of information for the organization. Enabling you to store data in a unified store, it can be used for all your reporting and analytics use cases and can serve as a data source for other applications.
However, there is some data latency in this data integration method. There will be some time difference between when the data is updated in the original data source and when it gets updated in your central repository.
Since data is transformed before it is consolidated, you get data in a consistent format on the central data source, providing your data professionals an opportunity to improve data quality and integrity.
2. Data Federation
Unlike the data consolidation strategy, where you move all data to a single source of truth, data federation offers a virtual database.
Simplifying access for consuming users and front-end applications, this data integration technique performs data abstraction to create a uniform user interface for easy data access and retrieval.
Your queries to the federated virtual database are sent to the relevant data source, which then returns the data you requested. This is an on-demand data solution compared to other real-time data integration techniques.
3. Data Propagation
Data propagation uses applications for data transfer from enterprise data warehouses to multiple source data marts on an event driven-basis.
As data continues to be updated in the warehouse, the respective data marts are updated synchronously or asynchronously.
You can use enterprise application integration (EAI) and enterprise data replication (EDR) technologies for data propagation.
4. Middleware Data Integration
- Compared to other data integration techniques, the middleware data integration strategy uses a middleware application to transfer data from multiple applications and source systems into a central repository.
- This approach validates and formats the data before beginning the transfer to the data store, thereby significantly reducing the chances of compromised data integrity or disorganized data.
- This is especially beneficial for integrating older systems with newer ones, as the middleware can help transform the legacy data into a format that the newer systems can understand.
- However, there are a few challenges with this approach when compared to similar data integration techniques. Middlewares have to be continuously monitored, deployed, and maintained by the engineering team.
- You may also face limited functionalities with middleware data integration techniques, as middlewares are not always completely compatible with all applications.
5. Data Warehousing
Generally referred to as Common Data Storage, data warehousing is one of the popular data integration techniques where data is replicated from the source and stored in a data warehouse.
Consistently storing all your data, this data integration strategy includes cleansing, formatting, and transforming data before storing it in the data warehouse.
Data warehousing also promotes better data integrity as all data information can be accessed from the data warehouse that acts as a single source.
6. Manual Data Integration
Using hand-coding, organizations can develop their data integration strategies and custom code for organizing and integrating data.
This is a good option if you only need to integrate data from a few sources or rarely need to replicate data from applications to a destination of your choice. However, it is a time-consuming task that requires manual intervention, often leading to more errors.
Out of the other data integration techniques, the manual method can be challenging when you want to scale and add more data sources.
You have to spend a considerable amount of your engineering bandwidth to continuously monitor the data pipeline and fix any data leaks on priority.
5 Popular Data Integration Technologies
There has been rapid development of data integration technologies over the past decade. Let’s check out the most popular methodologies and technologies used for data integration in businesses:
1. Extract Transform Load(ETL)
This is the most versatile and popular data integration technology preferred by organizations worldwide. From extracting data to transforming and loading it into a data warehouse, the ETL method takes care of it all.
You can do a batch ETL for bulk movements of large amounts of data or go for an incremental loading or near-real-time replication using Change Data Capture(CDC) technique.
To get data in an analysis-ready form, ETL allows you to perform multiple transformations like data cleansing, quality, aggregation, and reconciliation. For one-time data replications or when there are only a few data sources, your engineering team can build a custom solution.
However, if your business users need analysis-ready data from multiple sources updated every few hours, then you can try using automated no-code cloud ETL tools like Hevo Data.
Streamline your data integration with Hevo’s zero-maintenance pipelines. Schedule a demo to explore our user-friendly ETL tool, renowned for accuracy and seamless performance.
2. Enterprise Information Integration(EII)
Considered as a data federation technology, Enterprise Information Integration provides on-demand data. It essentially creates a virtual layer or a business view of relevant data sources.
It presents business users simple user interface where they can input their queries while multiple connections to various sources having different formats, interfaces, and semantics are at work at the backend.
Compared to traditional batch ETL processes, EII can easily handle real-time data integration and delivery use cases, allowing business users to consume updated data for data analysis and reporting.
3. Enterprise Data Replication (EDR)
Applied as a data propagation strategy, Enterprise Data Replication(EDR) follows a near-real-time data consolidation approach. Based on your business requirements, EDR allows you to replicate complex data from disparate sources and load it to target destinations in near-real-time or in regular intervals.
Though EDR also involves bulk movement of data, there is no data transformation or manipulation compared to ETL.
4. Data Visualisation
Analytics and reporting platforms also offer easy access to data for business intelligence. With in-built connections to common data sources, you can quickly visualize your data through dashboards, reports, charts, and other formats. However, you may not always find the custom integration or reporting functionalities you need.
5. API(Application Programming Interface)
Many of your data sources will offer direct access to data via APIs. Though your engineering team has to spend a significant amount of time connecting, testing & monitoring these API connections to ensure a smooth integration.
Extra information to Enhance your understanding
Final Thoughts
- Based on your data sources, data replication frequency, and the complexity of your data, you can now choose the best one out of the above data integration techniques.
- After deciding on your data integration strategy, you can opt for the data integration technology that is economical and efficient for you.
- Building new connections from scratch might be an effective choice if you only handle a handful of data sources.
Sanchit Agarwal is an Engineer turned Data Analyst with a passion for data, software architecture and AI. He leverages his diverse technical background and 2+ years of experience to write content. He has penned over 200 articles on data integration and infrastructures, driven by a desire to empower data practitioners with practical solutions for their everyday challenges.
