MuleSoft Connectors

Enhanced Anypoint Platform Connectivity with CData Connectors for MuleSoft.

105+ NoSQL, Big Data, and SaaS Connectors for the MuleSoft Anypoint Platform. The fastest and easiest way to integrate data with MuleSoft.

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The Easiest Way to Connect MuleSoft With SaaS, NoSQL, & Big Data

A powerful set of MuleSoft Connectors that simplify the process of connecting to enterprise data. Connect with Accounting, CRM, ERP, Marketing Automation, On-Premise and Cloud Data Stores through SQL - just like using the MuleSoft Database Connector.

Write SQL, Get Data

Powerful SQL abstraction simplifies connectivity and decouples data access.

Fully-Integrated Connectors

Seamless integration with the Anypoint Studio workflow designer.

Full CRUD Support

Bi-directional dsata access with full Read, Write, Update, and Delete support.

Enterprise-Class Security

Advanced Security and Authentication. Secure TLS/ SSL data encryption.

Connectors for MuleSoft Anypoint Platform

Data-centric connectivity to SaaS, NoSQL, & Big Data. Leverage the power of SQL to simplify data integration with MuleSoft.

Key Features

  • Data-Centric Operations (SQL Access to Data)

    CData Connectors for MuleSoft function in a similar way to the Mule 4 Anypoint Connector for Database. They interoperate with MuleSoft DataWeave to offer dynamic data access from MuleSoft workflow.

  • Fully-Dynamic Queries

    Users can parameterize all parts of the Query passed to the Connector. This capability includes the WHERE clause as well as the ability to parameterize parts of the query itself.

  • Streaming & Bulk Operations

    Connectors support data streaming and bulk operations by default, making operations over large datasets fast and efficient. The connectors include integrated paging that streamlines data access, and support bulk and batch operations for reading and writing.

  • Intelligent Query Folding

    Improved performance through intelligent query folding. Connectors push data operations server-side wherever possible to improve execution performance. Operations that cannot be executed server side are handled client-side by the robust embedded SQL Engine.

  • Metadata Discovery

    Extensive schema discovery capabilities. Explore tables, columns, keys, and other data constructs based on user identity.

  • Replication and Caching

    Our replication and caching commands make it easy to copy data to local and cloud data stores such as Oracle, SQL Server, Google Cloud SQL, etc. The replication commands include many features that allow for intelligent incremental updates to cached data.

  • String, Date, & Numeric Function

    Includes a library of 50 plus functions that can manipulate column values into the desired result. Popular examples include Regex, JSON, and XML processing functions.

  • Collaborative Query Processing

    Connectors enhance the data source's capabilities with additional client-side processing, when needed, to enable analytic summaries of data such as SUM, AVG, MAX, MIN, etc.

  • Customizable and Configurable

    Data models can easily be customized to add or remove tables/columns, change data types, etc. without requiring a new build. Customizations are supported at runtime using human-readable schema files that are easy to edit.

  • All-in-One Deployment

    Standard MuleSoft Connectors that can be easily deployed to any supported MuleSoft platform.

Superior Data-Driven Integration

Simplify architectural complexity and speed solution delivery

Connectors found elsewhere are often tightly bound to the source APIs that they connect with. The object models, data models, interfaces, and service capabilities all derive from dissimilar APIs making many integrations overly complicated and fragile.

The CData Connectors for MuleSoft are a giant leap forward in Mule-based data integration. Standard SQL interfaces provide a familiar experience across all supported data sources, and shield systems integrators from the complexities of data access. A robust SQL engine simplifies data connectivity and allows users to accomplish complex data manipulation in a single operation that would otherwise require extensive supporting workflow.