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It's true that there are different techniques to successfully execute a data integration project, but how do you know which one is the most effective for your company?
In the previous article, we discussed what data integration was all about, the steps to follow in the process, and all the advantages it brings to organizations. Today, we'll talk about the different approaches within data integration and how to choose the best one for your particular case. Will you join us? Inside the article!
Data Integration Techniques
Like on any journey, there are different routes that can lead you to the final destination. The same applies to data integration; there are different alternatives to carry out the project. Let's take a look at them:
1. ETL (Extract, Transform, Load)
One of the problems that companies face is collecting data from different sources and formats, as it will later be necessary to move them to one or more data warehouses. And it's very likely that the destination is not the same type of data warehouse as the source, since often the format is different or the data needs to be shaped and cleaned before loading them into the final destination. The ETL process is precisely responsible for that. Its initials come from English:
Extract
Transform
Load
Thus, an ETL process is a pipeline and data integration used to collect data from different sources and formats. First, it extracts them, then transforms them according to business rules into a standardized format, and finally loads them into a target system or warehouse. This approach is perfect for processing large volumes of data and for batch processing.
2. ELT (Extract, Load, Transform)
It's a variant of the previous one, where the process is reversed slightly. This approach also involves extracting data from multiple sources and formats; however, instead of being transformed, they are first loaded into the target warehouse, and then they are often transformed using SQL-based tools.
3. API-Based Integration
This approach involves the use of APIs. But what are APIs exactly? Well, they're Application Programming Interfaces that allow different software programs to interact with each other. Put simply, APIs act as the language that applications use to communicate with each other. These rules enable different software to interact, connect, and exchange data in real-time. This way, existing infrastructures are leveraged, saving time and resources. APIs aren't anything new; they've been around for years. However, with the evolution of technology, Data Science, and Artificial Intelligence, they've undergone a makeover. Initially, they were just an internal interface for communication between applications within the same company. But now, they serve as a means to interconnect applications and data with third parties. Therefore, APIification is a business model based on an interface that allows one application to interact with another. Let's take an example to understand it better: Samsung's Smart Home allows us to control household appliances and devices through a mobile phone. From the TV to the washing machine, everything can be interconnected. And this is possible thanks to an API operating through the cloud. Now that we know what it's all about, going back to the main topic, API-based data integration is often used for cloud-based applications seeking maximum efficiency and agility and can be more efficient than batch processing.
4. Federated Integration
This approach involves creating a virtual data reality across different systems, without physically moving or consolidating them. Federated integration provides real-time access to data across different systems, but be careful, it can be complex to implement and maintain.
5. Data Virtualization
This technique involves creating a virtual layer of data that integrates from multiple sources, providing a unified view. Data virtualization consists of creating a virtual data management architecture that sits on top of traditional Datawarehouses and accesses and integrates data from multiple sources, providing a unified view. Thus, it generates access to a single logical layer of information, facilitating its understanding and obtaining valuable insights. Data virtualization can be used to integrate quickly without the need for physical consolidation or ETL processes.
How to Determine the Best Data Integration Approach for Your Company?
Just like in any journey, there will be a route that is most suitable for you because it's shorter, involves fewer risks, or adapts much better to your needs. Each of the approaches we've discussed has its strengths and weaknesses depending on the data sources, the level of integration required, and business objectives. You can choose to use one or several, depending on your specific needs or resources, but how do you know which one is the best for you? Choosing the best technique will depend on various factors, including the volume and complexity of the data, your business objectives, available resources, and your IT infrastructure. Here are some steps that can help you determine the best data integration approach:
Identify your company's requirements: The first phase involves identifying your specific data integration requirements and business objectives. This includes identifying the different types of data sources, the frequency and speed required for data integration, and the level of quality and governance needed.
Examine the complexity of your data: Now you'll need to evaluate the complexity and volume of the data you'll be integrating. Here, you'll need to consider factors such as data formats, data models, their quality, and security.
Assess available resources: It's time to assess the resources you have available, including IT infrastructure, data integration tools, and qualified personnel.
Consider cost: The fourth step is to examine the implementation and maintenance costs of the data integration approach, including software licenses, hardware, and personnel costs.
Determine the best approach: Based on the above factors, it's time to evaluate the different data integration approaches we've discussed earlier and determine which one best suits your specific needs and objectives. Your company may also choose to use a combination of different approaches to achieve better results. Tip: It's important to carefully evaluate the different techniques and consider the costs and benefits of each before making a decision.
In summary, in this exciting adventure towards data integration, we've explored different approaches and techniques that will allow you to unify and leverage your information successfully. And as in any journey, choosing the right path is crucial to achieving your goals. To make the right choice, you'll need to identify your company's requirements, examine the complexity of your data, evaluate the resources at your disposal, and consider the costs of implementing and maintaining the technique you choose.