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| = Introduction to Connectors =
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| == Connectors vs BigID Applications ==
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| BigID has two main ways to extend your system’s functionality: connectors and applications. While both connectors and applications have the same goal of giving you more value from BigID, they do that very differently.
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| === Applications ===
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| Applications allow you to add new screens and business logic using the BigID application framework. While these applications can contact external systems, an application cannot be a data source or entity source within BigID. That means that BigID applications will not directly take part in the scanning process.
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| The main use case for applications is to add additional screens or business logic to your BigID system. An application could add a screen to your system that allows you to view a report with additional statistics specific to your industry or synchronize findings from BigID to an external DLP tool.
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| More information about applications and how to build your own is available in our BigID AppDev Pro class.
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| === Connectors ===
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| For BigID to scan a system, a connector must exist. The BigID product ships with 50+ connectors out of the box for common enterprise systems. Connectors serve as the translator between your data source’s language and the language of BigID. They allow BigID to support any data source, whether that be a file share or a NoSQL database.
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| For a full list of supported data sources and their compatibility, visit [https://docs.bigid.com/docs/data-source-feature-summary].
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| == BigID Scanning Process ==
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| Before we create our connector, we need to understand more about the scanning process. Scanning is how BigID gets insights about your data. In a scan, BigID does the following:
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| * Scan data repositories for PI, correlating records to entities across multiple data sources using advanced multi-prong data intelligence techniques.
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| * Scan metadata to identify data objects containing PI that allow Open Access.
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| * Classify data and files using advanced data analysis techniques.
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| A high-level view of this scanning process is useful when thinking about the connector you wish to create.
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| === Steps in Scanning ===
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| # **User executes a scan**: This can be done via the UI, a scheduled scan, or an API call.
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| # **BigID sends a message to the scanner**: The scanner is a separate server located close to your data source.
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| # **BigID scanner contacts the data source**: The scanner uses the appropriate connector to access the data source.
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| # **Data source returns information to the scanner**: The data source sends the requested data back to the scanner.
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| # **Scanner builds search tree and returns to BigID**: The search tree is used to correlate data across all sources.
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| # **User sees details in Scan Result Details**: The results are displayed in a report where users can view and adjust connections.
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| == Connector Types ==
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| There are two different types of connectors supported within BigID:
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| * **Internal (Java-based Connectors)**: These connectors are written in Java and distributed as JAR files. They offer customization options but are complex to develop and deploy.
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| * **External (Generic REST API Connectors)**: These allow developers to create connectors using their preferred programming language. External connectors can be hosted on any server accessible to both the scanner and the data source.
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| === Deciding on a Connector Type ===
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| Unless your use case requires specific features of internal connectors, external connectors are recommended due to their ease of development and maintenance.
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| = Developing a Structured Connector =
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| == The Connector Supermarket ==
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| The hierarchy of BigID connectors mimics that of a supermarket:
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| * **Departments are Objects**: Departments represent similar types of items, akin to how objects in connectors represent a specific type of data.
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| * **Items are Records**: Items within departments are like records within objects.
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| * **Information is Fields**: Each record contains fields with names, types, and values.
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| == Connector Services ==
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| Structured connectors must fulfill the following services:
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| * **List what fields all items in an object have.**
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| * **List what objects exist.**
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| * **List what records are inside an object.**
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| * **Return the fields and values for a given record.**
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| * **Search for records with specific fields.**
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| = Developing an Unstructured Connector =
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| == Unstructured Connectors ==
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| Unstructured connectors handle systems that store unstructured data, like files. The hierarchy includes:
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| * **Containers**: Storage mechanisms, such as folders or buckets.
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| * **Objects**: Individual files within containers.
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| * **Metadata**: Information about files, such as creation date and size.
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| == Implementing Services ==
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| Unstructured connectors must provide services for listing containers, retrieving metadata, returning file contents, and searching for data.
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| = Distributing a Connector =
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| == Private Distribution ==
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| Private distribution allows connectors to be shared internally or with specific customers.
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| == Public Distribution ==
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| Public distribution makes connectors available to the community via platforms like:
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| * **BigID Community**: Open-source connectors shared freely.
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| * **BigID Marketplace**: Paid or free connectors for BigID customers.
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| == Packaging a Connector ==
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| Connectors are packaged as Docker containers. The process involves creating a Dockerfile, building the image, and compressing it for distribution.
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| == Deploying a Connector ==
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| To deploy a connector in BigID:
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| # Enable external connectors in the environment.
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| # Use Docker to run the connector.
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| # Add the connector as a data source in BigID.
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| = Connector Setup and Testing =
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| == Connector Setup ==
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| Adding a connector as a data source involves:
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| # Configuring data source parameters.
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| # Testing the connection.
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| # Saving the configuration.
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| == Testing Your Connector ==
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| Test the connector by running scans and viewing results in the BigID UI.
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