Some of these performance optimizations were gained by changes made to the architecture that allows for reduced memory usage and query times. It also can now perform more efficient batch processing for inserts, updates, and deletes.

This release also features faster and more cost-efficient horizontal scaling, which allows data to be split — or “sharded” — across multiple servers (shards). In this release, data can be distributed across shards 50x faster for up to 50% lower cost compared to MongoDB 7.0.

Another notable update is that users now have more control over optimizing performance during unpredictable usage spikes and periods of sustained high demand. They can now set a default maximum time limit for running queries, reject recurring types of queries that have caused issues, and set query settings that will persist through events like database restarts.

MongoDB 8.0 also offers better support for search and AI applications through quantized vectors, which are “compressed representations of full-fidelity vectors.” According to the company, these require very little memory and are very fast to retrieve—all while preserving accuracy. 

And finally, it is introducing updates to MongoDB Queryable Encryption, which is a capability that allows users to encrypt sensitive data, store it in their database, and run queries on the encrypted data. Now users can perform range queries, which further reduces the risk of data exposure and exfiltration by keeping it encrypted throughout its full lifecycle. 

According to MongoDB, it has already moved its internal build system over to MongoDB 8.0 and its development team has seen a 75% query latency reduction since switching over. “This was a double win, as it improved the performance of our own tooling, and it set our performance chat room abuzz with excitement in anticipation of delighting external customers. While results may vary based on your particular workload, the point is that we just couldn’t wait to share MongoDB 8.0’s performance gains with customers,” Jim Scharf, CTO of MongoDB wrote in a blog post. 

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According to MongoDB, many customers have reported that they lack the multi-modal data structures to power AI applications. They also lack the skills to confidently integrate components from multiple vendors and are worried about the risk of doing these integrations wrong. MAAP aims to provide customers the expertise they need and remove the risk of integration. 

“Thousands of customers already rely on MongoDB to power their mission-critical apps, and we have years of experience helping customers unlock the power of data,” MongoDB wrote in a blog post. “The ultimate aim of MAAP is to enable customers to get the most out of their data, and to ensure that they can confidently innovate with AI.”

MAAP users can utilize the LLM of their choice and it can run anywhere, including all major cloud providers, on premises, and at the edge. 

MAAP’s ecosystem includes Accenture, Anthropic, Anyscale, Arcee AI, AWS, Cohere, Credal, Fireworks AI, Google Cloud, gravity9, LangChain, LlamaIndex, Microsoft Azure, Nomic, PeerIslands, Pureinsights, and Together AI. The company plans to continue adding more providers over time. 

“MAAP is designed to set customers up for AI success today and tomorrow: the program will be continuously enhanced with the latest technological advancements and industry best practices, to ensure that customers stay ahead of this rapidly evolving space,” the company wrote. 

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“Customers tell us they love MongoDB Atlas because it provides an integrated set of capabilities on one platform that can store and process their organization’s operational data across all of their applications,” said Sahir Azam, chief product officer at MongoDB. “Customers also tell us that MongoDB’s highly flexible and scalable document data model is a perfect fit for powering modern applications that can take advantage of generative AI and their real-time proprietary data. The additional services we’re launching today for MongoDB Atlas not only make it easier to build, deploy, and run modern applications, but also make it easier to optimize performance while reducing costs.”

First, it announced the general availability of MongoDB Atlas Stream Processing, which allows for analysis of streaming data, or data in motion, coming from IoT devices or inventory feeds. Streaming data can be used to create dynamic experiences in applications, but requires a new data model. With MongoDB Atlas Stream Processing, developers will be able to create applications that can change their behavior based on this dynamic data. 

Examples of applications that can be built using this include an app that optimizes shipping routes using current weather conditions and supply chain data feeds, or fraud detection that utilizes transaction histories in real time. 

Next, MongoDB Atlas Search Nodes provide dedicated infrastructure for generative AI workloads on MongoDB Atlas Vector Search and MongoDB Atlas Search. It consists of nodes that are separate from the core database nodes, allowing customers to isolate AI workloads, optimize costs, and reduce query times. 

According to MongoDB, one of the other benefits is that it enables high availability for AI-based search workloads. For instance, an airline could use it to offer an agent that helps with booking flights that can adjust to surges in demand by isolating the vector search workload and scaling the needed infrastructure without also scaling the resources for the database workload. 

MongoDB Atlas Search Nodes are available on AWS and Google Cloud and in preview on Microsoft Azure.

The company also announced a preview of MongoDB Atlas Edge Server, which allows developers to deploy and operate distributed applications in the cloud and the edge. It provides applications with access to data even on intermittent connections. It also supports data tiering, which prioritizes critical data to be synchronized first and helps reduce network congestion, MongoDB explained. 

And finally, the company announced the availability of MongoDB Atlas Vector Search on Knowledge Bases for Amazon Bedrock. This allows Amazon Bedrock applications to act on data processed by MongoDB Atlas Vector Search. 

“With the integration of MongoDB Atlas Vector Search and Amazon Bedrock now generally available, we’re making it easier for joint MongoDB-AWS customers to use a variety of foundation models hosted in their AWS environments to build generative AI applications that can securely use their proprietary data within MongoDB Atlas to improve accuracy and provide enhanced end-user experiences,” said Azam.

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In the transition from private to public preview, Atlas Stream Processing has focused on enhancing the developer experience to position itself as a go-to solution for development teams. A significant part of this enhancement includes the integration of Atlas Stream Processing with Visual Studio Code. The MongoDB VS Code plugin now supports connections to Stream Processing instances, enabling developers to create and manage processors within a familiar environment. This integration aims to streamline the development process by reducing the need to switch between different tools, thereby allowing developers to devote more time to building applications.

Another notable improvement in the public preview of Atlas Stream Processing is the advancement of its dead letter queue (DLQ) capabilities. DLQ enables effective stream processing, and the latest updates have made it even more powerful. Now, DLQ messages are more accessible and can be displayed directly during the execution of pipelines with sp.process() and when using .sample() on running processors. This enhancement eliminates the previous requirement for a separate target collection to serve as a DLQ, simplifying the development process and making it more efficient.

Atlas Stream Processing has enhanced its capabilities by adding features that bridge the gap between traditional database operations and real-time stream processing. The introduction of windowing functions and the integration for merging and emitting data to an Atlas database or a Kafka topic mark significant advancements. The public preview introduces the $lookup operator, allowing developers to enrich stream-processed documents with data from remote Atlas clusters by performing joins. 

This enhancement, alongside the improved change streams feature which now supports pre- and post-imaging, empowers developers to handle complex data processing tasks such as calculating deltas between document fields and accessing full contents of deleted documents, thereby enabling more sophisticated customer experiences.

Atlas Stream Processing now supports conditional routing with dynamic expressions in the merge and emit stages, facilitating more nuanced data routing strategies based on document field values. This feature allows for dynamic forking of messages to different Atlas collections or Kafka topics, leveraging the Query API’s flexibility for diverse use cases. Additionally, the introduction of idle stream timeouts addresses the challenge of managing streams with inconsistent data flows by allowing streams to close automatically after a specified period of inactivity. These enhancements collectively aim to provide developers with more robust tools for real-time data processing, catering to the needs of advanced teams and enabling the delivery of richer, more responsive customer experiences.

“Public preview is a huge step forward for us as we expand the developer data platform and enable more teams with a stream processing solution that simplifies the operational complexity of building reactive, responsive, event-driven applications, while also offering an improved developer experience,” Clark Gates-George and Joe Niemiec from the MongoDB team wrote in a blog post. 

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MongoDB Atlas Vector Search is now generally available, which allows customers to build generative AI into their applications based on their own data. This enables the AI to provide accurate, relevant responses for a specific organization or domain. 

Customers can build AI features, such as semantic search or image comparison, into their applications. It uses a “flexible and scalable” document-based data model, which, according to the company, enables customers to combine queries for vector data, analytical aggregations, text-based search, geospatial data, and time series data.

As an example, a user could request “Find real estate listings with houses that look like this image, were built in the last five years, and are in an area within seven miles north of downtown Seattle with top-rated schools and walking distance to parks.”  The application can then provide a response that is based on queries from multiple data sources. 

In addition to this new tool, the company also announced the release of MongoDB Atlas Search Nodes, which provide dedicated infrastructure for managing generative AI search workloads for MongoDB Atlas Vector Search and MongoDB Atlas Search. 

This infrastructure runs separately from the operational nodes of the database, which allows for workload isolation, cost optimization, and better performance, MongoDB explained. 

For example, it could be used by a retailer running a holiday promotion to isolate and scale workloads for chatbots in a specific location. 

According to MongoDB, this new service can reduce query times by up to 60%. 

“With the general availability of MongoDB Atlas Vector Search and MongoDB Atlas Search Nodes, we’re making it even easier for customers to use a unified, fully managed developer data platform to seamlessly build, deploy, and scale modern applications and provide end users with the types of personalized, AI-powered experiences that save them time and keep them engaged,” said Sahir Azam, chief product officer at MongoDB.

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According to MongoDB, developers’ time is an organization’s “most precious commodity.” Therefore, the company would like to improve the experience around the most common tasks a developer does every day. It narrowed this down to two areas that could be improved and is releasing new capabilities around those.

The first area is making the Atlas database easier to work with. It is now possible for developers to use the Atlas CLI to manage their development environments locally using the same experience they have in the cloud. Atlas CLI has also been updated to include Atlas Search and Atlas Vector Search, which enables developers to create and manage search indexes within development workflows. 

“By giving developers the power of Atlas at their fingertips no matter their preferred development environment, MongoDB continues to expand the scope and capabilities of its developer data platform while placing a premium on developer experience,” MongoDB wrote in a blog post. 

The second area is making it easier to write MongoDB queries. Developers can now use plain English to ask questions, and the MongoDB GUI, Compass, will automatically generate a query for the request. The company also has a private preview for SQL query conversion in Relational Migrator, which enables queries and procedures to be converted to the MongoDB query language. 

The company also is making MongoDB accessible from more places. It announced Atlas for the Edge, which brings data processing and storage closer to where the data lives. Atlas Edge Servers can be deployed anywhere, enabling developers to create customer experiences that need low latency, heavy computation close to where data is generated, or run applications in locations that have intermittent internet. 

“With MongoDB Atlas for the Edge, organizations can now use a single, unified interface to deliver a consistent and frictionless development experience from the edge to the cloud — and everything in between. Together, the capabilities included with MongoDB Atlas for the Edge allow organizations to significantly reduce the complexity of building edge applications and architectures,” MongoDB wrote in a blog post. 

MongoDB has also announced its own publishing company, MongoDB Press, which will make it easier to publish knowledge about MongoDB. Currently two books have been published: one is on aggregations and the other on MongoDB 7.0. 

The company has also published a new solutions library that features use cases from across the industry to give developers inspiration on how they can use MongoDB. And finally, it has also added new content to MongoDB University.

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MongoDB Atlas Vector Search now uses generative AI to create highly relevant data retrieval and personalization into applications for improved customer experiences, the company said in its announcement. 

The company also said it enhanced MongoDB Search Nodes to provide a dedicated  infrastructure for search use cases, enabling customers to scale the infrastructure independent of the database. This helps them more flexibly and efficiently manage unforeseen spikes and high-throughput workloads.

MongoDB Atlas Stream Processing helps developers build event-driven applications by unifying how they handle data in motion and at rest; MongoDB Time Series collections now have significant scaling and efficiency improvements; and MongoDB Atlas Data Federation and Online Archive have new features for querying data and isolating workloads on Microsoft Azure as well as AWS, the company announced.

Dev Ittycheria, president and CEO at MongoDB, said, “With the new features we’re launching today, we’re further supporting not only customers who are just getting started, but also customers who have the most demanding requirements for functionality, performance, scale, and flexibility so they can unleash the power of software and data to build advanced applications to transform their businesses.”

To learn more about MongoDB Atlas, visit mongodb.com/atlas.

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These announcements are intended to empower developers to innovate faster by addressing more use cases, servicing more of the data lifecycle, optimizing for modern architectures, and implementing sophisticated levels of data encryption.

“Hundreds of millions of new applications will be developed over the coming years that deliver compelling customer experiences, enable new capabilities to transform businesses, and increase operational efficiency via more sophisticated automation – and these applications all require a highly scalable, cloud-native, globally distributed data platform,” said Dev Ittycheria, president and CEO of MongoDB. “Our vision is to offer a developer data platform that provides a modern and elegant developer experience, enables broad support for a wide variety of use cases, and delivers the performance and scale needed to address the most demanding requirements.”

MongoDB time series collections make it faster, simpler, and cheaper to build apps to monitor physical systems, track assets, or deal with financial data. 

This feature will be available in the upcoming MongoDB 6.0 release and it will support secondary indexes on measurements, and feature read performance improvements and optimizations for sorting time-based data quicker. 

It was also announced at the conference that Atlas Data Lake will feature fully managed support capabilities that provide the economics of cloud object storage while optimizing for high performance analytical queries. 

Additionally, MongoDB announced an integration with Vercel that will allow teams using Vercel’s platform to develop, preview, and ship websites and applications to get started using MongoDB Atlas as their backend database more easily. 

The company also unveiled that Queryable Encryption is available in preview with MongoDB 6.0. This feature introduces encrypted search scheme utilizing cryptography engineering and offers developers the ability to query encrypted sensitive data in a simple way with no impact on performance and zero cryptography experience required.

For a full list of new features and enhancements announced at MongoDB World 2022, visit the website.

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With database connections and SQL cells, users can now connect to a wide range of databases from the interface, browse database schema, and then query their data using native SQL cells. In addition, with interactive reports users can easily turn notebooks into interactive data stories in one click, as well as hide cells from viewers and share interactive reports with stakeholders.

For more information on the release, or to schedule a demo, visit here. 

MongoDB announces pay-as-you-go pricing model

MongoDB, a modern, general purpose database platform, today announced the launch of pay-as-you-go MongoDB Atlas with a free trial in AWS Marketplace. This will make it easier for users to build, scale, and manage data-rich applications with MongoDB Atlas in AWS Marketplace in calendar Q1 2022. 

With this, developers gain access to a simplified subscription experience and enterprises are offered another way to procure MongoDB in addition to privately negotiated offers already supported on AWS Marketplace.

In addition, there will be no up-front commitments required in order to use MongoDB Atlas on AWS and users will only be expected to pay for the resources they actually use and scale based on their needs. MongoDB is also launching the capability for customers to purchase MongoDB Professional Services in AWS Marketplace at AWS re:Invent.

Tenable adds new features to Terrascan 

Tenable, a cyber exposure company, today announced new features being added to Terrascan, the open source cloud native security analyzer. These new features allow organizations to embed security into their DevOps tooling, pipelines, and supply chains.

Among Terrascan’s new capabilities are 

• The ability to identify risks in more laC and container definition formats 
• Integration with all major container registries
• More flexible developer workflows, including the pragmatic enforcement of security policies before changes are committed into the code repository and before they are applied to the runtime environment 
• Improved ability filter and prioritize findings based on user needs 
• Deeper integration with external dashboards and reporting frameworks 
• A new graphical user interface to simplify creation and testing of new policies 

Clearlake Capital Group acquires Quest Software 

Quest Software, a global cyber security, data intelligence, and IT operations management software provider, announced that it has signed a definitive agreement with Clearlake Capital Group, L.P. to acquire the company from Francisco Partners. 

Patrick Nichols, the current CEO of Quest Software, will continue to lead the organization supported by the existing executive management team. After closing the transaction, Clearlake will become the majority shareholder in Quest Software. The financial terms of the transaction were not disclosed.

“We have long admired Quest as a leading identity-centric cybersecurity, data intelligence, and IT operations management software platform and the Company’s software solutions that help secure enterprise IT environments,” said Behdad Eghbali, co-founder and managing partner at Clearlake. “We are excited to partner with Patrick and Carolyn McCarthy, Quest’s CFO, to utilize Clearlake’s O.P.S. framework to help the Company strengthen its strategic growth plans.”

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Users can now color file tabs by project, a highly requested item on the Developer Community forum. In addition, color tabs can function as a useful organizational tool for users who have learning differences or problems focusing. 

In addition, now users can also bold current active tabs, change the tab width, and add an extra close button at the top of the document. Some other ways users can personalize Visual Studio include installing custom themes, converting VS Code themes to work in Visual Studio 2022, or synching VS themes to Windows themes.

Sparx Systems announces Enterprise Architect 16.0 beta

Sparx Systems announced that Enterprise Architect 16.0, a precise and intuitive modeling and design platform that integrates exploration and invention, is now in beta. The features of this beta release focus on modeling, collaboration, and governance; enhanced technologies; and tools, framework, and user experience.

The new features provided in version 16 offer users a richer and cleaner experience. Among these features are: 

• The ability to select and apply a template to initial document creation
• More details on setting sections
• Improved content list, now contains element and template being used for each dynamic section
• Progress bar to provide better feedback during document generation
• Warning are now displayed in cases where data would be lost upon updating a dynamic section
• Custom document elements can now be created via Diagram Toolbox
• Enhancements and visibility updates
• And expanded and easily reached help information 

For more information, visit here. 

Amazon RStudio on Amazon SageMaker now available

Amazon announced the general availability of RStudio on Amazon SageMaker, a fully managed RStudio Workbench IDE in the cloud. 

Now, administrators can have an easy experience to migrate their RStudio environments to integrate into Amazon SageMaker as well as bring existing RStudio licenses to manage through AWS License Manager. In addition, users can now onboard R and Python developers to the same Amazon SageMaker domain using AWS Single Sign-On or AWS Identity and Access Management.

MongoDB certified for ISO and CSA compliance 

MongoDB today announced its certification for compliance with ISO 27017:2015 and ISO 27018:2019. These compliance certifications come in addition to the company’s existing ISO 27001:2013 certification as well as the completion of Cloud Security Alliance (CSA) Security Trust and Risk (STAR) Level 2 certification.

These certifications came following MongoDB Atlas for Government achieving the FedRAMP designation of “In-Process” reflecting the company’s progress toward a FedRAMP Authorized modern data platform for the U.S. Government. 

For more information on these certifications, visit here. 

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