“Data integration” is undoubtedly the most interesting subject of the tech-world these days. The customer and business are rhyming better on account of the modern data processing system. And, with data-integration being part of it, companies can take the holistic view of their internal processes and expand their business with minimum risks.
Businesses from all areas are refining valuable information on various stages, whether it is the human resource, online payment, logistics, supply chain, or even social media accounts. If business owners connect all this information, it may give valuable insights into business operations and better decision-making ability. It helps to create the roadmap to run a business successfully.
In simple words, data integration is the process of accumulating data from disparate sources into meaningful and valuable information.
However, being said so, it is essential to choose the right approach for data integration. There are various techniques for data integration, but based on the complexity of the data extraction process, the techniques are adopted.
Data integration applies to various areas like:
Approach for Data-Integration
Step 1: Decide how you want to sync your data
Step 2: Inputting data into an integration system
Step 3: Map your systems, fields, and objects
Step 4: Setting up filters for data refining.
Step 5: Start your integration- sync historical data or start fresh?
Techniques for Data-integration
1) Manual Integration:
Manual integration usually involves writing code for connecting different data sources, collecting the data, and cleaning it, etc., without automation. Right through data collection, to cleaning, to the presentation, everything is done by hand. The strategy is best for one-time instances, but it is a time-consuming and tedious process.
2) Middleware integration:
It is ideal for businesses, who want to integrate the legacy systems with newer ones, as middleware can act as an interpreter between these systems. Middleware is a layer of software that creates a common platform for all interactions, internal and external to the organization—system-to-system, system-to-database, human-to-system, web-based, and mobile-device-based interactions. It is mostly a communications tool and has limited capabilities for data analytics.
3) Application-based integration:
The “application based integration’ technique is mostly a communications tool and has limited capabilities for data analytics. It allows the user to access various data sources and returns integrated results to the user. It is a standard integration method used in enterprises working in hybrid cloud environments. However, when there is a large volume of data, and users have to manage multiple data sources, this technique is less preferable. The technique is most suited to integrate a very limited number of applications.
4) Uniform access integration or Virtual Integration:
The approach is best for those businesses that need to access multiple, disparate systems. In this technique, there is no need to create a separate place to store data. The technique creates a uniform appearance of data for the end-user. The main advantage is that there is zero latency from the source system to the consolidated view for the data updates. With data virtualization, there is no need for a separate data store for the consolidated unified data. However, the limitation for accessing the data history and version management is a challenge for this data integration technique. It can be applied to only some kinds of database types. It means it may not handle the excess load on the source system.
5) Common storage integration:
It is similar to uniform access, except it creates and stores a copy of the data in a data warehouse. It is the best approach and allows for the most sophisticated queries. The technique collects data from various sources, combining them to a central space and management (Database files, mainframes, and flat files). Though it is considered as one of the best data integration techniques, the user has to bear higher maintenance cost.
Tools for Data integration
Data integration is the process by which information from a variety of sources is integrated into the business system. Integration can be done through a number of approaches such as manual data integration, automated data integration or a combination of both techniques. In order to increase the business benefits from data integration, it is important for an organization to choose the right data integration techniques.
1. Data consolidation
Physically, data consolidation puts data from many different systems together, generating a version of the merged data in one data store. The purpose of data consolidation is also to decrease the number of places for data collection. Data consolidation is assisted by the technologies of extract, convert, and load (ETL).
ETL collects information from multiple sources, translates it into a comprehensible format, and then moves it to another archive or data warehouse. Until data populates the new source, the ETL method clears, scans, and transforms data, and then imposes regulations.
2. Data Propagation
The use of technologies to replicate data from one location to another is the propagation of data. It can be performed synchronously or asynchronously and is event-driven. A two-way data interchange between the source and the target is enabled by most synchronous data propagation.
There are two technologies that facilitate data propagation:
For the exchanging of messages and transactions, EAI incorporates application frameworks. It is also used for the handling of real-time business transactions. A new approach to EAI integration is the Integration platform as a service (iPaaS).
EDR usually moves vast volumes of data instead of systems, between databases. For the capture and distribution of data modifications between the source and external repositories, triggers and logs are used.
3. Data Virtualization
Virtualization provides an interface for multiple data structures for the coherent representation of data from diverse sources in real-time. It is possible to view data at one location; however, it is not stored at that one location. Virtualization collects and analyzes data, but requires no standardized formatting or a single access point.
4. Data Federation
This makes use of a virtual database that generates a shared data model from various systems for diverse data. Enterprise Information Integration (EII) is a data federation enabling infrastructure. To offer a single view of data from various sources, it uses data abstraction. Via applications, the information can then be interpreted or analyzed in new ways.
5. Data warehouse:
Data warehouses are information storage repositories. However, the word 'data warehousing' implies data that is processed, revised, and preserved.
All of these technologies and applications are important for a smooth workflow. A data integration expert can assist an organization in integrating its data, internally and externally, effectively and efficiently.
Nowadays, data integration is a process that could hardly be overestimated as it is essential for every business. The idea behind it is to collect data coming from external and internal resources in a consistent and ready-to-use format.
Our team has been developing software solutions for more than 15 years, and during this time, we have gained experience with different data integration techniques. As each of our customers has a unique set of requirements, we know how to adapt to the suggested workflow. Among various approaches to defining data integration techniques, I would mention:
At Redwerk, we have delivered over 300 projects. While working on them, we learned how to assure data migration from one database to another, synchronizing multiple data segments between the applications and enterprise data warehouses.