Itegration

Itegration

WORLD-CLASS E-COMMERCE SOLUTIONS

4.00/5 (1 Reviews)
About Itegration
ITEGRATION is a full-service e-commerce solution provider focused on Magento and Bigcommerce implementation, consultancy, system integration and mobile development. Our goal is to help every client grow their business through the best in class implementations, optimization a...
read more
$50 - $99/hr
10 - 49
2014
United States, Hungary
Itegration
WORLD-CLASS E-COMMERCE SOLUTIONS
4.00/5 (1 Reviews)
1 Review
Client Reviews
Alternergy PVReviewed 2 months ago
Alternergy Review of Itegration, Hungary
Reviewed 2 months ago by Alternergy PV
Alternergy Review of Itegration, Hungary
We have used Itegration for over 2 years and they have performed extremely well. The team is knowledgeable and helpful and the work has been done to a very good standard. We are currently in the process of upgrading to the latest version of Magento 2.3 and, to date, Itegration have been excellent in getting the job done.

What do you like most about the company?

Responsive and knowledgeable

What they should improve on?

Occasionally, quicker delivery of the project, but it could be that they have a lot of Magento 2 upgrades to complete before Magento 1 ceases to receive support.

Rating breakdown
Quality
Reliability
Ability
Overall
Other details
Services:
E-commerce Development
Project Budget:
$10001 to $50000
Project Duration:
26 Weeks
Project Status:
In progress
Services

We provide Magento and BigCommerce implementation, development and support also mobile development, PWA development, among others. 

Among other projects, we have completed several Magento 1 to Magento 2 transitions.

 

Focus
Service Focus
Company Video
2 Videos
Itegration Overview
Portfolio
1 Portfolio
Dockyard Islands Kft.

Itegration has helped optimize the website, which helps it to run very quickly. Page speed and stability have improved significantly. They installed new modules and have planned unique features. Project management and communication have been solid. 

$10001 to $50000 10 weeks Consumer Products
Discussions
“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.     

 

Tools for Data integration

  • IBM InfoSphere
  • Informatica PowerCenter
  • Microsoft SQL Server Integration Services
  • Oracle Data Integration Platform (DIP)
  • SAP Data Services
  • Panoply
  • Actian DataConnect
  • Syncsort
Contact information
us
Itegration
100 Westgreen Drive Suite 206, North Carolina, North Carolina 27516
United States
+1-713-878-0512
hu
Itegration
Kiraly street 26, Budapest, Budapest 1061
Hungary
+43 650 55 22 466
View more
GoodFirms