Visualizing Your MongoDB Atlas Data with Atlas Charts

MongoDB Atlas is the leading multi-cloud developer data platform. We see some of the world’s largest companies in manufacturing, healthcare, telecommunications, and financial services all build their businesses with Atlas at their foundation.

Every company comes to MongoDB with a need to safely store operational data. But all companies also have a need to analyze data to gain insights into their business and data visualization is core to establishing that real-time business visibility. Data visualization enables the insights required to take action, whether that’s on key sales data, production and operations data, or product usage to improve your applications.

The best way to do this as an Atlas user is by using Atlas Charts – MongoDB’s first-class data visualization tool, built natively into MongoDB Atlas.

Why choose Charts

First, Charts is natively built for the document model. If you’re familiar with MongoDB, you should be familiar with documents. The document model is a data model made for the way developers think. And with Charts, you can take your data from documents and collections in Atlas, and visualize them with no ETL, data movement or duplication. This speeds up your ability to discover insights.

Second, Charts supports all cluster configurations you can create in Atlas, including dedicated clusters, serverless instances, data stored in Online Archive, as well as federated data in Atlas Data Federation. Typically when you learn about a company’s integrated products and services, you find some “gotchas” or limitations that make any benefits come at a significant cost. In the case of a MongoDB Atlas customer, that could come in the form of someone finding out that a cluster configuration option isn’t supported by Charts. But that will never be the case. If you create and manage your application data in Atlas, you can visualize it in Charts. That’s it.

Third, Charts is a robust data visualization tool with a variety of chart types, extensive customization options, and interactivity. Compared to other options in the business intelligence market, you get the same key benefits, without all the complexity. You can learn how to use Charts in a few hours and you can easily teach your team. It’s the simplest data visualization solution for most teams.

Fourth, the value of Charts can extend beyond individual use cases, with sharing and embedding. This lets you both flexibly share charts and dashboards with your team, as well as embed them into contexts that matter most to your data consumers, such as in a blog post or inside your company’s wiki.

Finally, Charts is free for Atlas users up to 1GB per project per month, which covers moderate usage for most teams. There are no seat-based licensing fees associated with Charts, so no matter how many team members you have, Charts will remain a low-cost, if not zero cost solution for your data visualization needs. Beyond the included free usage, it’s just $1/GB transferred per month. You can check out more pricing details here.

How to use Charts

The best way to learn how to use Charts is to simply give it a try. It’s free to use and we have a variety of sample dashboards you can use to get started. But let’s walk through some basics to help illustrate the kinds of visualizations that Charts can enable.

Charts makes visualizing your data easy by automatically making your Atlas deployments (any cluster configuration) available for visualization. If you’re a project owner, you can manage permissions to data sources in Charts. We could write an entire blog post on data sources, but if you’re just getting started, just know that your data is made easily available in Charts unless your project owner intentionally hides it.

Create a dashboard

Everything in Charts starts with a dashboard and creating a dashboard is easy. Simply select the Add Dashboard button at the top right of the Charts page in Atlas. From there, you’ll fill in some basic information like a title and optional description, and you’re on your way.

Here’s what one of our new sample dashboards looks like. They are a great place to start:

Sample Atlas Charts dashboard

Build a chart

Once you have a dashboard created, you can add your first chart. The chart builder gives you a simple and powerful drag and drop interface to help you quickly construct charts. The first step is selecting your data source:

Once you have a data source selected, simply add desired fields into your chart and start customizing. The example below uses our IoT sample dashboard dataset to create a bar chart displaying the total distance traveled by different users. From there you can add filters and further customize your chart by adding custom colors, data labels, and more.

The chart builder even allows you to write, save, and share queries and aggregation pipelines as shown below. You can learn more in our documentation. Play around with the chart builder to get familiar with all of its functionality.

Share and embed

A chart can be useful in itself to individual users, but we see users get the most benefit out of Charts when sharing visualizations with others. Once you have created a dashboard with one or more charts, we offer a variety of options letting you share your dashboards with your team, your organization, or via a public link if your data is not sensitive.

If you would rather embed a chart or dashboard where your team is already consuming information, check out Charts embedding functionality. Charts lets you embed a chart or dashboard via iframe or SDK, depending on your use case. Check out our embedding documentation to learn more.

That was just a brief overview of how to build your first charts and dashboards in Atlas Charts, but there’s a lot more functionality to explore. For a full walkthrough, watch our product demo here:

Atlas Charts is the only native data visualization tool built for the document model and it’s the quickest and easiest way to get started visualizing data from Atlas. We hope this introduction helps you get started using Charts to gain greater visibility into your application data, helping you to make better decisions on your data.

Get started with Atlas Charts today by logging into or signing up for MongoDB Atlas, deploying or selecting a cluster, and navigating to the Charts tab to activate for free.

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Why MongoDB’s Partner Team is Focused like a Laser, Not a Flashlight

Four years ago, I wrote an article about how our Partner and Sales teams work together to ensure success. Since then, our Partner organization has grown five times in size and become even more of a competitive differentiator for MongoDB. As we continue to build lasting relationships with our partners and become even more strategic in how we leverage our partnerships, I’m reflecting on how far the Partner organization has come and where we’re headed. The Partner organization is the x-factor for MongoDB It starts with the customers, but more specifically, developers. Developers are creating some of the most innovative and modern applications with MongoDB, but our developer data platform is only one component of their tech stack. That’s why it’s essential to have an ecosystem of companies who help developers write or modernize their software faster. For MongoDB, this could be system integrators, cloud providers, ISVs who embed MongoDB into their products, technology partners who want to integrate with us, or resellers who enable us to sell MongoDB in new markets and regions. Most companies have a strategy for each and a team that manages these relationships, but there are a few things that make MongoDB’s Partner organization different. First, the people we hire. We look for individuals who have a sales-first mentality, are willing and able to generate pipeline, and can position the value of MongoDB. It’s extremely important for our Partner team to show ROI to our Sales teams, and I’d argue that if your Partner organization can’t do that, you might not need them. As part of the Partner team at MongoDB, you have the opportunity to master your sales skills and be rewarded for your success in finding new partnerships. One of our core MongoDB values is “Own What You Do” and it’s embodied every day on the Partner team. We demand excellence from ourselves. We take accountability for our actions and our success. We are empowered to make things happen. The second thing that sets MongoDB apart is that we manage partnerships like a laser, not a flashlight. We do not measure success by the number of partners we have. We prefer to deeply invest resources in a handful of alliances while we create an ecosystem funnel to drive the next wave of investments. We look for partnerships with organizations that our customers have told us they’d like us to work better with. Though we have over 1,000 partners, we put most of our horsepower into the top 50 based on this feedback. Lastly, the opportunity at MongoDB is enormous. If you are looking to work with a product that people love, and you believe there is an opportunity to be well-compensated for selling and building full solutions around a product, you’ll find that at MongoDB. Driving focus via the Partner Specialist teams At the beginning of this year, we created dedicated specialist teams for Cloud, System Integrator, ISV, VAR, and Tech partners. Customers have told us time and time again that they wanted us to become more intimate with their use cases and the associated ecosystem, and we listened. For example, we now have specialized teams for each cloud partner who know their products inside out and focus on strengthening the relationship by sourcing new opportunities for our sales force. This isn’t something you find in most Partner organizations, as it’s more common for teams to be generalists opposed to specialists. We began experimenting with specialization in 2021, and a highlight of this specialization is our partnership with Amazon Web Services (AWS). In the past, MongoDB and AWS were viewed as competitors rather than partners. In 2021, both sides realized that it’s better to work together and decided to dedicate individuals to build a partnership that has since resulted in an incredible number of co-sell wins. AWS has leaned into MongoDB and continues to position MongoDB Atlas as a preferred database for customers. This puts MongoDB as one of the top three data partners that AWS has globally, and AWS is now MongoDB’s largest partnership in the world . Scaling without diluting impact MongoDB’s Partner organization has quintupled in size since 2019. We have partners in almost every major location around the world and teams who provide regional coverage. With the ROI we’ve seen from specialization, we’ve invested in more specialists and therefore can provide more dedicated resources to each partner. MongoDB’s Partner organization is known as a place with a winning culture where people consistently deliver results. We’ve had many internal transfers from employees who joined MongoDB in Sales, Sales Development, or Marketing and decided to transition into a role on the Partner team. Similarly, our team is focused on providing opportunities for growth. The number of individuals who joined the Partner team as individual contributors and have since been promoted into Director and VP roles is extraordinary. For example, our VP of System Integrator Partner Specialists, Global Lead of Accenture Partner Specialists, RVP of Capgemini Partner Specialists, RVP of Cloud Programs, Global Lead of AWS Partner Specialists, and RVP of Azure Partner Specialists all began their careers as individual contributors here at MongoDB. As we grow our Partner organization, diversity of background, thought, and experiences will continue to be a key differentiator for us. We value different perspectives and view diversity as a way to better serve our customers. Diversity drives a culture of innovation and investing in inclusion helps us serve customers in all markets, giving us a competitive advantage. The future of MongoDB's Partner organization I’m very excited about our coming year. We continue to look for the next partnership to break records with. Whether it's Alibaba , IBM, Databricks , Carahsoft, Microsoft, or Google , working with partners to find new workloads is key to MongoB’s success. MongoDB plans to continue to invest directly in partners via MongoDB ventures as part of this strategy. We also take great pride in promoting folks into leadership positions and we expect even more of that in the year ahead. Our leaders and I live by one of John McMahon’s mottos: "Too many companies think culture is ping-pong, foosball, and beer taps. Helping people win is a culture. Teaching them how to win on their own is a culture. If people aren’t learning, earning, growing, and being promoted, they’re not staying around for the pool table.” This is why we hope you are interested in joining us. We have great products, specialized partnerships, and most importantly, a winning team of fantastic leaders. Want to be part of a team that takes ownership and makes their work matter? View our open roles today .

March 15, 2023
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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024