CData Drivers for Epicor Kinetic is described to be a very comprehensive and cloud-based ERP solution that can provide the users and the developers, at the same time, the manufacturers, with the capability for the job costing, which is robust. It can also be used in the process of the supply chain, inventory control, material planning, pre-production material planning, as well as a process of manufacturing execution in order to help with decisions in the manufacturing process to be profitable for the users and the manufacturers. Epicor ERP can support any manufacturer that is discrete with more than 1 million dollars in revenue annually. Companies and business organizations and Epicor ERP enterprise resource planning mainly use it.
It should be added that the Epicor ERP software corporation is known to be a global business software company based in Austin, TX, founded in 1972. The product has the goal and the aiming process, distribution, as well as the services of the industries along with the retail. Does the ability to provide the enterprise's resource planning, customer relationship management, and Supply Chain management? This software can perform a huge range of business tasks, starting from accounting to inventory management, and it is a software component that is about to take care of the specific operations in the organization. For a lot of manufacturers and distributors, the critical feature of the ERP is the Supply Chain management, which starts from raw materials to the delivery process.
This package includes the following components:
- Epicor Kinetic ADO.NET Provider
- Epicor Kinetic BizTalk Adapter
- Epicor Kinetic Cmdlets
- Epicor Kinetic Data Cmdlets
- Epicor Kinetic FireDAC Components
- Epicor Kinetic JDBC Driver
- Epicor Kinetic MuleSoft Connector
- Epicor Kinetic ODBC Driver
- Epicor Kinetic Power BI Connector
- Epicor Kinetic SSIS Components
- Epicor Kinetic Tableau Connector
- Excel Add-In for Epicor Kinetic
Key Features of CData Drivers for Epicor Kinetic :
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ETL & Replication: ETL, the process of extracting, transforming, and loading, is known to be the general process for the process of copying data from one source or another into a destination system, which can represent the data differently from its source or even in a different context than the source. The process of data extraction involves extracting the data from the sources of homogeneous or heterogeneous and the process of data transformation is described to be the process of open data cleaning as well as transforming them to a format or a structure for the purpose of analyzing and querying. An ETL that is appropriately designed can extract data from the system and enforce the quality of the data to be standard. It can commonly integrate data from different applications that are supported by different vendors or even computer hardware.
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Workflow & Automation: The process of workflow automation is described as the technology that can use rule-based logic to automate the work manually, just like the case of data entry and nurturing of the lead. By the process of leveraging the self-operating process that can run manual tasks, the process of workflow automation can help the users, the developers, and the user business to save time and money, minimize errors and mistakes, as well as boost the productivity of the organization.
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Software Development: As described by IBM research, the process of software development refers to a set of computer science activities that are dedicated to the process of creating, deploying, designing, and supporting the software for the users and the developers. The software itself is described to be a set of instructions as well as programs that have the ability to tell the computer what to do and what not to. It can make the computer programmable and also can provide the functions that are core such as operating systems, disk management, management of the hardware, and the necessities that are operational.
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Data Virtualization: For decades companies are is trying to break down the silos along with the process of copying data from huge operational systems right into the central data stores for the process of analysis such a data warehouse and data lakes. With the help of the data virtualization process, the user can query the data without having to copy the data or even perform the replication process of the data, which can reduce cost. It can simplify the user analytics to make them more updated as well as accurate because the user is trying to query the latest data that is present at the source.
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