MongoDB Atlas AWS CloudFormation and CDK Integration Expansion

At MongoDB, we meet our developers where they’re at and offer multiple ways to get started and work with MongoDB Atlas. Since our GA launch of the MongoDB Atlas integration with the AWS CloudFormation Registry at the start of this year, users have had the freedom to manage their MongoDB Atlas resources using familiar YAML or JSON CloudFormation Templates. This provided developers and DevOps teams the core Infrastructure as Code (IaC) benefits: enhanced automation, version control, infrastructure consistency, and improved compliance.

In addition to these updates, we went further and announced support for CDK at MongoDB.Local NYC in June 2023, which allowed development teams to leverage MongoDB Atlas resources natively in the language of their choice: JavaScript, TypeScript, Python, Java, Go, and C#.

Today, just ahead of AWS re:Invent, we are excited to announce several key improvements and expansions to our AWS CloudFormation and CDK integrations that we hope will continue to make developers' lives even easier.

New MongoDB Atlas resources on the AWS CloudFormation Registry

Nine new MongoDB Atlas Resources have been published including Federated Database Instance, Serverless Private Endpoint, Programmatic API Keys Management, MongoDB Atlas Gov Support, and MongoDB Atlas Organization Management. This brings the total MongoDB Atlas Resources count on CloudFormation Registry to 42 and allows developers to do more with MongoDB Atlas and AWS CloudFormation.

AWS region expansion

Are you a developer based in or have your end customers in Hyderabad India, Melbourne Australia, Spain, Switzerland, or the UAE? The good news, we have published all 42 Atlas Resources in each of these new AWS regions as well. Benefits include reduced latency and improved compliance with data sovereignty regulations. This brings the total MongoDB Atlas availability from 22 to 27 AWS regions on the AWS CloudFormation and CDK.

New CDK level 3 resources

The CDK provides different levels of abstraction for defining cloud resources: L1 constructs, which are direct mappings to AWS CloudFormation resources, and higher-level constructs like L2 and L3, which can provide high levels of abstraction. L3 constructs, also known as "Design Patterns" or "High-Level Constructs," combine multiple resources together in commonly used architectures with intelligent defaults, saving developers from manually having to glue L1 and L2 constructs together each time.

Hence, we are happy to announce several new AWS CDK L3 resources including support for MongoDB Atlas Serverless.

Migration to the Atlas Go SDK

Lastly, we are delighted to have migrated our AWS CloudFormation resources to the new Atlas Go SDK. This is the middle layer that translates AWS CloudFormation calls to the Atlas Admin API (which is ultimately responsible for provisioning your MongoDB Atlas infrastructure). This migration goes a long way in accelerating our internal development velocity and enabling us to publish more MongoDB Atlas Resources on AWS CloudFormation soon after they go GA. Learn more about the key benefits of the Atlas Go SDK.

Start building today

These MongoDB Atlas integrations with AWS CloudFormation are free and open-source, licensed under the Apache License 2.0. Users only pay for underlying MongoDB Atlas and AWS resources created and can get started building with the Atlas always-free tier (M0 clusters).

Getting started today is faster than ever with MongoDB Atlas and AWS CloudFormation. We can’t wait to see what you will build next.

Learn more on our MongoDB Atlas and AWS CloudFormation page.

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Building AI with MongoDB: Improving Productivity with WINN.AI’s Virtual Sales Assistant

Better serving customers is a primary driver for the huge wave of AI innovations we see across enterprises. WINN.AI is a great example. Founded in November 2021 by sales tech entrepreneur Eldad Postan Koren and cybersecurity expert Bar Haleva, their innovations are enabling sales teams to improve productivity by increasing the time they focus on customers. WINN.AI orchestrates a multimodal suite of state-of-the-art models for speech recognition, entity extraction, and meeting summarization, relying on MongoDB Atlas as the underlying data layer. I had the opportunity to sit down with Orr Mendelson, Ph.D., Head of R&D at WINN.AI, to learn more. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Tell us a little bit about what WINN.AI is working to accomplish Today’s salespeople spend over 25% of their time on administrative busywork - costing organizations time, money, and opportunity. We are working to change that so that sales teams can spend more time solving their customer’s problems and less on administrative tasks. At the heart of WINN.AI is an AI-powered real-time sales assistant that joins your virtual meetings. It detects and interprets customer questions, and immediately surfaces relevant information for the salesperson. Think about retrieving relevant customer references or competitive information. It can provide prompts from a sales playbook, and also make sure meetings stay on track and on time. After concluding, WINN.AI extracts relevant information from the meeting and updates the CRM system. WINN.AI integrates with the leading tools used by sales teams, including Zoom, Hubspot, Salesforce, and more. Can you describe what role AI plays in your application? Our technology allows the system to understand not only what people are saying on a sales call, but also to specifically comprehend the context of a sales conversation, thus optimizing meeting summaries and follow-on actions. This includes identifying the most important talking points discussed in the meeting, knowing how to break down the captured data into different sales methodology fields (MEDDICC, BANT, etc.), and automatically pushing updates to the CRM. What specific AI/ML techniques, algorithms, or models are utilized in the application? We started out building and training our own custom Natural Language Processing (NLP) algorithms and later switched to GPT 3.5 and 4 for entity extraction and summarization. Our selection of models is based on specific requirements of the application feature – balancing things like latency with context length and data modality. We orchestrate all of the models with massive automation, reporting, and monitoring mechanisms. This is developed by our engineering teams and assures high-quality AI products across our services and users. We have a dedicated team of AI Engineers and Prompts Engineers that develop and monitor each prompt and response so we are continuously tuning and optimizing app capabilities. How do you use MongoDB in your application stack? MongoDB stores everything in the WINN.AI platform. Organizations and users, sessions, their history, and more. The primary driver for selecting MongoDB was its flexibility in being able to store, index, and query data of any shape or structure. The database fluidly adapts to our application schema, which gives us a more agile approach than traditional relational databases. My developers love the ecosystem that has built up around MongoDB. MongoDB Atlas provides the managed services we need to run, scale, secure, and backup our data. How do you see the broader benefits of MongoDB in your business? In the ever-changing AI tech market, MongoDB is our stable anchor. MongoDB provides the freedom to work with structured and unstructured data while using any of our preferred tools, and we leave database management to the Atlas service. This means my developers are free to create with AI while being able to sleep at night! MongoDB is familiar to our developers so we don’t need any DBA or external experts to maintain and run it safely. We can invest those savings back into building great AI-powered products. What are your future plans for new applications and how does MongoDB fit into them? We’re always looking for opportunities to offer new functionality to our users. Capabilities like Atlas Search for faceted full-text navigation over data coupled with MongoDB’s application-driven intelligence for more real-time analytics and insights are all incredibly valuable. Streaming is one area that I’m really excited about. Our application is composed of multiple microservices that are soon to be connected with Kafka for an event-driven architecture. Building on Kafka based messaging, Atlas Stream Processing is another direction we will explore. It will give our services a way of continuously querying, analyzing and reacting to streaming data without having to first land it in the database. This will give our customers even lower latency AI outputs. Everybody WINNs! Wrapping up Orr, thank you for sharing WINN.AI’s story with the community! WINN.AI is part of the MongoDB AI Innovators program , benefiting from access to free Atlas credits and technical expertise. If you are getting started with AI, sign-up for the program and build with MongoDB.

November 20, 2023
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Announcing DirectQuery Support for the MongoDB Atlas Connector for Power BI

Last year, we introduced the MongoDB Atlas Power BI Connector , a certified solution that has transformed how businesses gain real-time insights from their MongoDB Atlas data using their familiar Microsoft Power BI interface. Today, we’re excited to announce a significant enhancement to this integration: the introduction of DirectQuery support. DirectQuery mode provides a direct connection to your MongoDB Atlas database, allowing Power BI to query data in real-time. This means that your Power BI visualizations and reports will always reflect the latest data without importing and storing data within Power BI. This is especially beneficial for analyzing large datasets where up-to-date information is crucial, ensuring decisions are made efficiently without losing performance due to repetitive data imports and storage complexities. How DirectQuery in MongoDB Atlas Power BI Connector works: The Power BI Connector is supported through MongoDB’s Atlas SQL Interface , which is easily enabled from the Atlas console. Atlas SQL, powered by Atlas Data Federation , allows you to integrate data across sources and apply transformations directly, enhancing your analytics. Once enabled, you’ll receive a SQL Endpoint or URL to input into your MongoDB Atlas SQL Connection Dialog within Power BI Desktop. Here, you can choose between two connectivity modes: Import or DirectQuery. Once connected through DirectQuery, Query folding takes place with Power Query , which is how data retrieval and transformation of source data is optimized. You can also achieve data transformation using a SQL Statement, either with the SQL Statement option in the Atlas SQL Interface or within the M Code script accessed via the Power Query Advanced Editor. After your data is transformed and ready for analysis, start building reports with your Atlas data within the Power BI Desktop! Then, simply save, publish, and distribute within the Power BI online app, which is now part of the Microsoft Fabric platform. Watch our comprehensive tutorial below covering how to connect your Atlas data to Power BI , control SQL schemas in Atlas, and use DirectQuery to gain real-time access to your data for business insights. Power BI Connector for MongoDB Atlas is a Microsoft-certified solution. It not only supports the advanced capabilities of DirectQuery but also continues to offer Import Mode for scenarios where data volume is manageable and detailed data modeling is preferred. Whether you’re analyzing real-time data streams or creating comprehensive reports, the Atlas Power BI Connector adapts to your needs, ensuring your business leverages the full power of MongoDB Atlas. DirectQuery Support is available now and can be accessed by updating your existing MongoDB Atlas Power BI Connector or downloading it here . Start transforming your data analysis and making more informed decisions with real-time Atlas data. Log in and activate the Atlas SQL Interface to try out the Atlas Power BI Connector ! If you are new to Atlas or Power BI, get started for free today on Azure Marketplace or Power BI Desktop .

May 13, 2024