MongoDB Updates

The newest releases and freshest updates

MongoDB Releases “Focus Mode” in Compass GUI

We’re excited to announce an improvement to the aggregation-building experience in MongoDB Compass. Compass already makes it easy to view and manage your MongoDB databases, and with the addition of Focus Mode you now have the option to dial in on specific stages within your aggregation pipeline. Overview MongoDB's Query API and Aggregation Pipelines enable easy retrieval and processing of data from collections. They also facilitate complex operations such as filtering, grouping, and transforming, making computation and analysis effortless. MongoDB Compass' intuitive interface simplifies the process of building aggregations by enabling developers to easily create, test, and refine aggregation pipelines, and the introduction of Focus Mode takes this a step further. When constructing pipelines, having to simultaneously view and consider multiple stages can make it challenging to analyze the impact of a specific stage, leading to increased cognitive load. Now, developers can toggle Focus Mode on stages, opening a view that focuses exclusively on the contents of the specific stage they are working on. This view can also be used to view sample input (before the aggregation stage is applied) and output (after the stage is applied) documents, aiding in the understanding, troubleshooting, and optimizing of the data pipeline. Developers can also switch between different stages by accessing a drop-down menu at the top of their screen. This makes identifying inefficiencies and optimizing performance easier, as well as providing deeper insights from the output documents for data-driven decision making. Focus Mode offers a streamlined and distraction-free environment for working with stages, improving the efficiency and precision of testing, debugging, and analyzing the impact of each stage on the data, ultimately simplifying the creation and management of pipelines. Conclusion The addition of Focus Mode is part of our continued refresh of the query and aggregation experience in Compass. These improvements are made possible thanks to the feedback of our developer community, so we encourage you to try out this new feature and let us know what you think! To learn more about Aggregation Pipeline Builder in Compass, visit our documentation .

March 21, 2023

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: 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.

March 16, 2023

MongoDB Atlas Integrations for CDKTF are now Generally Available

Infrastructure as Code (IaC) tools allows developers to manage and provision infrastructure resources through code, rather than through manual configuration. IaC have empowered developers to apply similar best practices from software development to application instructure deployments. This includes: Automation - helping to ensure repeatable, consistent, and reliable infrastructure deployments Version Control - check in IaC code into GitHub, BitBucket, or GitLab for improved team collaboration and higher code quality Security - create clear audit trails of each infrastructure modification Disaster Recovery - IaC scripts can be used to quickly recreate infrastructure in the event of availability zone or region outages Cost Savings - prevent overprovisioning and waste of cloud resources Improved Compliance - easier to enforce organizational policies and standards Today we are doubling down on this commitment and announcing MongoDB Atlas integrations with CDKTF (Cloud Development Kit for Terraform). These new integrations are built on top of the Atlas Admin API and allow users to automate infrastructure deployments by making it easy to provision, manage, and control Atlas infrastructure as code in the cloud without first having to create in HCL or YAML configuration scripts. CDKTF abstracts away the low-level details of cloud infrastructure, making it easier for developers to define and manage their infrastructure natively in their programming language of choice. Under the hood, CDKTF is converted into Terraform config files on your behalf. This helps to simplify the deployment process and eliminates context switching. MongoDB Atlas & HashiCorp Terraform: MongoDB began this journey with our partners at HashiCorp when we launched the HashiCorp Terraform MongoDB Atlas Provider in 2019. We then have since grown to 10M+ downloads over all time and our provider is the number one provider in the database category. Today we are delighted to support all CDKTF supported languages including JavaScript, TypeScript, Python, Java , Go, and .NET. In addition, with CDKTF users are free to deploy their MongoDB Atlas resources to AWS, Azure and Google Cloud enabling true multi-cloud deployments. Learn how to get started via this quick demo . Start building today! MongDB Atlas CDKTF integrations are free and open source licensed under Mozilla Public License 2.0 . Users only pay for underlying Atlas resources created and can get started with Atlas always free tier ( M0 clusters ). Getting started today is faster than ever with MongoDB Atlas and CDK for HashiCorp Terraform . We can’t wait to see what you will build next with this powerful combination! Learn more about MongoDB Atlas and CDK for Hashicorp Terraform

February 28, 2023

MongoDB Atlas Integrations for AWS CloudFormation and CDK are now Generally Available

Infrastructure as Code (IaC) tools allows developers to manage and provision infrastructure resources through code, rather than through manual configuration. IaC have empowered developers to apply similar best practices from software development to application instructure deployments. This includes: Automation - helping to ensure repeatable, consistent, and reliable infrastructure deployments Version Control - check in IaC code into GitHub, BitBucket, AWS CodeCommit, or GitLab for improved team collaboration and higher code quality Security - create clear audit trails of each infrastructure modification Disaster Recovery - IaC scripts can be used to quickly recreate infrastructure in the event of availability zone or region outages Cost Savings - prevent overprovisioning and waste of cloud resources Improved Compliance - easier to enforce organizational policies and standards Today we are doubling down on this commitment and announcing MongoDB Atlas integrations with AWS CloudFormation and Cloud Development Kit (CDK). AWS CloudFormation allows customers to define and provision infrastructure resources using JSON or YAML templates. CloudFormation provides a simple way to manage infrastructure as code and automate the deployment of resources. AWS Cloud Development Kit (CDK) is an open-source software development framework that allows customers to define cloud infrastructure in code and provision it through AWS CloudFormation. It supports multiple programming languages and allows customers to use high-level abstractions to define infrastructure resources. These new integrations are built on top of the Atlas Admin API and allow users to automate infrastructure deployments by making it easy to provision, manage, and control Atlas Infrastructure as Code in the cloud. MongoDB Atlas & AWS CloudFormation: To meet developers where they are, we now have multiple ways to get started with MongoDB Atlas using AWS Infrastructure as Code. Each of these allow users to provision, manage, and control Atlas infrastructure as code on AWS: Option 1: AWS CloudFormation Customers can begin their journey using Atlas resources directly from the AWS CloudFormation Public Registry . We currently have 33 Atlas Resources and will continue adding more. Examples of available Atlas resources today include: Dedicated Clusters, Serverless Instances, AWS PrivateLink , Cloud Backups, and Encryption at Rest using Customer Key Management. In addition, we have published these resources to 22 (and counting) AWS Regions where MongoDB Atlas is supported today. Learn how to get started via this quick demo . Option 2: AWS CDK After its launch in 2019 as an open source project, AWS CDK has gained immense popularity among the developer community with over a thousand external contributors and more than 1.3 million weekly downloads. AWS CDK abstracts away the low-level details of cloud infrastructure, making it easier for developers to define and manage their infrastructure natively in their programming language of choice. This helps to simplify the deployment process and eliminates context switching. Under the hood, AWS CDK synthesizes CloudFormation templates on your behalf which is then deployed to AWS accounts. In AWS CDK, L1 (Level 1) and L2 (Level 2) constructs refer to two different levels of abstraction for defining infrastructure resources: L1 constructs are lower-level abstractions that provide a one-to-one mapping to AWS CloudFormation resources. They are essentially AWS CloudFormation resources wrapped in code, making them easier to use in a programming context. L2 constructs are higher-level abstractions that provide a more user-friendly and intuitive way to define AWS infrastructure. They are built on top of L1 constructs and provide a simpler and more declarative API for defining resources. Today we announce MongoDB Atlas availability for AWS CDK in JavaScript and TypeScript, with plans for Python, Java, Go, and .NET support coming later in 2023. Now customers can easily deploy and manage all available Atlas resources by vending AWS CDK applications with prebuilt L1 Constructs. We also have a growing number of L2 and L3 CDK Constructs available. These include Constructs to help users to quickly deploy the core resources they need to get started with MongoDB Atlas on AWS in just a few lines JavaScript or TypeScript (see @mongodbatlas-awscdk/atlas-basic to learn more). Users can also optionally select to add more advanced networking configurations such as VPC peering and AWS PrivateLink. Option 3: AWS Partner Solutions (previously AWS Quick Starts) Instead of manually pulling together multiple Atlas CloudFormation resources, AWS Partner Solutions gives customers access to pre-built CloudFormation templates for both general and specific use cases with MongoDB Atlas. By using AWS Partner Solution templates, customers can save time and effort compared to architecting their deployments from scratch. These were jointly created and incorporate best practices from MongoDB Atlas and AWS. Go to the AWS Partner Solutions Portal to get started. Start building today! These MongDB Atlas integrations with AWS CloudFormation are free and open source licensed under Apache License 2.0 . Users only pay for underlying Atlas resources created and can get started with Atlas always free tier ( M0 clusters ). Getting started today is faster than ever with MongoDB Atlas and AWS CloudFormation. We can’t wait to see what you will build next with this powerful combination! Learn more about MongoDB Atlas integrations with AWS CloudFormation

February 28, 2023

What’s New in Atlas Charts: New Sample Dashboards and Embedding SDK Improvements

Atlas Charts is the best data visualization tool for quickly and easily analyzing all your Atlas data. Today, we’re announcing two big updates: New sample dashboards to give you even more inspiration when getting started Charts Embedding SDK enhancements: dashboard filters and save as PNG Let's start with sample dashboards We took a look at the current experience for new Charts users building their first dashboards. To help reduce the time it takes to experience the power of dashboards, we have always offered a sample dashboard that used a movie database to help explore Charts basics like chart types and understanding how a data source and fields work. While any dataset can be interesting and most of us enjoy watching movies, we knew that most development teams could benefit from some tangible examples of how they might visualize their application data in Charts. So we thought about a few key use cases that nearly every team must consider and we put together four new sample dashboards analyzing: product usage, sales or general business metrics, IoT sensor data, and finally, observability or log data commonly used to understand a platform’s reliability. All of these new dashboards are now available to every Charts user, and each provides a great starting point for exploring your data in Charts. Choosing the new sample IoT dashboard in Atlas Charts. With these new sample dashboards as inspiration, you will be able to quickly see the potential of complex dashboards and customize them to fit your own data and gain valuable business insights. Embedding SDK enhancements: Dashboard filters and save as PNG Next, we are continually listening to customers and trying to make our Embedding SDK more useful for diverse use cases. If you’re unfamiliar, the Embedding SDK lets you take charts and dashboards from Charts, and embed them into the contexts that matter most to your users, with rich customization. Up to this point, dashboard filters have only functioned in dashboards built within the Charts UI in MongoDB Atlas. Dashboard filters can be used to dig a level deeper into your datasets, and with interactive filtering , they are the foundation from which you can add interactivity into embedded dashboards. With this update, users can now seamlessly filter data directly within the embedded dashboard , providing a more interactive experience and enhancing data exploration. Setting up dashboard filters in the UI is simple, and once they’re enabled, you can either selectively choose the fields allowed for filtering or allow all fields present in the data sources used in the dashboard. You can also allow all fields present in a data source if you use chart embedding. Configuring an embedded dashboard with dashboard filters in the IoT sample dashboard. Taking the field ‘calories’ as an example used in the video above, in the SDK, you can easily set a dashboard filter to plot a chart with users who have spent more than 1000 calories: dashboard.setFilter({ calories: { $gt: 1000 } }); You can also use the getFilter method to see what filters have been applied to the embedded dashboard: dashboard.getFilter(); Additionally, you can now programmatically save an image of any chart as a PNG, in either base64 or binary formats using the getImage method in the SDK. We are excited to see how you use the sample dashboards and dashboard filtering in embedded dashboards to achieve your visualization goals using Atlas Charts. New to Atlas Charts? Get started today by logging into or signing up for MongoDB Atlas , deploying or selecting a cluster, and activating Charts for free.

February 27, 2023

New Aggregation Pipeline Text Editor Debuts in MongoDB Compass

There’s a reason why Compass is one of MongoDB’s most-loved developer tools: because it provides an approachable and powerful visual user interface for interacting with data on MongoDB. As part of this, Compass’s Aggregation Pipeline Builder abstracts away the finer points of MongoDB’s Query API syntax and provides a guided experience for developing complex queries. But what about when you want less rather than more abstraction? That’s where our new Aggregation Pipeline Text Editor comes in. Recently released on Compass, the Aggregation Pipeline Text Editor allows users to write free-form aggregations. While users could previously write and edit pipelines through a guided and structured builder organized by aggregation stage, a text-based builder can be preferable for some users. This new pipeline editor makes it easy for users to: See the entire pipeline without having to excessively scroll through the UI Stay “in the flow” when writing aggregations if they are already familiar with MongoDB’s Query API syntax Copy and paste aggregations built elsewhere (like in MongoDB’s VS Code Extension ) into Compass Use built-in syntax formatting to make pipeline text “pretty” before copying it over from Compass to other tools The Aggregation Pipeline Text Editor in Compass. Notice how toward the top right you can click on “stages” to move back to the traditional stage-based Aggregation Pipeline Builder. Ultimately, the addition of the Aggregation Pipeline Text Editor to Compass gives users more flexibility depending on how they want to build aggregations. For a more guided experience and to get result previews when adding each new stage, the existing Aggregation Pipeline Builder will work best for most users. But when writing free-form aggregations or copying and pasting aggregation text from other tools, the Aggregation Pipeline Text Editor may be preferable. It also previews the final pipeline output, rather than the stage-by-stage preview that exists today. Users will be able to access either both the traditional Aggregation Pipeline Builder and the new Pipeline Text Editor from directly within the Aggregations tab in Compass and can switch between the two views without losing their work. To get access to the new Aggregation Pipeline Text Editor, make sure to download the latest version of Compass here . And as always, we welcome your continued feedback on how to improve Compass. If you have ideas for how to improve your experience with Compass you can submit them on our UserVoice platform here . We’ll have even more great features coming in Compass soon. Keep checking back on our blog for the latest news!

January 26, 2023

Introducing MongoDB Connector for Apache Kafka version 1.9

Today, MongoDB released version 1.9 of the MongoDB Connector for Apache Kafka! This article highlights the key features of this new release! Pre/Post document states In MongoDB 6.0, Change Streams added the ability to retrieve the before and after state of an entire document . To enable this functionality on the collection you can set it as a parameter in the createCollection command such as: db.createCollection( "temperatureSensor", { changeStreamPreAndPostImages: { enabled: true } } ) Alternatively, for existing collections, use colMod as shown below: db.runCommand( { collMod: <collection>, changeStreamPreAndPostImages: { enabled: <boolean> } } ) Once the collection is configured for pre and post images, you can set the source connector parameter to include this extra information in the change event. For example, consider this source definition: { "name": "mongo-simple-source", "config": { "connector.class": "com.mongodb.kafka.connect.MongoSourceConnector", "connection.uri": "<< MONGODB CONNECTION STRING >>", "database": "test", "collection": "temperatureSensor", "":"whenavailable" } } When the following document is inserted: db.temperatureSensor.insertOne({'sensor_id':1,'value':100}) Then an update is applied: db.temperatureSensor.updateOne({'sensor_id':1},{ $set: { 'value':105}}) You can see the change stream event written to Kafka topic is as follows: { "_id": { "_data": "82636D39C8000000012B022C0100296E5A100444B0F5E386F04767814F28CB4AAE7FEE46645F69640064636D399B732DBB998FA8D67E0004" }, "operationType": "update", "clusterTime": { "$timestamp": { "t": 1668102600, "i": 1 } }, "wallTime": { "$date": 1668102600716 }, "ns": { "db": "test", "coll": "temperatureSensor" }, "documentKey": { "_id": { "$oid": "636d399b732dbb998fa8d67e" } }, "updateDescription": { "updatedFields": { "value": 105 }, "removedFields": [], "truncatedArrays": [] }, "fullDocumentBeforeChange": { "_id": { "$oid": "636d399b732dbb998fa8d67e" }, "sensor_id": 1, "value": 100 } } Note the fullDocumentBeforeChange key includes the original document before the update occurred. Starting the connector at a specific time Prior to version 1.9, when the connector starts as a source, it will open a MongoDB change stream and any new data will get processed by the source connector. To copy all the existing data in the collection first before you begin processing the new data, you specify the “ copy.existing ” property. One frequent user request is to start the connector based upon a specific timestamp versus when the connector starts. In 1.9 a new parameter called startup.mode was added to specify when to start writing data. startup.mode=latest (default) “Latest” is the default behavior and starts processing the data when the connector starts. It ignores any existing data when the connector starts. startup.mode=timestamp “timestamp” allows you to start processing at a specific point in time as defined by additional startup.mode.timestamp.* properties. For example, to start the connector from 7AM on November 21, 2022, you set the value as follows:’2022-11-21T07:00:00Z’ Supported values are an ISO-8601 format string date as shown above or as a BSON extended string format. startup.mode=copy.existing Same behavior as the existing as the configuration option, “copy.existing=true”. Note that “copy.existing” as a separate parameter is now deprecated. If you defined any granular copy.existing parameters such as copy.existing.pipeline, just prepend them with “startup.mode.copy.existing.” property name. Reporting MongoDB errors to the DLQ Kafka supports writing errors to a dead letter queue . In version 1.5 of the connector, you could write all exceptions to the DLQ through the mongo.error.tolerance=’all’ . One thing to note was that these errors were Kafka generated errors versus errors that occurred within MongoDB. Thus, if the sink connector failed to write to MongoDB due to a duplicate _id error, for example, this error wouldn’t be written to the DLQ. In 1.9, errors generated within MongoDB will be reported to the DLQ. Behavior change on inferring schema Prior to version 1.9 of the connector, if you are inferring schema and insert a MongoDB document that contains arrays with different value data types, the connector is naive and would simply set the type for the whole array to be a string. For example, consider a document that resembles: { "myfoo": [ { "key1": 1 }, { "key1": 1, "key2": "dogs" } ] } If we set output.schema.infer.value . to true on a source connector, the message in the Kafka Topic will resemble the following: … "fullDocument": { … "myfoo": [ "{\"key1\": 1}", "{\"key1\": 1, \"key2\": \"dogs\"}" ] }, … Notice the array items contain different values. In this example, key1 is a subdocument with a single value the number 1, the next item in the “myfoo” array is a subdocument with the same “key1” field and value of an integer, 1, and another field, “key 2” that has a string as a value. When this scenario occurs the connector will wrap the entire array as a string. This behavior can also apply when using different keys that contain different data type values. In version 1.9, the connector when presented with this configuration will not wrap the arrays, rather it will create the appropriate schemas for the variable arrays with different data type values. The same document when run in 1.9 will resemble: "fullDocument": { … "myfoo": [ { "key1": 1, }, { "key1": 1, "key2": "DOGS" } ] }, Note that this behavior is a breaking change and that inferring schemas when using arrays can cause performance degradation for very large arrays using different data type values. Download the latest version of the MongoDB Connector for Apache Kafka from Confluent Hub! To learn more about the connector read the MongoDB Online Documentation . Questions? Ask on the MongoDB Developer Community Connectors and Integrations forum!

January 12, 2023

Introducing MongoDB Spark Connector Version 10.1

Today, MongoDB released version 10.1 of the MongoDB Spark Connector. In this post, we highlight key features of this new release. Microbatch streaming support The MongoDB Spark connection version 10 introduced support for Apache Structured Spark Streaming. In this initial release, continuous mode streaming was the only mode supported. In this 10.1 update, microbatch mode is now supported, enabling you to stream writes to destinations that currently do not support continuous mode streams, such as Amazon S3 storage. Increased control of write behavior When the Spark Connector issues a write, the default behavior is for an upsert to occur. This can cause problems in some scenarios in which you may not want an upsert, such as with time series collections. There is a new configuration parameter, upsertDocument , that, when set to false, will only issue insert statements on write. solar.write.format("mongodb").mode("append").option("database", "sensors").option("collection", "panels").option("upsertDocument", "false").save() In the above code snippet we are writing to the "panels" time series collection by setting the upsertDocument to false. Alternatively, you can set operationType to the value, “insert”. Setting this option will ignore any upsertDocument option set. Support for BSON types The data types supported in BSON are not exactly the same as those supported in a Spark dataframe. For example, Spark doesn't support ObjectId as a type specifically. To mitigate these scenarios where you need to leverage different BSON types, you can now set the new configuration values :<true/false> spark.mongodb.write.convertJson=<true/false> This will enable you to effectively leverage BSON datatypes within your Spark application. Call to action Version 10.1 of the MongoDB Spark Connector continues to enhance the streaming capabilities with support for microbatch processing. This version also adds more granular support for writing to MongoDB supporting use cases like time series collections. For those users wanting to upgrade from the 3.x version but could not because of lack of BSON data type support, the 10.1 version now provides an option for using BSON data types. To learn more about the MongoDB Spark Connector check out the online documentation . You can download the latest version of the MongoDB Spark Connector from the maven repository .

January 4, 2023

What’s New in Atlas Charts: Easy Organization-Wide Sharing

We’re excited to announce improvements to sharing dashboards in MongoDB Atlas Charts . Data visualization is a powerful tool for discovering insights, and sharing visualizations across your team helps amplify those insights to propel businesses forward. With organization-wide sharing in Atlas Charts, we’re making it even easier to share the insights you discover from your application data across your entire organization. Sharing dashboards Atlas Charts has always made it possible to share visualizations with either individual members or everyone inside your Atlas project. Assuming a user had access to a given data source in Atlas, adding a user to a Charts project was effectively a one-click process. However, many teams do not broadly share database access unless an individual specifically needs it. And, if you want to share data with many members of your team, provisioning users one by one is tedious. Once users are in a Charts project, however, sharing a dashboard with everyone inside the project becomes relatively easy — you can invite all users in your project to view your dashboard with a single action. There are probably scenarios in which some members of your organization have Atlas access and others do not. In this case, if your team has enabled Federated Authentication and uses a third-party authentication provider, such as Google or Okta, Charts now makes it simple to turn on sharing dashboards across your entire organization. Granting access This approach makes sharing company-wide information quick and easy. For example, you can keep employees aware of product or platform growth or other key business metrics. Any members of your organization can be granted access to view these dashboards with a single click, as shown in Figure 1. Figure 1: &nbsp; A look at a dashboard shared across an organization. Note that, with these changes to dashboard sharing, your ability to maintain the security of your data remains unchanged. New dashboard viewers still need at least viewer access to any data source behind the charts in a shared dashboard, thereby ensuring that your company's sensitive data remains private. Additionally, project owners can now manage data source access at a deployment level, which means they can give access to their clusters or federated database instances . This capability is in addition to the already available granular control of data source access at a collection level, which was introduced as part of recent improvements we made to data sources. You can read more about managing access to data sources in your organization in our documentation . We hope you find these sharing improvements valuable and start leveraging this capability to share additional insights across your organization. New to Atlas Charts? Get started today by logging into or signing up for MongoDB Atlas , deploying or selecting a cluster, and activating Charts for free.

December 5, 2022

Atlas Charts Adds Support for Serverless and Online Archive Data Sources

We recently introduced streamlined data sources in Atlas Charts, which eliminates the manual steps involved with adding data sources into Charts. With MongoDB Atlas project data automatically available in Charts, your visualization workflow can become quicker and simpler than ever. With this feature, Atlas Charts users can now visualize two new sources of data: Serverless instances and Atlas cluster data that’s been archived using MongoDB Atlas Online Archive . For those unfamiliar with these data sources, here’s a quick summary: A serverless instance is an Atlas deployment model that lets you seamlessly scale usage based on workload demand and ensures you are only charged for resources you need. Online Archive enables automated data tiering of Atlas data, helping you scale your storage and optimize costs while keeping data accessible. Use cases These data sources serve two distinct use cases, based on your needs. So, whether you are trying to eliminate upfront resource provisioning using a serverless instance or creating archives of your high-volume workloads, such as time-series or log data to reduce costs with Online Archive, Charts makes these sources natively available for visualization with zero ETL, just as it always has with your other Atlas clusters. To learn how easy it is to visualize these new data sources, let’s create a serverless database called “ServerlessInstance0” and separately activate Online Archive on a database called “Cluster0” that will run daily in Atlas (Figure 1). Figure 1: Screenshot showing a serverless database deployed in MongDB Atlas. When setting up an Online Archive, Atlas creates two instances of your data (Figure 2). One instance includes only your archived data. The second instance contains your archive data and your live cluster data. This setup gives you additional flexibility to query data as your use case demands. Figure 2: Screenshot showing Online Archive instances in Atlas. Moving on to the Data Sources page in Charts (Figure 3), all of the data sources are shown, including serverless instances and Atlas cluster data archived in Online Archive, neatly categorized based on the instance type and ready for use in charts and dashboards. (Note that project owners maintain full control of these data sources.) For more details about connecting and disconnecting data sources, review our documentation . Figure 3: Screenshot showing Serverless and Online Archive data sources in Atlas Charts. With these additions, Charts now supports all the cluster configurations you can create in Atlas, and we are excited to see how you achieve your visualization goals using these new data sources. New to Atlas Charts? Get started today by logging into or signing up for MongoDB Atlas , deploying or selecting a cluster, and activating Charts for free.

October 27, 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

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

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