MFSQL Data Exchange and reporting Connector

This  module is used where the Connector is deployed to perform data exchange only with no need for extensive development of integrations and applications.  The Integration module include a range of helper functions and procedures that may be useful of in the case of extensive data exchange operations.

Examples of usage of this module:

  • Extract metadata from M-Files into SQL for reporting, trouble shooting or analysis and data cleansing

  • Upload metadata from SQL into M-Files. This is particularly relevant when the data preparation is complex or require the extensive data management tools of SQL.

  • Simple extraction data from a third party system and updating the data into M-Files

  • Combine M-Files metadata with other corporate data that is not in M-Files for reporting and analysis

M-Files External Connectors

When to use Data Exchange instead of M-Files External Connectors.

The standard M-Files External Connector is powerful and applicable in many case cases.  MFSQL Connector takes data exchange to the next level and overcomes some of the disadvantages and limitations of the external connector.  Some of the differences are highlighted below.

Note that the same class table cannot be used to process records using a external connector and MFSQL Connector procedures at the same time. However, it is possible to use external connector for classes and MFSQL Connector for classes of a different object type.

M-Files External Data Connectors

MFSQL Connector: Data Exchange

SQL server and vault must be in the same network

Vault & Database in different locations and networks

ODBC is fundamentally a text based data exchange

Encrypted data exchange

Update timing is set on a schedule

Update is near immediate and is highly configurable

Limited data manipulation

Extensive ability to prepare and manipulate data to be exchanged

Simple source data structures

Complex data management with conditional criteria

Limited Valuelist lookups

Multiple joins and combinations

Source and target data is similar

SQL based data preparation

Limited triggers for update process

Alignment of source and target data Batch preparation of data updates Multiple triggers for data update