132 results

Tackling the 5G Complexity Beast with MongoDB’s Developer Data Platform Simplicity

The advent and commercialization of 5G is driving a sea change in the mobile user experience. This success is evidenced by the booming adoption of 5G-enabled devices. Supporting real-time business, streaming, and gaming applications on a 5G network is essential for telecommunications companies’ enterprise growth but demanding on the systems that support them. As the “cloudification” of network functions continues to evolve, it grows more challenging for older business support systems (BSS) and operations support systems (OSS) to keep up. To address the needs of increasingly complex networks, operators are reevaluating their data strategy by recognizing that a developer-focused data platform, to address the needs of mission critical systems, can enable a greater level of agility across the enterprise. This is the thesis of a new IDC white paper, sponsored by MongoDB, Effective Data Management is Essential for Taming the 5G Network Complexity Beast (doc #US49660722, September 2022). In the analysis, led by Karl Whitelock, Research Vice President, Communication Service Provider - Operations & Monetization, IDC examines the new generation of services that will drive innovation in multiple industries, and reviews solutions for the challenges telecommunications providers will face amid new operations and monetization strategies derived from 5G and mobile edge computing services. Take me straight to the IDC White Paper: Effective Data Management is Essential for Taming the 5G Network Complexity Beast Building business solutions at the network edge As software-driven 5G services evolve through a cloud-native network architecture, complexity grows. Within the multi-technology network, an advancing web of systems connects data from the mobile network to an edge cloud, HCP cloud, the core network, the internet, and back again. To manage this complexity, network automation and extensive data analytics capabilities are key components in delivering a first-class customer experience. The new generation of digital services is 5G enabled. IDC is witnessing demand from social media, streaming video, search, gaming, transport, and industrial internet IoT applications building network traffic, and associated data, at soaring rates. Businesses across diverse industries are jumping on the 5G bandwagon. The business solutions being dreamed up by developers are redefining services and business outcomes, particularly when utilizing delivery at the network edge. For example: Manufacturing Private 5G networks help high-speed production facilities identify defects and remove incorrectly assembled equipment. Architecture/Construction Robots measure architectural layouts and site dimensions are collected during construction. Records are stored in the cloud for later access by inspectors, builders, and customers. Sporting Events Edge computing can be faster and more reliable for processing data at large scale sporting events. This allows organizers to collect and process data to build interactive digital experiences at the edge.

December 7, 2022

MongoDB Named as a Leader in The Forrester Wave™: Translytical Data Platforms, Q4 2022

In The Forrester Wave™: Translytical Data Platforms, Q4 2022, translytical data platforms are described by Forrester as being “designed to support transactional, operational, and analytical workloads without sacrificing data integrity, performance, and analytics scale.” Characterized as next-generation data platforms, the Forrester report further notes that “Adoption of these platforms continues to grow strongly to support new and emerging business cases, including real-time integrated insights, scalable microservices, machine learning (ML), streaming analytics, and extreme transaction processing.” To help users understand this emerging technology landscape, Forrester published its previous Translytical Data Platforms Wave back in 2019. Three years on, Forrester has named MongoDB as a Leader in its latest Translytical Data Platforms Wave. We believe MongoDB was named a Leader in this report due to the R&D investments made in further building out capabilities in MongoDB Atlas , our multi-cloud developer data platform. These investments were driven by the demands of the developer communities we work with day-in, day-out. You told us how you struggle to bring together all of the data infrastructure needed to power modern digital experiences – from transactional databases to analytics processing, full-text search, and streaming. This is exactly what our developer data platform offers. It provides an elegant, integrated, and fully-managed data architecture accessed via a unified set of APIs. With MongoDB Atlas, developers are more productive, they ship code faster and improve it more frequently. Translytics and the Rise of Application-Driven Analytics Translytics is part of an important shift that we at MongoDB call application-driven analytics . By building smarter apps and increasing the speed of business insights, application-driven analytics gives you the opportunity to out-innovate your competitors and improve efficiency. To do this you can no longer rely only on copying data out of operational systems into separate analytics stores. Moving data takes time and creates too much separation between application events and actions. Instead, analytics processing has to be “shifted left” to the source of your data – to the applications themselves. This is the shift MongoDB calls application-driven analytics . It’s a shift that impacts both the skills and the technologies developers and analytics teams use every day. This is why understanding the technology landscape is so important. Overall, MongoDB is good for customers that are driving their strategy around developers who are tasked with building analytics into their applications. The Forrester Wave™: Translytical Data Platforms, Q4 2022 Evaluating the top vendors in the Translytic Data Platforms Wave Forrester evaluated 15 of the most significant translytical data platform vendors against 26 criteria. These criteria span current offering and strategy through to market presence. Forrester gave MongoDB the highest possible scores across eleven criteria, including: Number of customers Performance Scalability Dev Tools/API Multi-model Streaming Cloud / On-prem / distributed architecture Commercial model The report cites that “MongoDB ramps up its translytical offering aggressively”, and that “Organizations use MongoDB to support real-time analytics, systems of insight, customer 360, internet of things (IoT), and mobile applications.” Access your complimentary copy of the report here . Customer Momentum Many development teams start out using MongoDB as an operational database for both new cloud-native services as well as modernized legacy apps. More and more of these teams are now improving customer experience and speeding business insight by adopting application-driven analytics. Examples include: Bosch for predictive maintenance using IoT sensor data. Keller Williams for relevance-based property search and sales dashboarding. Iron Mountain for AI-based information discovery and intelligence. Volvo Connect for fleet management. Getting started on your Translytics Journey The MongoDB Atlas developer data platform is engineered to help you make the shift to Translytics and application-driven analytics – leading to smarter apps and increased business visibility. The best way to get started is to sign up for an account on MongoDB Atlas . Then create a free database cluster, load your own data or our sample data sets, and explore what’s possible within the platform. The MongoDB Developer Center hosts an array of resources including tutorials, sample code, videos, and documentation organized by programming language and product. Whether you are a developer or a member of an analytics team, it's never been easier to get started enriching your transactional workloads with analytics!

November 29, 2022

Modernize your GraphQL APIs with MongoDB Atlas and AWS AppSync

Modern applications typically need data from a variety of data sources, which are frequently backed by different databases and fronted by a multitude of REST APIs. Consolidating the data into a single coherent API presents a significant challenge for application developers. GraphQL emerged as a leading data query and manipulation language to simplify consolidating various APIs. GraphQL provides a complete and understandable description of the data in your API, giving clients the power to ask for exactly what they need — while making it easier to evolve APIs over time. It complements popular development stacks like MEAN and MERN , aggregating data from multiple origins into a single source that applications can then easily interact with. MongoDB Atlas: A modern developer data platform MongoDB Atlas is a modern developer data platform with a fully managed cloud database at its core. It provides rich features like native time series collections, geospatial data, multi-level indexing, search, isolated workloads, and many more — all built on top of the flexible MongoDB document data model. MongoDB Atlas App Services help developers build apps, integrate services, and connect to their data by reducing operational overhead through features such as hosted Data API and GraphQL API. The Atlas Data API allows developers to easily integrate Atlas data into their cloud apps and services over HTTPS with a flexible, REST-like API layer. The Atlas GraphQL API lets developers access Atlas data from any standard GraphQL client with an API that generates based on your data’s schema. AWS AppSync: Serverless GraphQL and pub/sub APIs AWS AppSync is an AWS managed service that allows developers to build GraphQL and Pub/Sub APIs. With AWS AppSync, developers can create APIs that access data from one or many sources and enable real-time interactions in their applications. The resulting APIs are serverless, automatically scale to meet the throughput and latency requirements of the most demanding applications, and charge only for requests to the API and by real-time messages delivered. Exposing your MongoDB Data over a scalable GraphQL API with AWS AppSync Together, AWS AppSync and MongoDB Atlas help developers create GraphQL APIs by integrating multiple REST APIs and data sources on AWS. This gives frontend developers a single GraphQL API data source to drive their applications. Compared to REST APIs, developers get flexibility in defining the structure of the data while reducing the payload size by bringing only the attributes that are required. Additionally, developers are able to take advantage of other AWS services such as Amazon Cognito, AWS Amplify, Amazon API Gateway, and AWS Lambda when building modern applications. This allows for a severless end-to-end architecture, which is backed by MongoDB Atlas serverless instances and available in pay-as-you-go mode from the AWS Marketplace . Paths to integration AWS AppSync uses data sources and resolvers to translate GraphQL requests and to retrieve data; for example, users can fetch MongoDB Atlas data using AppSync Direct Lambda Resolvers. Below, we explore two approaches to implementing Lambda Resolvers: using the Atlas Data API or connecting directly via MongoDB drivers . Using the Atlas Data API in a Direct Lambda Resolver With this approach, developers leverage the pre-created Atlas Data API when building a Direct Lambda Resolver. This ready-made API acts as a data source in the resolver, and supports popular authentication mechanisms based on API Keys, JWT, or email-password. This enables seamless integration with Amazon Cognito to manage customer identity and access. The Atlas Data API lets you read and write data in Atlas using standard HTTPS requests and comes with managed networking and connections, replacing your typical app server. Any runtime capable of making HTTPS calls is compatible with the API. Figure 1:   Architecture details of Direct Lambda Resolver with Data API Figure 1 shows how AWS AppSync leverages the AWS Lambda Direct Resolver to connect to the MongoDB Atlas Data API. The Atlas Data API then interacts with your Atlas Cluster to retrieve and store the data. MongoDB driver-based Direct Lambda Resolver With this option, the Lambda Resolver connects to MongoDB Atlas directly via drivers , which are available in multiple programming languages and provide idiomatic access to MongoDB. MongoDB drivers support a rich set of functionality and options , including the MongoDB Query Language, write and read concerns, and more. Figure 2:   Details the architecture of Direct Lambda Resolvers through native MongoDB drivers Figure 2 shows how the AWS AppSync endpoint leverages Lambda Resolvers to connect to MongoDB Atlas. The Lambda function uses a MongoDB driver to make a direct connection to the Atlas cluster, and to retrieve and store data. The table below summarizes the different resolver implementation approaches. Table 1:   Feature comparison of resolver implementations Setup Atlas Cluster Set up a free cluster in MongoDB Atlas. Configure the database for network security and access. Set up the Data API. Secret Manager Create the AWS Secret Manager to securely store database credentials. Lambda Function Create Lambda functions with the MongoDB Data APIs or MongoDB drivers as shown in this Github tutorial . AWS AppSync setup Set up AWS Appsync to configure the data source and query. Test API Test the AWS AppSync APIs using the AWS Console or Postman . Figure 3:   Test results for the AWS AppSync query Conclusion To learn more, refer to the AppSync Atlas Integration GitHub repository for step-by-step instructions and sample code. This solution can be extended to AWS Amplify for building mobile applications. For further information, please contact .

November 23, 2022

MongoDB and AWS: Simplifying OSDU Metadata Management

In this decade of the 2020s, the energy sector is experiencing two major changes at the same time: The transition from fossil to renewables, and the digital transformation that changes the way businesses operate through better applications and tools that help streamline and automate processes. To support both of these challenges, the Open Group OSDU Forum has created a new data platform standard for the energy industry that seeks to reduce data silos and enable transformational workflows via an open, standards-based API set and supporting ecosystem. OSDU (Open Subsurface Data Universe) is an industry-defining initiative that provides a unified approach to store and retrieve data in a standardized way in order to allow reductions in infrastructure cost, simplify the integration of separate business areas, and adopt new energy verticals within the same architectural principles. Amazon Web Services (AWS) — as an early supporter of OSDU — provides a premier, cloud-first offering available across more than 87 availability zones and 27 regions. MongoDB — an OSDU member since 2019 — and AWS are collaborating to leverage MongoDB as part of the AWS OSDU platform for added flexibility and to provide a robust multi-region OSDU offering to major customers. Why MongoDB for OSDU? OSDU provides a unique challenge, as its architecture is set to support a varied data set originating from the oil and gas industry, while also being extensible enough to support the expanding requirements of new energy and renewables. It must be able to support single-use on a laptop for beginning practitioners, yet scale to the needs of experts with varying deployment scenarios — from on-premises, in-field, and cloud — and from single tenant on one region to multi-region and multi-tenant applications. Furthermore, OSDU architectural principles separate raw object data from the metadata that describes it, which puts an additional burden on the flexibility needed to manage OSDU metadata, while supporting all the above requirements. Enter MongoDB Since 2008, MongoDB has championed the use of the document model as the data store that supports a flexible JSON-type structure, which can be considered a superset of different existing data types — from tabular, key-value, and text to geo-spatial, graph, and time series. Thus, MongoDB has the flexibility not only to support just the main metadata services in OSDU but also to adapt to the needs of domain-specific services as OSDU evolves. The flexibility of MongoDB allows users to model and query the data in a variety of ways within the same architecture without the need to proliferate disparate databases for each specific data type, which incurs overhead both in terms of deployment, cost and scale, and the ability to query. The schema flexibility inherent in this document model allows developers to adapt and make changes quickly, without the operational burden that comes with schema changes with traditional tabular databases. MongoDB can also scale from the smallest environment to massive, multi-region deployments, with cross-regional data replication support that is available today across more than 90 regions with MongoDB Atlas . With the addition of MongoDB’s cluster-to-cluster sync , MongoDB can easily support hybrid deployments bridging on-premises or edge to the cloud, a requirement that is increasingly important for energy supermajors or for regions where data sovereignty is paramount. Example: LegalTag An example of the benefit of MongoDB’s document model is OSDU’s LegalTag Compliance Service , which governs the legal status of data in the OSDU data ecosystem. It is a collection of JSON properties that governs how the data can be consumed and ingested. With MongoDB, the properties are directly stored, indexed, and made available to be queried — even via full-text search for more advanced use cases. The schema flexibility simplifies integrating additional derived data from ingested data sources, which is utilized for the further enrichment of the LegalTag metadata. Here the JSON document can accommodate more nodes to integrate this data without the need for new tables and data structures that need to be created and managed. AWS OSDU with MongoDB MongoDB and AWS collaborated to provide a MongoDB-based metadata implementation (Figure 1), which is available for all main OSDU services: Partition, Entitlements, Legal, Schema, Storage. The AWS default ODSU Partition service leverages MongoDB due to its simple replication capabilities (auto-deployable via CloudFormation, Terraform, and Kubernetes), which simplify identifying the correct connection information at runtime to the correct OSDU partition in a multi-region and multi-cluster deployment. The OSDU Entitlements service manages authorization and permissions for access to OSDU services and its data-using groups. The most recent OSDU reference implementation for Entitlements leverages a graph model to manage the relationship between groups, members, and owners. Thus, AWS again chose MongoDB with its inherent graph capabilities through the document model to simplify the implementation without the need to integrate a further dedicated database technology into the architecture. Figure 1:   MongoDB metadata service options with AWS OSDU. Other potential benefits for OSDU MongoDB also offers workload isolation , which provides the ability to dedicate instances only for reporting workloads against the operational dataset. This provides the ability to create real-time observability of the system based on the activity on metadata. Triggers and aggregation pipelines allow the creation of an alternate view of activity in real-time, which can easily be visualized via MongoDB Charts (part of Atlas) without the need for a dedicated visualization system. Flexibility and consistency A major use case for both the energy industry and the direction of OSDU is the ability to capture and preprocess data closest to where it originated. For remote locations where direct connections to the cloud are prohibitive, this approach is often the only option — think Arctic or off-shore locations. Additionally, certain countries have data sovereignty laws that require an alternative deployment option outside of the public cloud. A MongoDB-based OSDU implementation can provide a distinct advantage, as MongoDB as a data platform itself supports deployment in the field (e.g., off-shore), on-premises, in private cloud (e.g., Kubernetes, Terraform), public cloud (e.g., AWS) and as a SaaS implementation (e.g., Atlas). Adoption of MongoDB for OSDU provides consistency across different deployment/cloud scenarios, thereby reducing the overhead for managing and operating a disparate set of technologies where multiple scenarios are required. Conclusion OSDU has been created to change the way data is collected and shared across the oil and gas and energy industry. Its intent is to accelerate digital transformation within the industry. The range of use cases and deployment scenarios requires a solution that provides flexibility in the supported datasets, flexibility for the developer to innovate without additional schema and operational burden, as well as flexibility to be deployable in various environments. Through the collaboration of AWS and MongoDB, there is an additional metadata storage option available for OSDU that provides a modern technology stack with the performance and scalability for the most demanding scenario in the energy industry. 1. MongoDB Atlas 2. MongoDB Edge Computing 3. OSDU Data platform on AWS

November 22, 2022

Manage and Store Data Where You Want with MongoDB

Increasingly, data is stored in a public cloud as companies realize the agility and cost benefits of running on cloud infrastructure. At any given time, however, organizations must know where their data is located, replicated, and stored — as well as how it is collected and processed to constantly ensure personal data privacy. Creating a proper structure for storing your data just where you want it can be complex, especially with the shift towards geographically dispersed data and the need to comply with local and regional privacy and data security requirements. Organizations without a strong handle on where their data is stored potentially risk millions of dollars in regulatory fines for mishandling data, loss of brand credibility, and distrust from customers. Geographically dispersed data and various compliance regulations also impact how organizations design their applications, and many see these challenges as an opportunity to transform how they engage with data. For example, organizations get the benefits of a multi-cloud strategy and avoid vendor lock-in, knowing that they can still run on-premises or on a different cloud provider. However, a flexible data model is needed to keep data within the confines of the country or region where the data originates. MongoDB runs where you want your data to be — on-premises, in the cloud, or as an on-demand, fully managed global cloud database. In this article, we’ll look at ways MongoDB can help you keep your data exactly where you need it. Major considerations for managing data When managing data, organizations must answer questions in several key areas, including: Process: How is your company going to scale security practices and automate compliance for the most prevalent data security and privacy regulatory frameworks? Penalties: Are your business leaders fully aware of the costs associated with not adhering to regulations when storing and managing your data? Scalability: Do you have an application that you anticipate will grow in the future and can scale automatically as demand requires? Infrastructure: Is legacy infrastructure keeping you from being able to easily comply with data regulations? Flexibility: Is your data architecture agile enough to meet regulations quickly as they grow in breadth and complexity? Cost: Are you wasting time and money with manual processes when adhering to regulations and risking hefty fines related to noncompliance? How companies use MongoDB to store data where they want and need it When storing and managing data in different regions and countries, organizations must also understand the rules and regulations that apply. MongoDB is uniquely positioned to support organizations to meet their data goals with intuitive security features and privacy controls, as well as the ability to geographically deploy data clusters and backups in one or several regions. Zones in sharded clusters MongoDB uses sharding to support deployments with very large data sets and high-throughput operations. In sharded clusters, you can create zones of sharded data based on the shard key, which helps improve the locality of data. Network isolation and access Each MongoDB Atlas project is provisioned into its own virtual private cloud (VPC), thereby isolating your data and underlying systems from other MongoDB Atlas users. This approach allows businesses to meet data requirements while staying highly available within each region. Each shard of data will have multiple nodes that automatically and transparently fail over for zero downtime, all within the same region. Multi-cloud clusters MongoDB Atlas is the only globally distributed, multi-cloud database. It lets you deploy a single cluster across AWS, Microsoft Azure, and Google Cloud without the operational complexity of managing data replication and migration across clouds. With the ability to define a geographic location for each document, your teams can also keep relevant data close to end users for regulatory compliance. IP whitelists IP whitelists allow you to specify a specific range of IP addresses against which access will be granted, delivering granular control over data. Queryable encryption Queryable encryption enables encryption of sensitive data from the client side, stored as fully randomized, encrypted data on the database server side. This feature delivers the utmost in security without sacrificing performance and is available on both MongoDB Atlas and Enterprise Advanced. MongoDB Atlas global clusters Atlas global clusters allow organizations with distributed applications to geographically partition a fully managed deployment in a few clicks and control the distribution and placement of their data with sophisticated policies that can be easily generated and changed. Thus, your organization can not only achieve compliance with local data protection regulations more easily but also reduce overhead. Client-Side Field Level Encryption MongoDB’s Client-Side Field Level Encryption (FLE) dramatically reduces the risk of unauthorized access or disclosure of sensitive data. Fields are encrypted before they leave your application, protecting them everywhere — in motion over the network, in database memory, at rest in storage and backups, and in system logs. Segmenting data by location with sharded clusters As your application gets more popular, you may reach a point where your servers will reach their maximum load. Before you reach that point, you must plan for scaling your database to adjust resources to meet demand. Scaling can occur temporarily, with a sudden burst of traffic, or permanently with a constant increase in the popularity of your services. Increased usage of your application brings three main challenges to your database server: The CPU and/or memory becomes overloaded, and the database server either cannot respond to all the request throughput, or do so in a reasonable amount of time. Your database server runs out of storage and thus cannot store all the data. Your network interface is overloaded, so it cannot support all the network traffic received. When your system resource limits are reached, you will want to consider scaling your database. Horizontal scaling refers to bringing on additional nodes to share the load. This process is difficult with relational databases because of the difficulty in spreading out related data across nodes. With non-relational databases, this is made simpler because collections are self-contained and not coupled relationally. This approach allows them to be distributed across nodes more simply, as queries do not have to “join” them together across nodes. Horizontal scaling with MongoDB Atlas is achieved through sharding. With sharded clusters, you can create zones of sharded data based on the shard key . You can associate each zone with one or more shards in the cluster. A shard can be associated with any number of zones. In a balanced cluster, MongoDB migrates chunks covered by a zone only to those shards associated with the zone: If one of the data centers goes down, the data is still available for reads unlike a single data center distribution. If the data center with a minority of the members goes down, the replica set can still serve write operations as well as read operations. However, if the data center with the majority of the members goes down, the replica set becomes read-only. Figure 1 illustrates a sharded cluster that uses geographic zones to manage and satisfy data segmentation requirements. Figure 1:   Sharded cluster Other benefits of MongoDB Atlas MongoDB Atlas also provides organizations with an intuitive UI or administration API to efficiently perform tasks that would otherwise be very difficult. Upgrading your servers or setting up sharding without having to shut down your servers can be a challenge, but MongoDB Atlas removes this layer of difficulty through the features described here. With MongoDB, scaling your databases can be done with a couple of clicks. Meeting your data goals with MongoDB Organizations are uniquely positioned to store and manage data where they want it with MongoDB’s range of features discussed above. With the shift towards geographically dispersed data, organizations must make sure they are aware of – and fully understand – the local and regional rules and requirements that apply for storing and managing data. To learn more about how MongoDB can help you meet your data goals, check out the following resources: MongoDB Atlas security, with built-in security controls for all your data Entrust MongoDB Cloud Services with sensitive application and user data Scalability with MongoDB Atlas

November 22, 2022

Optimizing Your MongoDB Deployment with Performance Advisor

We are happy to announce additional enhancements to MongoDB’s Performance Advisor, now available in MongoDB Atlas , MongoDB Cloud Manager , and MongoDB Ops Manager . MongoDB’s Performance Advisor automatically analyzes logs for slow-running queries and provides index suggestions to improve query performance. In this latest update, we’ve made some key updates, including: A new ranking algorithm and additional performance statistics (e.g., average documents scanned, average documents returned, and average object size) make it easier to understand the relative importance of each index recommendation. Support for additional query types including regexes, negation operators (e.g., $ne, $nin, $not), $count, $distinct, and $match to ensure we cover with optimized index suggestions. Index recommendations are now more deterministic so they are less impacted by time and provide more consistent query performance benefits. Before diving further into MongoDB’s Performance Advisor, let’s look at tools MongoDB provides out of the box to simplify database monitoring. Background Deploying your MongoDB cluster and getting your database running is a critical first step, but another important aspect of managing your database is ensuring that your database is performant and running efficiently. To make this easier for you, MongoDB offers several out-of-the-box monitoring tools , such as the Query Profiler, Performance Advisor, Real-Time Performance Panel, and Metrics Charts, to name a few. Suppose you notice that your database queries are running slower. The first place you might go is to the metrics charts to look at the “Opcounters” metrics to see whether you have more operations running. You might also look at the “Operation Execution Time” to see if your queries are taking longer to run. The “Query Targeting” metric shows the ratio of the number of documents scanned over the number of documents returned. This datapoint is a great measure of the overall efficiency of a query — the higher the ratio, the less efficient the query. These and other metrics can help you identify performance issues with your overall cluster, which you can then use as context to dive a level deeper and perform more targeted diagnostics of individual slow-running queries . MongoDB’s Performance Advisor takes this functionality a step further by automatically scanning your slowest queries and recommending indexes where appropriate to improve query performance. Getting started with Performance Advisor The Performance Advisor is a unique tool that automatically monitors MongoDB logs for slow-running queries and suggests indexes to improve query performance. Performance Advisor also helps improve both your read and write performance by intelligently recommending indexes to create and/or drop (Figure 1). These suggestions are ranked by the determined impact on your cluster. Performance Advisor is available on M10 and above clusters in MongoDB Atlas as well as in Cloud Manager and Ops Manager. Figure 1:  Performance Advisor can recommend indexes to create or drop. Performance Advisor will suggest which indexes to create, what queries will be affected by the index, and the expected improvements to query performance. All of these user interactions are available in the user interface directly within Performance Advisor, and indexes can be easily created with just a few clicks. Figure 2 shows additional Performance Advisor statistics about the performance improvements this index would provide. The performance statistics that are highlighted for each index recommendation include: Execution Count: The number of queries per hour that would be covered by the recommended index Avg Execution Time: The average execution time of queries that would be covered by the recommended index Avg Query Targeting: The inefficiency of queries that would be covered by the recommended index, measured by the number of documents or index keys scanned in order to return one document In Memory Sort: The number of in-memory sorts performed per hour for queries that would be covered by the recommended index Avg Docs Scanned: The average number of documents that were scanned by slow queries with this query shape Avg Docs Returned: The average number of documents that were returned by slow queries with this query shape Avg Object Size: The average object size of all objects in the impacted collection If you have multiple index recommendations, they are ranked by their relative impact to query performance so that the most beneficial index suggestion is displayed at the top. This means that the most impactful index is displayed at the top and would be the most beneficial to query performance. Figure 2:  Detailed performance statistics. Creating optimal indexes ensures that queries are not scanning more documents than they return. However, creating too many indexes can slow down write performance, as each write operation needs to check each index before writing. Performance Advisor provides suggestions on which indexes to drop based on whether they are unused or redundant (Figure 3). Users also have the option to “hide” indexes as a way to evaluate the impact of dropping an index without actually dropping the index. Figure 3: Performance Advisor shows which indexes are unused or redundant. The Performance Advisor in MongoDB provides a simple and cost-efficient way to ensure you’re getting the best performance out of your MongoDB database. If you’d like to see the Performance Advisor in action, the easiest way to get started is to sign up for MongoDB Atlas , our cloud database service. Performance Advisor is available on MongoDB Atlas on M10 cluster tiers and higher. Learn more from the following resources: Monitor and Improve Slow Queries Monitor Your Database Deployments

November 22, 2022

Start on Your Journey to Operationalize AI-Enhanced Real-Time Applications with MongoDB and Databricks

MongoDB and Databricks have succeeded in two complementary worlds: For MongoDB , the focus is making the world of data easy for developers building applications. For Databricks, the focus is helping enterprises to unify their data, analytics, and AI by combining a data lake's flexibility with the openness, performance, and governance of a data warehouse. Traditionally, these operational and analytical functions have existed in separate domains built by different teams and serving different audiences. Though some will pretend a data warehouse can unify such disparate data and systems, the reality is this approach leaves you making false trade-offs where your developers, your data scientists, and, ultimately, your applications and customers suffer. Data warehouses are not designed to serve consumer-facing applications at scale and process machine learning in real time. It takes the unique application-serving layer of a MongoDB database, combined with the scale and real-time capabilities of a lakehouse, such as Databricks, to automate and operationalize complex and AI-enhanced applications at scale. We observed that a large and growing population of joint customers has for years enabled the flow of data between our two platforms to run real-time businesses and enable a world of application-driven analytics, using MongoDB Connector for Apache Spark . So we asked ourselves: How could we make that a more seamless and elegant experience for these customers? Today we're announcing that Databricks now features MongoDB as a data source within a Databricks notebook , thereby enabling data practitioners with an easier, more curated experience for connecting Databricks with MongoDB Atlas data. This notebook experience makes it simpler for enterprises to deliver real-time analytics, handle complex data warehouse/BI workloads, and to operationalize AI/ML pipelines using the MongoDB Spark Connector . In turn, developer and data teams can collaborate more closely on building a new generation of app-driven intelligence. MongoDB and Databricks are committed to further improve our integration in the coming months. In this post, we'll explain how Databricks can be used as a real-time processing layer for data on MongoDB Atlas using the Spark Connector, extending MongoDB's built-in data processing capabilities like our aggregation framework . We'll also cover how to use Databricks' MongoDB notebook to make this even easier. In future posts we'll outline how to use MongoDB Atlas and Databricks Delta Lake to build sophisticated AI/ML pipelines. Live application data plus the data lakehouse MongoDB Atlas is a fully-managed developer data platform that powers a wide variety of workloads - supporting everything from simple CRUD operations to sophisticated data processing pipelines for analytics and transformation - all with a common query interface. With MongoDB Atlas you can isolate operational and analytical workloads using dedicated analytical nodes. Analytics nodes are read-only nodes that can be exclusively targeted by your queries Let's look at an example. Assume you have long-running analytical queries that you want to run against your cluster and your operations team does not want these queries competing for resources with your regular operational workload. To address this, you add an analytics node to your cluster and then target it in your connection string using an Atlas replica set tag. You can connect to the analytical nodes to run sophisticated aggregation queries, BI and reporting workloads using the Atlas SQL interface , visualize your data using MongoDB Charts , or run Spark jobs using MongoDB’s Spark Connector. For more complex data science and warehousing analytical queries, many enterprises choose the Databricks Lakehouse Platform . Enterprises can also benefit from enriching MongoDB data with data from other internal or external sources in the Databricks Lakehouse. The Databricks Lakehouse Platform combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. This unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science, and machine learning. With Databricks notebooks, developers and analytics teams can collaboratively write code in Python, R, Scala, and SQL, plus explore data with interactive visualizations and discover new insights. You can confidently and securely share code with co-authoring, commenting, automatic versioning, Git integrations, and role-based access controls. As good as MongoDB and Databricks are on their own, together we offer enterprises the unmatched ability to work with live application data across traditionally separate domains. This ability allows your teams to deliver what we call application-driven analytics . How does this work? Using MongoDB and Databricks together MongoDB and Databricks offer several ways to integrate the two systems, but the primary means is MongoDB’s Spark Connector. The Spark connector can be used within Databricks notebooks to directly query live application data managed in MongoDB collections and then loaded into data frames for further processing. You can also transform and/or enrich this data with data ingested from other sources using SparkSQL. Queries issued by the Spark Connector can be pushed down to MongoDB's aggregation framework and indexes for pre-processing, significantly improving query efficiency (measured in milliseconds not seconds or minutes). Result sets generated from the Databricks notebooks can then be inserted back into MongoDB collections or can be pushed into Delta Lake for long-running analytics and machine learning. Easier integration using Databricks' MongoDB Notebook A Databricks notebook is a web-based interface that contains runnable code, visualizations, and explanatory text in the form of paragraphs. It lets personas, such as data scientists and data engineers, build linked sets of code in different languages and visualize results in a format in which they are used to working. Notebooks are great for collaboration and can be easily iterated on and improved. MongoDB and Databricks created an example notebook that has sample code for: Reading the data from MongoDB Atlas collections as is into Spark dataframes. Pre-processing and filtering the data from Atlas collections using the aggregation framework, before passing into Spark dataframes. Enriching/transforming the data using SparkSQL Writing the enriched data back to the MongoDB Atlas collection. Figure 1:   Screenshot of data sources in a Databricks notebook. This notebook can help as an initial template for developers to start building complex transformation jobs on MongoDB data with Databricks platform. Interested in a practical example of how this works? Let's demonstrate how you can run analytics on a sample sales dataset using MongoDB's aggregation framework and visualize it with Charts. The example also explains how you can enrich this data using our Databricks notebook and load that back to MongoDB. Refer to the GitHub repo for the same. Figure 2:   Ways to integrate MongoDB and the Databricks Lakehouse Platform. In addition to Spark, MongoDB and Databricks provide seamless integration through shared Cloud Object stores to enable a more traditional data exchange using analytics-optimized formats such as Parquet, as well as event streaming integration using Apache Kafka. Together, MongoDB and Databricks offer unparalleled abilities to unify and process data from disparate systems in real-time. And now with the newly announced Databricks notebooks integration, data engineers and data scientists have an even easier and more intuitive interface to harness MongoDB data for their most sophisticated analytics and AI processing, making real-time applications more intelligent. Conclusion MongoDB Atlas along with Databricks Platform together will help organizations handle the increasing convergence between operational and analytical workloads. This convergence enables application-driven analytics and will help you build smarter applications and derive the right insights in real-time. Reach out to to learn more.

November 21, 2022

Introducing Pay-As-You-Go MongoDB Atlas on Azure Marketplace

MongoDB was an official sponsor at the recent two-day, jam-packed 2022 Microsoft Ignite event. The centralized theme was “How to empower the customer to do more with less” in the Microsoft Cloud. The interactive conference created a meeting space for professionals to connect in-person with subject matter experts to discuss current and future points of digital transformation, attend workshops, learn key announcements, and discover innovative new offerings. Microsoft officially announced MongoDB to be part of a set of companies that make up the new Microsoft Intelligent Data Platform Partner Ecosystem and we are pleased to highlight our expanded alliance. Our partnership provides a frictionless process for developers to access MongoDB Atlas , the leading multi-cloud developer data platform available on the Microsoft Azure Marketplace . By procuring Atlas through the Azure Marketplace, customers can access a streamlined procurement and billing experience and use their Azure accounts to pay for their Atlas usage. MongoDB is also offering a free trial of the Atlas database through the Azure Marketplace. With the new Pay-As-You-Go Atlas listing on the Azure Marketplace, you only pay for the Atlas resources you use, with no upfront commitment required. You will receive just one monthly invoice on your Azure account that includes your Atlas usage, and you can apply existing Azure committed spend to it. Read the Azure Marketplace documentation to learn how to take advantage of the Microsoft Azure consumption commitment (MACC) and Azure commit to consume (CtC). You can even start free with an M0 Atlas cluster and scale up as needed. A free Atlas cluster comes with 512 MB of storage, out-of-the-box security features, and a basic support plan. If you’d like to upgrade your support plan, you can select one in Atlas and the additional cost will also be billed through Azure. MongoDB offers several support subscriptions with varying SLAs and levels of technical support. Whether you’re a new or existing Atlas customer, you can subscribe to Atlas directly from the Azure Marketplace. After you subscribe, you’ll be prompted to log in or create a new Atlas account. You can then deploy a new Atlas cluster or link your existing cluster(s) to your Azure account. Atlas customers can take advantage of best-in-class database features including: Production-grade security features, such as always-on authentication, network isolation, end-to-end encryption, and role-based access controls to keep your data protected. Global, high availability. Clusters are fault-tolerant and self-healing by default. Deploy across multiple regions for even better guarantees and low-latency local reads. Support for any class of workload. Build full-text search, run real-time analytics, share visualizations, and sync to the edge with fully integrated and native Atlas data services that require no manual data replication or additional infrastructure. New integrations that empower builders, developers, and digital natives to unlock the power of MongoDB Atlas when running on Azure—including PowerApps, PowerAutomate, PowerBI, Synapse, and Purview—to seamlessly add Atlas to existing architectures. With MongoDB Atlas on Microsoft Azure, developers receive access to the most comprehensive, secure, scalable, and cloud–based developer data platform in the market. Now, with the availability of Atlas on the Azure Marketplace, it’s never been easier for users to start building with Atlas while streamlining procurement and billing processes. Get started today through the Atlas on Azure Marketplace listing.

October 19, 2022

MongoDB and Google Partnership Gains Momentum

In April 2022 MongoDB launched a pay-as-you-go Atlas service on Google Cloud Marketplace. As we said at the time, this offering provides developers with a simplified subscription experience and gives enterprises more freedom in how they run MongoDB on Google Cloud. Since that launch, we've had many hundreds of customers sign up from a wide range of industries including Retail, Automotive, Education, Media & Entertainment, Healthcare, and more. But that's not all that happened in the past six months. Developers clearly love to build data-rich applications with MongoDB Atlas, and just as clearly they love to bring that data to life through Google Cloud's data services like Google BigQuery, Vertex AI, and more. To indulge that developer affection for MongoDB + Google Cloud, the two companies have been busy integrating our managed services to help customers make data smarter, more intuitive, and easier to use—wherever developers choose. Making data smart Modern applications must be able to automate the process of capturing and processing the data within an application. Combining real-time, operational, and embedded analytics enables a business to influence and automate decision-making for the app and provide real-time insights for the user. This year MongoDB and Google Cloud have combined to deliver best-in-class, application-level analytics. For example, in the weeks leading up to Google Cloud Next ’22, Google Cloud and MongoDB announced integration of Google BigQuery and MongoDB Atlas, among other Google data announcements . Many enterprises turn to BigQuery for its powerful, simple approach to data warehousing needs, but applying it to data in MongoDB wasn't always straightforward. To make moving and transforming data between Atlas and BigQuery easier, the MongoDB and Google teams worked together to build Dataflow templates that make it simple to package a Dataflow pipeline for deployment. The two companies also announced the integration of Atlas and BigQuery with Vertex AI to bring the power of Google's machine learning/AI expertise to MongoDB data. Developers can access a reference architecture and demo for retail and finance fraud detection scenarios. More integrations will roll out over the coming months. All of which is great for customers. For years customers like Universe, part of Live Nation, have used MongoDB with Google Cloud services such as Cloud Pub/Sub, Cloud Dataflow, and BigQuery to build data pipelines and more. In early 2022, Forbes, a 100-year old leader in business journalism, turned to MongoDB and Google Cloud to deliver a recommendation engine for its journalists, which uses Google Cloud's machine learning services to make suggestions to appropriate contributors. These and other customers have discovered that MongoDB's data platform and Google's data services are truly better together. Making data intuitive All those data smarts don't amount to much if developers can't easily make use of them. Over the past six months, MongoDB and Google Cloud have further partnered to ensure a simple, intuitive developer experience. For example, we've made it incredibly easy to deploy a serverless, MEAN stack (MongoDB, ExpressJS, AngularJS, NodeJS) application with Google Cloud Run (you can read the how-to or watch a video tutorial). Similarly, we've also combined with Vercel to make it simple to build full-stack serverless apps. Serverless means you don't need to worry about any hassle associated with managing infrastructure, and Cloud Run means deployment is also a breeze. More collaboration like this will follow, all with the goal of reducing developer friction and making it easier to use stacks that combine Google and MongoDB products together. Additionally, we've made it straightforward for developers to extend their MongoDB applications with APIs using Google's Apigee, a platform for managing and securing their APIs. For example, developers increasingly turn to Apigee and MongoDB to help enterprises pull data from legacy systems without needing the cumbersome process of integrating legacy systems. Recently, the MongoDB connector has been released in pre-GA to help developers build their APIs with MongoDB even more quickly. Developers love these and other integrations. For example, Conrad, a leading European retailer, needed to find a way to build an online B2B marketplace for its own and third-party products. Conrad turned to Atlas and Google Cloud. Together, the companies partnered to help Conrad shift to a microservices-based architecture and delivered a simple, fast, and comprehensive data environment. In like manner, TIM, a global fixed, mobile, cloud, and data center service provider, has leaned on Atlas and Google Cloud to create a dynamic data infrastructure, which has led to a dramatic improvement in customer satisfaction scores. Making data omnipresent MongoDB has always put a premium on developer flexibility, which has not only meant unparalleled support for a wide variety of languages, frameworks, etc., but also flexibility in deployment, including multicloud. Google, for its part, has been a leader in multicloud with Anthos, a platform that enables enterprises to manage GKE clusters and workloads running on virtual machines across environments. It's a way for developers to build once and deploy anywhere, including at the edge, in the data center, or on another cloud, yet with a single cloud control plane. Very cool. Among other benefits, this is a great way for enterprises to meet regulatory and data sovereignty requirements. It is not, however, the only way enterprises can attain that benefit with MongoDB and Google Cloud. As recently announced, MongoDB and Google Cloud have collaborated to give European customers additional choice in where they can securely keep their data, by making MongoDB available on the T-Systems Sovereign Cloud powered by Google Cloud. Finally, MongoDB and Google Cloud have announced the availability of MongoDB Enterprise Advanced on Google Cloud Marketplace. As much as developers love cloud, sometimes they have the need to self-manage MongoDB. With this listing we together offer that freedom. Now is the time to give it a try MongoDB and Google keep giving developers increasingly rich ways to make use of data with operational, application-centered analytics and ML/AI, while also serving up a wide array of choices of where to run those applications. There are many reasons to run MongoDB Atlas on Google Cloud, and one of the easiest is with our self-service, pay-as-you-go listing on Google Cloud Marketplace . Please give it a try and let us know what you think. Try our self-service, pay-as-you-go listing on Google Cloud Marketplace today.

October 11, 2022

Introducing Snapshot Distribution in MongoDB Atlas

Data is at the heart of everything we do and in today’s digital economy has become an organization's most valuable asset. But sometimes the lengths that need to be taken to protect that data can present added challenges and result in manual processes that ultimately slow development, especially when it comes to maintaining a strict backup and recovery strategy. MongoDB Atlas aims to ease this burden by providing the features needed to help organizations not only retain and protect their data for recovery purposes, but to meet compliance regulations with ease. Today we’re excited to announce the release of a new backup feature, Snapshot Distribution. Snapshot Distribution allows you to easily distribute your backup snapshots across multiple geographic regions within your primary cloud provider with the click of a button. You can configure how snapshots are distributed directly within your backup policy and Atlas will automatically distribute them to other regions as selected—no manual process necessary. How to distribute your snapshots To enable Snapshot Distribution, navigate to the backup policy for your cluster and select the toggle to copy snapshots to other regions. From there, you can add any number of regions within your primary cloud provider—including regions you are not deployed in—to store snapshot copies. You can even customize your configuration to copy only specific types of snapshots to certain regions. Copy snapshots to other regions Restore your cluster faster with optimized, intelligent restores If you need to restore your cluster, Atlas will intelligently decide whether to use the original snapshot or a copied snapshot for optimal restore speeds. Copied snapshots may be utilized in cases where you are restoring to a cluster in the same region as a snapshot copy, including multi-region clusters if the snapshots are copied to every cluster region. Alternatively, if the original snapshot becomes unavailable due to a regional outage within your cloud provider, Atlas will utilize a copy in the nearest region to enable restores regardless of the cloud region outage. Perform point in time restore Get started with Snapshot Distribution Although storing additional snapshot copies in varying places may not always be required, this can be extremely useful in several situations, such as: For organizations who have a compliance requirement to store backups in different geographical locations from their primary place of operation For organizations operating multi-region clusters looking for faster direct-attach restores for the entire cluster If you fall into either of these categories, Snapshot Distribution may be a valuable feature addition to your current backup policy, allowing you to automate prior manual processes and free up development time to focus on innovation. Check out the documentation to learn more or navigate to your backup policy to enable this feature. Enable Snapshot Distribution

September 29, 2022

How to Use MongoDB Atlas to Make Your CRM More Efficient

As part of digital transformation, many companies want to optimize their internal business processes, gain more visibility into important business metrics, and create new automation routines. Data is always at the core of business processes and metrics, and most business-critical data is often located in one or a few repositories, such as a customer relationship management system (CRM). Historically business users have relied on spreadsheets and enterprise data warehouses for bringing the data together and making decisions. These solutions can range from a disjointed set of dashboards to an all-in-one central console. But businesses that need to move fast need to iterate on their data and processes fast, and they can’t do that if implementing a change in CRM takes months or if the things are done manually in spreadsheets. This article describes how MongoDB Professional Services created an internal solution to address these issues. Our approach In MongoDB Professional Services, we also needed to streamline our business processes and get out of spreadsheets for business management, especially for revenue forecasting. As the organization grew, the amount of manual labor associated with spreadsheet maintenance became untenable, and making sense of the data became more difficult, especially when the data might be inconsistent, stale, or even inaccurate. Ordinarily, a good CRM or Professional Services Automation (PSA) system can help solve this problem. At MongoDB, for example, we use Salesforce, which provides decent flexibility, but also requires heavy customization and has limitations. We’ve also seen MongoDB customers address the problem by building ETL pipelines into MongoDB Atlas and taking advantage of MongoDB’s flexible schema, query language and aggregation framework, and Atlas Search . The data from source systems is ingested as-is or remapped to create a single view. The best approach we’ve found, however, is to optimize the schema for how the data will be consumed, with different parts of documents potentially coming from different source systems. Atlas App Services provides a serverless abstraction layer that allows fine-grained but flexible control over the schema to help you avoid conflicts and iterate without breaking compatibility. After considering alternatives, we created an internal CRM/PSA-augmenting system that is built on top of the MongoDB Atlas platform to provide us with additional capabilities and flexibility. This solution allows Professional Services to rapidly deliver advanced functionality, such as revenue forecasting, automation, and visibility into complex business metrics. The solution also allows Professional Services to address business systems' needs and promptly react to changes, with functionality beyond what is typically provided by other systems. MongoDB’s internal solution, at its core, is serverless and data-centric, leveraging Atlas App Services functions and triggers for processing the data and Atlas Search for full-text search. It uses Connector for BI , Atlas GraphQL API , and App Services wire protocol and Atlas Functions to access and manipulate data from other components. Its components include a React-based console application, Atlas Charts, Tableau dashboards, Google Sheets, and microservices for data import and integrations. Project view of our internal solution console. Revenue forecasting module in our internal solution console. MongoDB Charts shows business metrics. Solution architecture The data architecture in our internal solution builds on the single view approach and the data-mart concept. The main idea is to ingest relevant data from Salesforce and other systems, enrich it, and build on it quickly, as shown in the following image. We followed these eight key principles to help enable this functionality: Focus on bringing in data in the form that makes the most sense for the business. And, find the right balance between making the ETL easy and optimizing for the foreseen application use cases. Apply transformations in the ETL process to make the ingested data intuitive, including document hierarchy, field names, and data types. Clearly define the data lifecycle in terms of data producers and consumers. Data producers can only overwrite documents and fields that they “own” - and only those. For example, the ETL process from the source system should overwrite the data in MongoDB documents as needed, but it should only modify those fields that are actually coming from the pipeline. Aim to structure MongoDB documents in a way that makes it clear which fields are owned by what producer. Atlas App Services schema and rules can help ensure that the most critical documents and fields are correctly accessed and modified. Use the Atlas Functions and App Services wire protocol in applications and services, as opposed to directly connecting to the Atlas instance. This allowed us to use Google SSO in the console without requiring any sophisticated security mechanisms when we need to do regular CRUD operations from within the application. For complex data logic and on-the-fly calculations, use App Functions . Use database triggers for propagating changes and generating data-driven events. Use scheduled triggers for generating aggregated views and periodic work. Use external services for communicating with the outside world (e.g., email sender, ETL job). The external services are invoked asynchronously by listening on change streams from their respective namespaces (pub-sub model). All external services work independently of each other. Don’t overthink. MongoDB Atlas’s Developer Data Platform offers a lot of flexibility and, if these principles are followed, making changes and iterating on a working system is surprisingly easy. To reiterate the last point, our internal solution is easy to modify and extend because of the flexible schema concept in MongoDB and the independence of external components. Users can access the data through available tools and integrations, and developers can update specific parts of the system or introduce new ones without delays, making this solution efficient in terms of both cost and effort. Conclusion Through this example of our internal solution, we demonstrated that by leveraging MongoDB Atlas in full force, you can solve seemingly intractable business problems with speed, efficiency, and robustness on top of what regular systems can do. Whether you’re optimizing your company’s business processes, building business dashboards, or improving automation, the MongoDB Atlas developer data platform can help make the process easier. Learn how MongoDB’s consulting engineers can help you with design and architecture decisions and accelerate your development efforts. Contact us to learn more .

September 12, 2022

Introducing MongoDB’s Prometheus Monitoring Integration

Wouldn’t it be great if you could connect your data stored in the world’s leading document database to the leading open source monitoring solution? Absolutely! And now you can. Prometheus has been a longstanding developer favored solution by providing monitoring and alerting functionality for cloud-native environments. It has key features like a multi-dimensional data model with time series support, a flexible query language to leverage their dimensionality called PromQL, and no reliance on distributed storage. MongoDB meets monitoring like never before Our integration allows you to view MongoDB hardware and monitoring metrics all within Prometheus. If you were a user of MongoDB and Prometheus before, this means you no longer have to worry about jumping back and forth between applications to view your data. Our official Prometheus integration provides complete feature parity with Atlas metrics in a secure and supported environment. With a few clicks in the UI, you can configure the integration and set up custom scraping intervals for your Atlas Admin API endpoints to ensure your view in Prometheus is consistently updated based on your preference. Best of all, this integration is free and available for use with MongoDB Atlas (clusters M10 and higher) and Cloud Manager. We truly believe in the freedom to run anywhere, and that includes viewing your data in your preferred monitoring solutions. How the Prometheus Integration works with MongoDB The MongoDB Prometheus integration converts the results of a series of MongoDB commands into Prometheus protocol, allowing Prometheus to scrape the metrics you can view through your MongoDB monitoring charts and more. Once Prometheus successfully collects your metrics, you can parse your metrics in the Prometheus UI or create custom dashboards in Grafana. Get started with the Prometheus Integration If you already have an Atlas account, get started by following the instructions below: Log into your Atlas account. Click the vertical three dot menu next to the project dropdown in the upper lefthand corner of the screen. Select “Integrations.” The Prometheus Monitoring Integration is listed here. Select “Configure” on the Prometheus tile, and follow the guided setup flow. If you don’t have an Atlas account, create an m10 or higher Atlas cluster and follow the instructions above. Note: If you were one of the customers who requested this integration, we thank you! We appreciate your feedback and suggestions, and look forward to implementing more in the future. Input is always welcome at .

March 16, 2022