What's New in Atlas Charts: Schedule Dashboard Reports to Share Data with Your Team

Today, we’re introducing an exciting feature addition for teams using Atlas Charts.

Charts project owners can now schedule dashboard reports to be sent via email to keep team members informed about key data. This feature has been heavily requested by some of our largest users as there are many use cases where dashboards may be valuable to your team, but you don’t necessarily want to require anyone to do extra work to access and view data. Enter scheduled dashboard reports in Atlas Charts!

In any dashboard that your team relies on for regular data review, simply schedule a dashboard report. The new Schedule button can be found at the top right of the dashboard screen:

Once you’ve chosen a dashboard from which to create a report, you will see a variety of options letting you customize the content and frequency of your report before you schedule. A report requires basic fields like a name or subject line, recipient list, and optionally, a message for the body of the email.

In addition to a link to the dashboard in Charts, you can choose whether to attach an image or PDF for quick reference in the message itself. Finally, you can set a schedule of daily, weekly, monthly, or quarterly delivery. You can also simply send a single email if you have a one-time need to share a report.

And once you’ve set everything up, your email will be sent on your defined schedule.

As you use scheduled dashboard reports more and more, we created a Reports page where you can manage all reports in your project. Note that if you’re on an free tier, you can try one scheduled report. If you’re on an M2 cluster or higher, you can create up to 100 reports per project.

To learn more, please check out our documentation. We’re always listening to feature requests that will enhance using Charts across teams, so if you have any requests or feedback, please share them with us here.

Log in to Atlas Charts today to schedule your first report! If you’re new to Atlas Charts, get started today by logging into or signing up for MongoDB Atlas.

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How slice Enables Credit Approval in Less Than a Minute for Millions of Indians

Building a fast, simple, and flexible way to make payments and offer credit to 12 million registered users slice, which has emerged as a leading innovator in India’s fintech ecosystem, calls itself a zillennial product built by the zillennials. Zillennials being a term for the demographic group born towards the end of the time period for millennials and beginning of the generation-z time period. The company believes that personalization combined with an extreme focus on superior customer service is the key to building long-lasting relations with young people. Not surprising then that the company’s singular focus is on providing the best consumer payments experience in the world, according to Upendra Kumar Singh, Head of Engineering, slice. Speaking at the December 2022 MongoDB Day at Bangalore, Upendra described how slice is leading the disruption and innovation in the fintech ecosystem and setting global benchmarks. The company has more than 12 million registered users on its platform. The slice app provides a fast, simple, and flexible way for users to make payments and avail of credit. For the vast majority of Indians, getting credit is typically challenging due to stringent regulations and lack of credit data. Through the use of modern underwriting systems, slice is helping broaden the accessibility to credit in India. The company is among the top pre-paid card providers in the South Asian market. The challenge Providing a seamless credit experience for customers by transforming the ‘Know Your Customer’ (KYC) process slice found itself compared to other fintech apps which were typically ready to use immediately after downloading. They didn’t require users to fill out forms or wait for approvals. Since slice offers a credit product, the process of determining worthiness requires that they match several variables to ascertain the creditworthiness of the user. They would run a reverse look-up process matching the profile of the user to a network of other users to estimate likelihood of default. This process was manual and quite time-consuming, which meant that users needed to wait 24 to 48 hours for the completion of the credit underwriting process. This was inconvenient, especially in scenarios such as flash sales or medical issues where users require credit immediately. The other problem was dynamic spikes in traffic which could bring down the entire infrastructure and, therefore, the whole service. “The team needed the infrastructure to scale seamlessly through a dynamic scaling capability. We also needed the ability to preschedule clusters for scaling when high volumes were anticipated such as in the event of marketing campaigns or monthly billing cycles,” said Upendra Kumar Singh, Hhead of Engineering, slice. The team was also keen to free up time from managing databases to be able to focus on building features that would add value for customers. The solution Real-time computation of more than 100 variables to determine creditworthiness in minimal time From early on MongoDB was one of the core databases the company used in order to take advantage of its flexible schema and speed of development. However, at first the development team self-managed the database. Then as the scale grew, they found there were often dynamic spikes which would threaten to bring down the application and cause an outage. The team realized the need for dynamic scaling capabilities and moved to MongoDB Atlas . slice uses MongoDB Atlas for a number of use cases. One example highlighted in slice’s MongoDB Day presentation was a use case called real time feature store. The slice team took on the challenge of making the onboarding process for credit users more smooth and quick. Code named ‘Project Makhan’ (Makhan means butter in Hindi), the objective was to reduce processing time to less than a minute. To achieve this, the team used Change Streams in MongoDB Atlas to evaluate the user information in real-time. “As and when the user filled out the application with details such as name and gender, we used the Real-time feature store with MongoDB and ML models to compute 100+ direct and indirect variables in real-time to determine if the user was eligible for credit,” said Upendra Kumar Singh, Head of Engineering, slice. When users start filling out details using the mobile app, this information is stored in Atlas and computation starts with derived variables. Once all the variables are computed, an AWS step function is triggered. In turn, this triggers the credit underwriting service, which takes all the computed variables and feeds them into the ML model. The model determines the user’s score between 0 and 100. A rules engine looks out for red flags. The red flags are analyzed using MongoDB to determine the likelihood of the user being a defaulter. This was a manual process, but with MongoDB Atlas, the resolution of red flags happens automatically in real-time. The decision is then communicated to the user. Currently, MongoDB supports 15+ clusters with 17.5 TB data, which is used by more than 12 million registered users. slice can carry out about 15,000 MongoDB input/output operations per second (IOPS) during peak hours. With the auto-scaling capabilities, the team can add or remove nodes on demand using Atlas cluster APIs. Given that the functionalities can often be used for critical workloads in emergency situations, uptime was important. The result Unprecedented processing speed and 99.995% uptime for more than 12 million registered users “The solution enables credit underwriting decisions to be taken in under 30 seconds by calculating more than 100 user variables in real-time. This solution triggered exponential growth of slice as no other player in the industry was providing credit in 30 seconds. With MongoDB’s resilient distributed architecture, slice can achieve a 99.995% uptime SLA. There have been no outages except for those due to manual errors,” said Upendra Kumar Singh, Head of Engineering, slice. MongoDB Atlas provides the core capabilities that slice requires to dynamically scale their business, including the flexibility of the document model, Change Streams, always-on security, continuous backup, easy migrations, real time analytics and native tooling.

April 12, 2023
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MongoDB.local London 2024: Better Applications, Faster

Since we kicked off MongoDB’s series of 2024 events in April, we’ve connected with thousands of customers, partners, and community members in cities around the world—from Mexico City to Mumbai. Yesterday marked the nineteenth stop of the 2024 MongoDB.local tour, and we had a blast welcoming folks across industries to MongoDB.local London, where we discussed the latest technology trends, celebrated customer innovations, and unveiled product updates that make it easier than ever for developers to build next-gen applications. Over the past year, MongoDB’s more than 50,000 customers have been telling us that their needs are changing. They’re increasingly focused on three areas: Helping developers build faster and more efficiently Empowering teams to create AI-powered applications Moving from legacy systems to modern platforms Across these areas, there’s a common need for a solid foundation: each requires a resilient, scalable, secure, and highly performant database. The updates we shared at MongoDB.local London reflect these priorities. MongoDB is committed to ensuring that our products are built to exceed our customers’ most stringent requirements, and that they provide the strongest possible foundation for building a wide range of applications, now and in the future. Indeed, during yesterday’s event, Sahir Azam, MongoDB’s Chief Product Officer, discussed the foundational role data plays in his keynote address. He also shared the latest advancement from our partner ecosystem, an AI solution powered by MongoDB, Amazon Web Services, and Anthropic that makes it easier for customers to deploy gen AI customer care applications. MongoDB 8.0: The best version of MongoDB ever The biggest news at .local London was the general availability of MongoDB 8.0 , which provides significant performance improvements, reduced scaling costs, and adds additional scalability, resilience, and data security capabilities to the world’s most popular document database. Architectural optimizations in MongoDB 8.0 have significantly reduced memory usage and query times, and MongoDB 8.0 has more efficient batch processing capabilities than previous versions. Specifically, MongoDB 8.0 features 36% better read throughput, 56% faster bulk writes, and 20% faster concurrent writes during data replication. In addition, MongoDB 8.0 can handle higher volumes of time series data and can perform complex aggregations more than 200% faster—with lower resource usage and costs. Last (but hardly least!), Queryable Encryption now supports range queries, ensuring data security while enabling powerful analytics. For more on MongoDB.local London’s product announcements—which are designed to accelerate application development, simplify AI innovation, and speed developer upskilling—please read on! Accelerating application development Improved scaling and elasticity on MongoDB Atlas capabilities New enhancements to MongoDB Atlas’s control plane allow customers to scale clusters faster, respond to resource demands in real-time, and optimize performance—all while reducing operational costs. First, our new granular resource provisioning and scaling features—including independent shard scaling and extended storage and IOPS on Azure—allow customers to optimize resources precisely where needed. Second, Atlas customers will experience faster cluster scaling with up to 50% quicker scaling times by scaling clusters in parallel by node type. Finally, MongoDB Atlas users will enjoy more responsive auto-scaling, with a 5X improvement in responsiveness thanks to enhancements in our scaling algorithms and infrastructure. These enhancements are being rolled out to all Atlas customers, who should start seeing benefits immediately. IntelliJ plugin for MongoDB Announced in private preview, the MongoDB for IntelliJ Plugin is designed to functionally enhance the way developers work with MongoDB in IntelliJ IDEA, one of the most popular IDEs among Java developers. The plugin allows enterprise Java developers to write and test Java queries faster, receive proactive performance insights, and reduce runtime errors right in their IDE. By enhancing the database-to-IDE integration, JetBrains and MongoDB have partnered to deliver a seamless experience for their shared user-base and unlock their potential to build modern applications faster. Sign up for the private preview here . MongoDB Copilot Participant for VS Code (Public Preview) Now in public preview, the new MongoDB Participant for GitHub Copilot integrates domain-specific AI capabilities directly with a chat-like experience in the MongoDB Extension for VS Code .

October 3, 2024