Qarbine Helps MongoDB Developers Deliver Modern Data Insights

Gain Publication Quality In-App Analysis

Qarbine is the Modern Data Collaboration Suite™ for MongoDB Atlas enterprises to gain more value from their application development investments. It is a set of 10+ integrated tools to natively query, analyze and present modern data to improve everyone’s productivity. Qarbine enables developers to easily deliver in-app analytics and insights. Most BI tools rely on SQL interactions and lack nested document model support. As a result, many high value MongoDB features are not available. In contrast, Qarbine supports native MongoDB features and is focused on delivering interactive analytics for the drill down details behind summary dashboards. Qarbine’s detailed reporting complements popular summary visualizations. Developers, end users, and analysts can all benefit by applying Qarbine to their workflows.

Enhancing the Developer Data Platform

Qarbine’s native integration provides access to the full capabilities of the various MongoDB querying features including the many powerful aggregation pipeline ones. Tested and optimized application queries can be used 1:1 within Qarbine. This avoids artificial query translation into SQL syntax required by legacy tools. Native queries provide the best way to access MongoDB’s many developer data platform features. 

MongoDB Atlas unifies operational, analytical, and vector search data services to streamline building AI applications. The recent MongoDB AI Application Program (MAAP) announcement offers enterprises strategic advice, professional services, and an integrated end-to-end technology stack for building generative AI applications. Qarbine directly supports MongoDB’s Gen AI vector search capability and also the Atlas Full Text Search (FTS) and Atlas SQL features. Legacy SQL-centric tools are greatly challenged to access these powerful MongoDB features. The initial “GenAI release” will have integrations with popular Gen AI service providers. Multi-modal searches and presenting of multi-media data provide developers with a spectrum of innovative possibilities.

Leveraging the Document Data model

The MongoDB document data model enables data to better model the business application domain. The flexible schema architecture allows different fields and data types, unlike the rigid structure of relational database tables. This improves developer productivity and overal application design. This flexibility places demands on any analysis and reporting tool. Qarbine is built to process such dynamic data structures using various techniques including cell, line and group conditional logic. Embedded documents and embedded arrays are processed in their natural form as well. As a result, analysis templates and their formulas are much more intuitive and easier to author. Legacy SQL tools requiring the flattening MongoDB documents into a homogeneous tabular form which explodes the answer set size and entirely loses the business model object benefits.

Improving the Productivity of Developers and Others

Developers are the initial beneficiaries of Qarbine’s robust Modern Data Collaboration Suite™. Qarbine provides 10+ integrated tools for querying, designing user dialogs, authoring templates, accessing the shared catalog managements, and administering the system. Among its many tools is a template designer providing the power of Microsoft Word formatting, Excel formulas, and PowerPoint layout features all in one. 

Many of these tools offer benefits for other enterprise roles ranging from end users to analysts. Qarbine’s shared catalog enables those skilled in MongoDB querying to author retrieval components and store them in the shared catalog. There they may be discovered 24×7 and reused by other enterprise staff with analysis skills and less so native MongoDB querying skills. The separation of data retrieval from data formatting helps better aligns with staff roles and skills which improves everyone’s productivity.

In-App Analytics Flexibility

Qarbine supports the broad variety of MongoDB deployment scenearios ranging from direct online analytical processing (OLAP) for real-time data driven decisions, to using MongoDB secondary and analytic nodes. Atlas Data Lake and Data Federation benefits are readily leveraged. Customers can mix and match MongoDB interactions to best suit their requirements.

In addition, many business decisions can’t be based on stale data. It is much better to have MongoDB be the one source of truth. Modern application developers can embed context and GenAI driven application analytics for seamless end user experiences. Insights are a click away and not in a separate data silo requiring logging in and extensive navigation. Data movement (ETL) approaches typically target SQL data stores and are inherently stale data stores. That’s no way to make business decisions! MongoDB offers a unified operational and analytical data store with AI features that covers a variety of data use cases. Qarbine leverages that flexibility which can avoid the costs of legacy SQL stores.

Supporting the Full Development Life-cycle

Qarbine’s uses span the application lifecycle from the Atlas collection reports for development status to providing publication quality embedded application analysis. In some cases dev teams can entirely avoid the manual coding of application pages. In its embedded form, applications can pass runtime values to Qarbine as analysis report parameters launched from buttons or menus. The results of the Qarbine analysis area visually all part of the application. These pages can drill down details along with various calculations including 

How to Get Started

Staying native with MongoDB analysis interactions is how to best gain the highest ROI from modern data applications. Whether vector search, full text search, analytic nodes, search nodes, data lake, multi-cloud, secondary nodes, or the aggregation pipeline, Qarbine is end-to-end native for your drill down analytic needs.

Review the MongoDB related tutorial here.

Close Menu