A leading Australian IT services and solutions provider, Data#3 Limited (DTL) is focused on helping customers solve complex business challenges using innovative technology solutions.
Built on a foundation of 40 years’ experience, combined with world-leading vendor t...
A leading Australian IT services and solutions provider, Data#3 Limited (DTL) is focused on helping customers solve complex business challenges using innovative technology solutions.
Built on a foundation of 40 years’ experience, combined with world-leading vendor technologies, Data#3 delivers an integrated array of solutions spanning cloud, mobility, security, data & analytics and IT lifecycle management. These technology solutions are delivered by combining Data#3's services across consulting, project services and managed services.
“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:Data warehousingData migrationEnterprise application/information integrationMaster data management 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-integration1) 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 integrationIBM InfoSphereInformatica PowerCenterMicrosoft SQL Server Integration ServicesOracle Data Integration Platform (DIP)SAP Data ServicesPanoplyActian DataConnectSyncsort
“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:Data warehousingData migrationEnterprise application/information integrationMaster data management 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-integration1) 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 integrationIBM InfoSphereInformatica PowerCenterMicrosoft SQL Server Integration ServicesOracle Data Integration Platform (DIP)SAP Data ServicesPanoplyActian DataConnectSyncsort
“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:
Data warehousing
Data migration
Enterprise application/information integration
Master data management
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.
