Month: September 2023

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MongoDB Atlas for the Edge enables organizations to build, deploy, and manage highly reliable, data-driven applications anywhere—across devices, on-premises data centers, and the cloud

AWS and Cloneable among partners and customers working with MongoDB Atlas for the Edge

LONDON, Sept. 26, 2023 /PRNewswire/ — MongoDB, Inc. (NASDAQ: MDB) today at MongoDB.local London announced MongoDB Atlas for the Edge, a set of capabilities that make it easier for organizations to deploy applications closer to where real-time data is generated, processed, and stored—across devices, on-premises data centers, and major cloud providers. With MongoDB Atlas for the Edge, data is securely stored and synchronized in real time across data sources and destinations to provide highly available, resilient, and reliable applications. Organizations can now use MongoDB Atlas for the Edge to build, deploy, and manage applications that are accessible virtually anywhere for use cases like connected vehicles, smart factories, and supply chain optimization—without the complexity typically associated with operating distributed applications at the edge. To get started with MongoDB Atlas for the Edge, visit https://www.mongodb.com/use-cases/atlas-for-the-edge.

“Flexibility and abstracting away complexity is one of the key attributes of a development experience that our customers have come to expect from us,” said Sahir Azam, Chief Product Officer at MongoDB. “Atlas for the Edge delivers a consistent development experience across the data layer for applications running anywhere—from mobile devices, kiosks in retail locations, remote manufacturing facilities, and on-premises data centers all the way to the cloud. Now, customers can more easily build and manage distributed applications securely using data at the edge with high availability, resilience, and reliability—and without the complexity and heavy lifting of managing complex edge deployments.” 

Advancements in edge computing offer significant opportunities for organizations to deploy distributed applications to reach end users anywhere with real-time experiences. However, many organizations today that want to take advantage of edge computing lack the technical expertise to manage the complexity of networking and high volumes of distributed data required to deliver reliable applications that run anywhere. Many edge deployments involve stitching together hardware and software solutions from multiple vendors, resulting in complex and fragile systems that are often built using legacy technology that is limited by one-way data movement and requires specialized skills to manage and operate. Further, edge devices may require constant optimization due to their constraints—like limited data storage and intermittent network access—which makes keeping operational data in sync between edge locations and the cloud difficult. Edge deployments can also be prone to security vulnerabilities, and data stored and shared across edge locations must be encrypted in transit and at rest with centralized access management controls to ensure data privacy and compliance. As a result of this complexity, many organizations struggle to deploy and run distributed applications that can reach end users with real-time experiences wherever they are.

MongoDB Atlas for the Edge eliminates this complexity, providing capabilities to build, manage, and deploy distributed applications that can securely use real-time data in the cloud and at the edge with high availability, resilience, and reliability. Tens of thousands of customers and millions of developers today rely on MongoDB Atlas to run business-critical applications for real-time inventory management, predictive maintenance, and high-volume financial transactions. 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—with the ability to build distributed applications that can process, analyze, and synchronize virtually any type of data across locations. Together, the capabilities included with MongoDB Atlas for the Edge allow organizations to significantly reduce the complexity of building, deploying, and managing the distributed data systems that are required to run modern applications anywhere:

• Deploy MongoDB on a variety of edge infrastructure for high reliability with ultra-low latency: With MongoDB Atlas for the Edge, organizations can run applications on MongoDB using a wide variety of infrastructure, including self-managed on-premises servers, such as those in remote warehouses or hospitals, in addition to edge infrastructure managed by major cloud providers including Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. For example, data stored in MongoDB Enterprise Advanced on self-managed servers can be automatically synced with MongoDB Atlas Edge Server on AWS Local Zones and MongoDB Atlas in the cloud to deliver real-time application experiences to edge devices with high reliability and single-digit millisecond latency. MongoDB Atlas for the Edge allows organizations to deploy applications anywhere, even in remote, traditionally disconnected locations—and keep data synchronized between edge devices, edge infrastructure, and the cloud—to enable data-rich, fault-tolerant, real-time application experiences.
• Run applications in locations with intermittent network connectivity: With MongoDB Atlas Edge Server and Atlas Device Sync, organizations can use a pre-built, local-first data synchronization layer for applications running on kiosks or on mobile and IoT devices to prevent data loss and improve offline application experiences. MongoDB Atlas Edge Servers can be deployed in remote locations to allow devices to sync directly with each other—without the need for connectivity to the cloud—using built-in network management capabilities. Once network connectivity is available, data is automatically synchronized between devices and the cloud to ensure applications are up to date for use cases like inventory and package tracking across supply chains, optimizing delivery routes in remote locations, and accessing electronic health records with intermittent network connectivity.
• Build and deploy AI-powered applications at the edge: MongoDB Atlas for the Edge provides integrations with generative AI and machine learning technologies to provide low-latency, intelligent functionality at the edge directly on devices—even when network connectivity is unavailable. For example, MongoDB Atlas Search and Atlas Vector Search make it faster and easier to build intelligent applications with search and generative AI capabilities that take advantage of vector embeddings (numeric representations of data such as text, images, and audio) and large language models. Once embeddings are generated and stored in MongoDB Atlas, edge applications running on the Atlas Device SDK (formerly Realm)—a fast, scalable platform with mobile-to-cloud data synchronization that makes building real-time, reactive mobile applications easy—can use embeddings stored locally for use cases like real-time image similarity search and classification to identify potential product defects on factory lines. Developers can also use the Atlas Device SDK to build, train, deploy, and manage machine learning models on edge devices using popular frameworks like CoreML, TensorFlow, and PyTorch for customized applications that take advantage of real-time data.
• Store and process real-time and batch data from IoT devices to make it actionable: With MongoDB Atlas Stream Processing, organizations can ingest and process high-velocity, high-volume data from millions of IoT devices (e.g., equipment sensors, factory machinery, medical devices) in real-time streams or in batches when network connectivity is available. Data can then be easily aggregated, stored, and analyzed using MongoDB Time Series collections for use cases like predictive maintenance and anomaly detection with real-time reporting and alerting capabilities. MongoDB Atlas for the Edge provides all of the tools necessary to process and synchronize virtually any type of data across edge locations and the cloud to ensure consistency and availability.
• Easily secure edge applications for data privacy and compliance: MongoDB Atlas for the Edge helps organizations ensure their edge deployments are secure with built-in security capabilities. The Atlas Device SDK provides out-of-the-box data encryption at rest, on devices, and in transit over networks to ensure data is protected and secure. Additionally, Atlas Device Sync provides fine-grained role-based access, with built-in identity and access management (IAM) capabilities that can also be combined with third-party IAM services to easily integrate edge deployments with existing security and compliance solutions.

“High reliability and ultra-low latency are key requirements that impact customers’ ability to access and process their data. This is where AWS’s edge services help meet customers’ data-intensive workload needs,” said Amir Rao, Director of Product Management for Telco at AWS. “With MongoDB Atlas for the Edge, customers can take advantage of managed edge infrastructure like AWS Local Zones, AWS Wavelength, and AWS Outposts to process data closer to end users and power applications across generative AI and machine learning, IoT, and robotics—making it easier for them to build, manage, and deploy their applications anywhere.”

Cloneable provides low/no-code tools to enable instant deployment of AI applications to a spectrum of devices—mobile, IoT devices, robots, and beyond. “We collaborated with MongoDB because Atlas for the Edge provided capabilities that allowed us to move faster while providing enterprise-grade experiences,” said Tyler Collins, CTO at Cloneable. “For example, the local data persistence and built-in cloud synchronization provided by Atlas Device Sync enables real-time updates and high reliability, which is key for Cloneable clients bringing complex, deep tech capabilities to the edge. Machine learning models distributed down to devices can provide low-latency inference, computer vision, and augmented reality. Atlas Vector Search enables vector embeddings from images and data collected from various devices to allow for improved search and analyses. MongoDB supports our ability to streamline and simplify heavy data processes for the enterprise.”

About MongoDB Atlas

MongoDB Atlas is the leading multi-cloud developer data platform that accelerates and simplifies building applications with data. MongoDB Atlas provides an integrated set of data and application services in a unified environment that enables development teams to quickly build with the performance and scale modern applications require. Tens of thousands of customers and millions of developers worldwide rely on MongoDB Atlas every day to power their business-critical applications. To get started with MongoDB Atlas, visit mongodb.com/atlas.

About MongoDB

Headquartered in New York, MongoDB’s mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. Built by developers, for developers, our developer data platform is a database with an integrated set of related services that allow development teams to address the growing requirements for today’s wide variety of modern applications, all in a unified and consistent user experience. MongoDB has tens of thousands of customers in over 100 countries. The MongoDB database platform has been downloaded hundreds of millions of times since 2007, and there have been millions of builders trained through MongoDB University courses. To learn more, visit mongodb.com.

Forward-looking Statements

This press release includes certain “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended, including statements concerning MongoDB’s technology and offerings. These forward-looking statements include, but are not limited to, plans, objectives, expectations and intentions and other statements contained in this press release that are not historical facts and statements identified by words such as “anticipate,” “believe,” “continue,” “could,” “estimate,” “expect,” “intend,” “may,” “plan,” “project,” “will,” “would” or the negative or plural of these words or similar expressions or variations. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations, strategies and prospects as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations or strategies will be attained or achieved. Furthermore, actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control including, without limitation: the impact the COVID-19 pandemic may have on our business and on our customers and our potential customers; the effects of the ongoing military conflict between Russia and Ukraine on our business and future operating results; economic downturns and/or the effects of rising interest rates, inflation and volatility in the global economy and financial markets on our business and future operating results; our potential failure to meet publicly announced guidance or other expectations about our business and future operating results; our limited operating history; our history of losses; failure of our platform to satisfy customer demands; the effects of increased competition; our investments in new products and our ability to introduce new features, services or enhancements; our ability to effectively expand our sales and marketing organization; our ability to continue to build and maintain credibility with the developer community; our ability to add new customers or increase sales to our existing customers; our ability to maintain, protect, enforce and enhance our intellectual property; the growth and expansion of the market for database products and our ability to penetrate that market; our ability to integrate acquired businesses and technologies successfully or achieve the expected benefits of such acquisitions; our ability to maintain the security of our software and adequately address privacy concerns; our ability to manage our growth effectively and successfully recruit and retain additional highly-qualified personnel; and the price volatility of our common stock. These and other risks and uncertainties are more fully described in our filings with the Securities and Exchange Commission (“SEC”), including under the caption “Risk Factors” in our Quarterly Report on Form 10-Q for the quarter ended April 30, 2023, filed with the SEC on June 2, 2023 and other filings and reports that we may file from time to time with the SEC. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations or otherwise.

MongoDB Public Relations
press@mongodb.com

SOURCE MongoDB, Inc.

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Posted on mongodb google news. Visit mongodb google news

MongoDB Atlas Vector Search now includes extended capabilities for querying contextual data and performance improvements to accelerate building generative AI applications

New integration with Confluent Cloud and MongoDB Atlas Vector Search allows developers to access real-time data streams from a variety of sources to fuel generative AI applications

Dataworkz, Drivly, ExTrac, Inovaare Corporation, NWO.ai, One AI, and VISO Trust among organizations building with MongoDB Atlas Vector Search

LONDON


,


Sept. 26, 2023


/PRNewswire/ — MongoDB, Inc. (NASDAQ: MDB) today at MongoDB.local

London

announced new capabilities, performance improvements, and a data-streaming integration for MongoDB Atlas Vector Search that make it even faster and easier for developers to build generative AI applications. Organizations of all sizes have rushed to adopt MongoDB Atlas Vector Search as part of a unified solution to process data for generative AI applications since being announced in preview in June of this year. MongoDB Atlas Vector Search has made it even easier for developers to aggregate and filter data, improving semantic information retrieval and reducing hallucinations in AI-powered applications. With new performance improvements for MongoDB Atlas Vector Search, the time it takes to build indexes is now significantly reduced by up to 85 percent to help accelerate application development. Additionally, MongoDB Atlas Vector Search is now integrated with fully managed data streams from Confluent Cloud to make it easier to use real-time data from a variety of sources to power AI applications. To learn more about MongoDB Atlas Vector Search, visit


mongodb.com/products/platform/atlas-vector-search


.

“It has been really exciting to see the overwhelmingly positive response to the preview version of MongoDB Atlas Vector Search as our customers eagerly move to incorporate generative AI technologies into their applications and transform their businesses—without the complexity and increased operational burden of ‘bolting on’ yet another software product to their technology stack. Customers are telling us that having the capabilities of a vector database directly integrated with their operational data store is a game changer for their developers,” said

Sahir Azam

, Chief Product Officer at MongoDB. “This customer response has inspired us to iterate quickly with new features and improvements to MongoDB Atlas Vector Search, helping to make building application experiences powered by generative AI even more frictionless and cost effective.”

Many organizations today are on a mission to invent new classes of applications that take advantage of generative AI to meet end-user expectations. However, the large language models (LLMs) that power these applications require up-to-date, proprietary data in the form of vectors—numerical representations of text, images, audio, video, and other types of data. Working with vector data is new for many organizations, and single-purpose vector databases have emerged as a short-term solution for storing and processing data for LLMs. However, adding a single-purpose database to their technology stack requires developers to spend valuable time and effort learning the intricacies of developing with and maintaining each point solution. For example, developers must synchronize data across data stores to ensure applications can respond in real time to end-user requests, which is difficult to implement and can significantly increase complexity, cost, and potential security risks. Many single-purpose databases also lack the flexibility to run as a managed service on any major cloud provider for high performance and resilience, severely limiting long-term infrastructure options. Because of these challenges, organizations from early-stage startups to established enterprises want the ability to store vectors alongside all of their data in a flexible, unified, multi-cloud developer data platform to quickly deploy applications and improve operational efficiency.

MongoDB Atlas Vector Search addresses these challenges by providing the capabilities needed to build generative AI applications on any major cloud provider for high availability and resilience with significantly less time and effort. MongoDB Atlas Vector Search provides the functionality of a vector database integrated as part of a unified developer data platform, allowing teams to store and process vector embeddings alongside virtually any type of data to more quickly and easily build generative AI applications. Dataworkz, Drivly, ExTrac, Inovaare Corporation, NWO.ai, One AI, VISO Trust, and many other organizations are already using MongoDB Atlas Vector Search in preview to build AI-powered applications for reducing public safety risk, improving healthcare compliance, surfacing intelligence from vast amounts of content in multiple languages, streamlining customer service, and improving corporate risk assessment. The updated capabilities for MongoDB Atlas Vector Search further accelerate generative AI application development:

• 
  Increase the accuracy of information retrieval for generative AI applications:
  
  Whether personalized movie recommendations, quick responses from chatbots for customer service, or tailored options for food delivery, application end-users today expect accurate, up-to-date, and highly engaging experiences that save them time and effort. Generative AI is helping developers deliver these capabilities, but the LLMs powering applications can hallucinate (i.e., generate inaccurate information that is not useful) because they lack the necessary context to provide relevant information. By extending MongoDB Atlas’s unified query interface, developers can now create a dedicated data aggregation stage with MongoDB Atlas Vector Search to filter results from proprietary data and significantly improve the accuracy of information retrieval to help reduce LLM hallucinations in applications.
• 
  Accelerate data indexing for generative AI applications:
  
  Generating vectors is the first step in preparing data for use with LLMs. Once vectors are created, an index must be built for the data to be efficiently queried for information retrieval—and when data changes or new data is available, the index must then be updated. The unified and flexible document data model powering MongoDB Atlas Vector Search allows operational data, metadata, and vector data to be seamlessly indexed in a fully managed environment to reduce complexity. With new performance improvements, the time it takes to build an index with MongoDB Atlas Vector Search is now reduced by up to 85 percent to help accelerate developing AI-powered applications.
• 
  Use real-time data streams from a variety of sources for AI-powered applications:
  
  Businesses use Confluent Cloud’s fully managed, cloud-native data streaming platform to power highly engaging, responsive, real-time applications. As part of the
  
  
  Connect with Confluent
  
  
  partner program, developers can now use Confluent Cloud data streams within MongoDB Atlas Vector Search as an additional option to provide generative AI applications ground-truth data (i.e. accurate information that reflects current conditions) in real time from a variety of
  
  
  sources
  
  
  across their entire business. Configured with a fully managed
  
  
  connector
  
  
  for MongoDB Atlas, developers can make applications more responsive to changing conditions and provide end user results with greater accuracy.

Organizations Already Innovating with MongoDB Atlas Vector Search in Preview

Dataworkz enables enterprises to harness the power of LLMs on their own proprietary data by combining data, transformations, and AI into a single experience to produce high-quality, LLM-ready data. “Our goal is to accelerate the creation of AI applications with a product offering that unifies data, processing, and machine learning for business analysts and data engineers,” said

Sachin Smotra

, CEO and co-founder of Dataworkz. “Leveraging the power of MongoDB Atlas Vector Search has allowed us to enable semantic search and contextual information retrieval, vastly improving our customers’ experiences and providing more accurate results. We look forward to continuing using Atlas Vector Search to make retrieval-augmented generation with proprietary data easier for highly relevant results and driving business impact for our customers.”

Drivly provides commerce infrastructure for the automotive industry to programmatically buy and sell vehicles through simple APIs. “We are using AI embeddings and Atlas Vector Search to go beyond full-text search with semantic meaning, giving context and memory to generative AI car-buying assistants,” said

Nathan Clevenger

, Founder and CTO at Drivly. “We are very excited that MongoDB has added vector search capabilities to Atlas, which greatly simplifies our engineering efforts.”

ExTrac draws on thousands of data sources identified by domain experts, using AI-powered analytics to locate, track, and forecast both digital and physical risks to public safety in real-time. “Our domain experts find and curate relevant streams of data, and then we use AI to anonymize and make sense of it at scale. We take a base model and fine-tune it with our own labeled data to create domain-specific models capable of identifying and classifying threats in real-time,” said

Matt King

, CEO of ExTrac. “Atlas Vector Search is proving to be incredibly powerful across a range of tasks where we use the results of the search to augment our LLMs and reduce hallucinations. We can store vector embeddings right alongside the source data in a single system, enabling our developers to build new features way faster than if they had to bolt-on a standalone vector database—many of which limit the amount of data that can be returned if it has meta-data attached to it. Because the flexibility of MongoDB’s document data model allows us to land, index, and analyze data of any shape and structure—no matter how complex—we are now moving beyond text to vectorize images and videos from our archives dating back over a decade. Being able to query and analyze data in any modality will help us to better model trends, track evolving narratives, and predict risk for our customers.”

Inovaare Corporation is a leading provider of AI-powered compliance automation solutions for healthcare payers. “At Inovaare Corporation, we believe that healthcare compliance is not just about meeting regulations but transforming how healthcare payers excel in the entire compliance lifecycle. We needed a partner with the technological prowess and one who shares our vision to pioneer the future of healthcare compliance,” said

Mohar Mishra

, CTO and Co-Founder at Inovaare Corporation. “MongoDB’s robust data platform, known for its scalability and agility, perfectly aligns with Inovaare’s commitment to providing healthcare payers with a unified, secure, and AI-powered compliance operations platform. MongoDB’s innovative Atlas Vector Search powers the reporting capabilities of our products. It allows us to deliver context-aware compliance guidance and real-time data-driven insights.”

NWO.ai is a premier AI-driven Consumer Intelligence platform helping Fortune 500 brands bring new products to market. “In today’s rapidly evolving digital age, the power of accurate and timely information is paramount,” said

Pulkit Jaiswal

, Cofounder of NWO.ai. “At NWO.ai, our flagship offering, Worldwide Optimal Policy Response (WOPR), is at the forefront of intelligent diplomacy. WOPR harnesses the capabilities of AI to navigate the vast oceans of global narratives, offering real-time insights and tailored communication strategies. This not only empowers decision-makers but also provides a vital counterbalance against AI-engineered disinformation. We’re thrilled to integrate Atlas Vector Search into WOPR, enhancing our ability to instantly search and analyze embeddings for our dual-use case. It’s an exciting synergy, and we believe it’s a testament to the future of diplomacy in the digital age.”

One AI is a platform that offers AI Agents, Language Analytics, and APIs, enabling seamless integration of accurate, production-ready language capabilities into products and services. “Our hero product – OneAgent – facilitates trusted conversations through AI agents that operate strictly upon company-sourced content, secured with built-in fact-checking,” said

Amit Ben

, CEO and Founder of One AI. “With MongoDB Atlas, we’re able to take source customer documents, generate vector embeddings from them that we then index and store in MongoDB Atlas Vector Search. Then, when a customer has a question about their business and asks one of our AI agents, Atlas Vector Search will provide the chatbot with the most relevant data and supply customers with the most accurate answers. By enabling semantic search and information retrieval, we’re providing our customers with an improved and more efficient experience.”

VISO Trust puts reliable, comprehensive, actionable vendor security information directly in the hands of decision-makers who need to make informed risk assessments. “At VISO Trust, we leverage innovative technologies to continue our growth and expansion in AI and security. Atlas Vector Search, combined with the efficiency of AWS and Terraform integrations, has transformed our platform,” said

Russell Sherman

, Cofounder and CTO at VISO Trust. “With Atlas Vector Search, we now possess a battle-tested vector and metadata database, refined over a decade, effectively addressing our dense retrieval requirements. There’s no need to deploy a new database, as our vectors and artifact metadata can be seamlessly stored alongside each other.”

About MongoDB Atlas


MongoDB Atlas is the leading multi-cloud developer data platform that accelerates and simplifies building applications with data. MongoDB Atlas provides an integrated set of data and application services in a unified environment that enables development teams to quickly build with the performance and scale modern applications require. Tens of thousands of customers and millions of developers worldwide rely on MongoDB Atlas every day to power their business-critical applications. To get started with MongoDB Atlas, visit


mongodb.com/atlas


.

About MongoDB


Headquartered in

New York

, MongoDB’s mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. Built by developers, for developers, our developer data platform is a database with an integrated set of related services that allow development teams to address the growing requirements for today’s wide variety of modern applications, all in a unified and consistent user experience. MongoDB has tens of thousands of customers in over 100 countries. The MongoDB database platform has been downloaded hundreds of millions of times since 2007, and there have been millions of builders trained through MongoDB University courses. To learn more, visit


mongodb.com


.

Forward-looking Statements


This press release includes certain “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended, including statements concerning MongoDB’s technology and offerings. These forward-looking statements include, but are not limited to, plans, objectives, expectations and intentions and other statements contained in this press release that are not historical facts and statements identified by words such as “anticipate,” “believe,” “continue,” “could,” “estimate,” “expect,” “intend,” “may,” “plan,” “project,” “will,” “would” or the negative or plural of these words or similar expressions or variations. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations, strategies and prospects as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations or strategies will be attained or achieved. Furthermore, actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control including, without limitation: the impact the COVID-19 pandemic may have on our business and on our customers and our potential customers; the effects of the ongoing military conflict between

Russia

and

Ukraine

on our business and future operating results; economic downturns and/or the effects of rising interest rates, inflation and volatility in the global economy and financial markets on our business and future operating results; our potential failure to meet publicly announced guidance or other expectations about our business and future operating results; our limited operating history; our history of losses; failure of our platform to satisfy customer demands; the effects of increased competition; our investments in new products and our ability to introduce new features, services or enhancements; our ability to effectively expand our sales and marketing organization; our ability to continue to build and maintain credibility with the developer community; our ability to add new customers or increase sales to our existing customers; our ability to maintain, protect, enforce and enhance our intellectual property; the growth and expansion of the market for database products and our ability to penetrate that market; our ability to integrate acquired businesses and technologies successfully or achieve the expected benefits of such acquisitions; our ability to maintain the security of our software and adequately address privacy concerns; our ability to manage our growth effectively and successfully recruit and retain additional highly-qualified personnel; and the price volatility of our common stock. These and other risks and uncertainties are more fully described in our filings with the Securities and Exchange Commission (“SEC”), including under the caption “Risk Factors” in our Quarterly Report on Form 10-Q for the quarter ended

April 30, 2023

, filed with the SEC on

June 2, 2023

and other filings and reports that we may file from time to time with the SEC. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations or otherwise.

MongoDB Public Relations




press@mongodb.com

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SOURCE MongoDB, Inc.

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Article originally posted on InfoQ. Visit InfoQ

Microsoft recently announced the general availability of Azure Update Manager, known previously as Update Management Center – a SaaS solution to manage and govern software updates to Windows and Linux machines across Azure, on-premises, and multi-cloud environments.

According to the company, the solution is an evolution of Azure Automation Update management that includes new features and functionality for the assessment and deployment of software updates on a single machine or multiple machines. The solution brings features like:

• The ability to enable incremental rollout of updates to Azure VMs in off-peak hours using automatic VM guest patching and reduce reboots by enabling hot patching.
• Allow operators to view and deploy pending updates to instantly secure machines and manage extended security updates (ESUs) for Azure Arc-enabled Windows Server 2012/2012 R2 machines. Furthermore, allowing them to define recurring time windows during which machines receive updates and may undergo reboots using scheduled patching and enforce machines grouped based on standard Azure constructs (Subscription, Location, Resource Group, Tags, etc.) to have common patch schedules using dynamic scoping.
• Proving to automatically assess machines for pending updates every 24 hours and flag machines out of compliance.
• Allowing to enforce enabling periodic assessments on multiple machines using Azure Policy.
• Create custom reports.

Screenshot of Azure Update Manager (Source: Microsoft Tech Community blog post)

Azure Update Manager supports the management of Azure VMs and non-Azure machines through a new Azure extension that the company designed to provide all the functionality required to interact with the operating system to manage the assessment and application of updates. The extension is automatically installed when operators initiate any Update Manager operations, such as Check for updates, Install one-time update, and Periodic Assessments on a machine. In addition, the deployment of the extension to Azure VMs or Azure Arc-enabled servers is possible via the Azure VM Windows agent or the Azure VM Linux agent for Azure VMs – and the Azure Arc-enabled servers’ agent for non-Azure Linux and Windows machines or physical servers.

The company states that Update Manager brings, besides new features, more benefits than its predecessor, Update Manager, such as no dependency on Log Analytics and Azure Automation and Azure Resource Manager-based operations.  However, John Joyner, a Microsoft Azure MVP, points out in a LinkedIn post:

Azure Update Manager is free for machines hosted on Azure or Azure Stack HCI. For Arc-enabled servers, it’s chargeable up to $5/server/month”. Since Azure Automation Update Management was free to all servers, including Azure Arc, this represents a significant cost increase for customers with large server populations outside Azure.

In addition, a respondent on a Reddit thread commented:

Microsoft announced last year that the Log Analytics agent will be deprecated and that Automation Update Management customers would be migrated to Azure Update Manager once the preview ended.

Feels like a giant bait and switch as Update management is free for Arc/onprem, and there was no reason to believe what would be replacing it wouldn’t also be free, let alone such a high price for a minimal service.

While in the same thread, another respondent pointed out:

Enabling “Microsoft updates” (instead of just “Windows updates”) will delete SQL Server AlwaysOn Availability Group configurations.

MS Support acknowledged that this is a bug in Update Manager, and a hotfix is yet to be implemented.

Do NOT use it on your SQL IaaS VM clusters, it’ll blow them away.

Lastly, more details on the Azure Update Manager are available on the documentation landing page and FAQs.

Article originally posted on InfoQ. Visit InfoQ

This week’s Java roundup for September 18th, 2023 features news from OpenJDK, JDK 22, JDK 21, GraalVM, Corretto, Liberica, Epicyro 3.0, Pinot 1.0, and releases for: Spring Boot; Spring Integration; Spring Batch; Spring Cloud Dataflow; Spring Security; Spring GraphQL; Spring Authorization Server; Spring Apache Pulsar; Spring Modulith; Quarkus; Open Liberty; Micronaut; Hibernate; OpenXava; Gradle.

OpenJDK

Daniel Smith, Programming Language Designer at Oracle, has submitted JEP 8316779, Value Object Storage Enhancements (Preview). Under the auspices of Project Valhalla, this JEP introduces null-restricted storage of value objects in fields and array components. “These variables are initialized to an initial instance of the class and reject attempts to write a null value. They can be optimized with compact, flattened object encodings.”

JDK 21

Oracle has released version 21 of the Java programming language and virtual machine, which ships with a final feature set of 15 JEPs. More details may be found in this InfoQ news story.

JDK 22

Build 16 of the JDK 22 early-access builds was made available this past week featuring updates from Build 15 that include fixes to various issues. Further details on this build may be found in the release notes.

With no objections to the proposed JDK 22 release schedule, Mark Reinhold, chief architect, Java Platform Group at Oracle, has declared the following release schedule as final:

• Rampdown Phase One (fork from main line): December 7, 2023
• Rampdown Phase Two: January 18, 2024
• Initial Release Candidate: February 8, 2024
• Final Release Candidate: February 22, 2024
• General Availability: March 19, 2024

For JDK 22, developers are encouraged to report bugs via the Java Bug Database.

GraalVM

In conjunction with the release of JDK 21, GraalVM for JDK 21 has also been released by Oracle Labs. new features include: full support for JDK 21; performance enhancements with Profile-Guided Optimizations; a new application levels policy for faster compilation time; and improved developer experience with a new CLI options, --parallelism and --color, for specifying the number of threads and output color during builds, respectively. More details on this release may be found in the release notes. InfoQ will follow up with a more detailed news story.

On the road to version 1.0, Oracle Labs has released version 0.9.27 of Native Build Tools, a GraalVM project consisting of plugins for interoperability with GraalVM Native Image. This latest release provides bug fixes and improvements for GraalVM for JDK 21. Further details on this release may be found in the changelog.

Amazon Corretto

Amazon has released Amazon Corretto 21, their downstream distribution of OpenJDK 21, which is available on Linux, Windows, and macOS. Developers may download this latest version from this site.

BellSoft Liberica JDK

Similarly, BellSoft has released Liberica JDK 21, their downstream distribution of OpenJDK 21. Developers may download this latest version from this site.

Spring Framework

The third milestone release of Spring Boot 3.2.0 delivers bug fixes, dependency upgrades and new feature such as: add the ConnectionDetails interface and @ServiceConnection annotation to the configuration in Spring for Apache Pulsar; provide an instance of the RestClientBuilderConfigurer class to apply Spring Boot defaults to a custom RestClient.Builder interface; and remove the use of the deprecated ServerHttpObservationFilter class for WebFlux instrumentation. More details on this release may be found in the release notes.

Similarly, versions 3.1.4, 3.0.11 and 2.7.16 of Spring Boot provide improvements in documentation, dependency upgrades, a TWENTY_ONE enum constant to the JavaVersion enum class, and notable bug fixes such as: the Saml2RelyingPartyAutoConfiguration class ignores the value set in the sign-request property when using the metadata-url query; a leaking file descriptor and socket within DomainSocket class; and an invalid Accept request HTTP header produces an HTTP 500 Internal Server Error when using the WelcomePageHandlerMapping class. Further details on these releases may be found in the release notes for version 3.1.4, version 3.0.11 and version 2.7.16.

The third milestone release of Spring Integration 6.2.0 ships with dependency upgrades and notable changes such as: a refactor of the KafkaMessageDrivenChannelAdapter class for future maintenance to avoid code duplication; new overloaded executeLocked() methods added to the LockRegistry interface to follow best practice and well-known patterns with the JdbcTemplate, RestTemplate and JmsTemplate classes; and support for custom instances of the DefaultSftpSessionFactory class. More details on this release may be found in the release notes.

The third milestone release of Spring Batch 5.1.0 provides bug fixes, improvements in documentation and new features such as: auto-configure the JobRegistryBeanPostProcessor class with @EnableBatchProcessing annotation and the DefaultBatchConfiguration class for improved job registration with the JobRegistry interface; the ability to specify a database type via a new parameter in the @EnableBatchProcessing annotation; and the ability to provide a custom JobKeyGenerator interface in the JdbcJobInstanceDao class. Further details on this release may be found in the release notes.

The release of Spring Cloud Dataflow 2.11.0 delivers bug fixes, dependency upgrades and support for: Spring Boot 3.x-based stream applications; Spring Cloud Task 3.x-based task applications; and Spring Batch 5.x-based batch applications. There was also an upgrade to the Kubernetes batch/v1 cron job so that developers can now use Kubernetes 1.25.0 and above. More details on this release may be found in the release notes.

Versions 6.2.0-M1, 6.1.4, 6.0.7 and 5.8.7 of Spring Security have been released featuring fixes for CVE-2023-34042, Incorrect Permission Assignment for spring-security.xsd, a vulnerability in which the spring-security.xsd file, found inside the spring-security-config JAR archive, is world writable and could result in an exploit. Developers are encouraged to upgrades to these releases. Further details on these releases may be found in the release notes for version 6.2.0-M1, version 6.1.4, version 6.0.7 and version 5.8.7.

Versions 1.2.3, 1.1.6 and 1.0.5 of Spring for GraphQL have been released deliver bug fixes, improvements in documentation, dependency upgrades and new features such as: the ability to access object type extensions (to complement object types) using the ConnectionTypeDefinitionConfigurer class; raise a Spring Security AuthenticationCredentialsNotFoundException to require authentication when an instance of the Java Principal interface is not present and not declared as Optional; and enhancements to the GraphQL request body checks to prevent an HTTP 500 Internal Server Error. These releases may be consumed with Spring Boot versions 3.1.4, 3.0.11 and 2.7.16, respectively. More details on these releases may be found in the release notes for version 1.2.3, version 1.1.6 and version 1.0.5.

The first milestone release of Spring Authorization Server 1.2.0 ships with bug fixes, dependency upgrades and new features such as: the ability to inject custom metadata to improve client registration; new code challenge methods for OIDC provider configuration response; and improvements in logging with the CodeVerifierAuthenticator class. Further details on this release may be found in the release notes.

The second milestone release of Spring for Apache Pulsar 1.0.0 features notable changes such as: the ability to add multiple customizers to the PulsarAdministration, DefaultPulsarConsumerFactory, DefaultPulsarReaderFactory and DefaultReactivePulsarSenderFactory classes; and move the cache provider modules source files from the default spring.pulsar.core package to a package that is specific to the module name to avoid any confusion with the Java module system. More details on this release may be found in the release notes.

Versions 1.1.0-M1 and 1.0.1 of Spring Modulith have been released provide bug fixes, improvements in documentation, dependency upgrades and new features such as: support to externalize domain events into messaging middleware (Kafka, AMQP, JMS, etc.) by registering an @ApplicationEventListener; a new Neo4j event publication repository; and new interfaces – CompletedEventPublications, IncompleteEventPublications and EventPublicationRepository – for improved handling of completed and incomplete event publications. Further details on these releases may be found in the release notes for version 1.1.0-M1 and version 1.0.1.

Quarkus

The release of Quarkus 3.4.1 features support for Redis 7.2 and changes in support for the Flyway extension that include: the ability to disable the automatic setup of the Flyway extension by setting the quarkus.flyway.enabled property to false; and declare a datasource as inactive for a specific datasource and named datasource by setting the quarkus.flyway.active and quarkus.flyway..active properties, respectively, to false. More details on this release may be found in the changelog.

Open Liberty

IBM has released version 23.0.0.9 of Open Liberty that provides support for: Spring Boot 3.0 requiring Jakarta EE 10, Spring Security 6.x, and a new server template named springBoot3; support for Private Key JWT authentication with OpenID Connect token endpoints; and the ability to set the LTPA or JWT cookie path to the application context root to allow for different LTPA and JWT tokens for different applications.

Micronaut

The Micronaut Foundation has released version 4.1.2 of the Micronaut featuring Micronaut Core 4.1.6 and updates to the Micronaut Data module. Further details on this release may be found in the release notes.

Hibernate

Versions 6.3.1.Final and 6.2.9.Final of Hibernate ORM have been released that ship with bug fixes and improvements in query methods and finder methods. More details on these releases may be found in the release notes for version 6.3.1.Final and version 6.2.9.Final.

Eclipse Foundation

Shortly after it was introduced by OmniFishEE, Eclipse Epicyro 3.0 has formally been released as a standalone implementation of the Jakarta Authentication 3.0 specification. This new project will define a general low-level SPI for authentication mechanisms, controllers that interact with a caller and a container’s environment to obtain the caller’s credentials. These will be validated and pass an authenticated identity (such as name and groups) to a container.

Apache Software Foundation

The release of Apache Pinot 1.0.0, a realtime distributed OLAP datastore, delivers bug fixes, enhancements and new features such as: initial support for Query runtime for Window Functions using ORDER BY clause within the OVER() clause; an early termination in the execution of the SortOperator class if the LIMIT clause is used; and support for partition-based leaf stage processing. Further details on this release may be found in the release notes. InfoQ will follow up with a more detailed news story.

OpenXava

The release of OpenXava 7.1.6 ships with notable fixes such as: improvements in the interactions between the @ElementCollection and @DescriptionsList annotations; grouping after filtering or sorting a list fails with the @Tab annotation if it contains a baseCondition parameter and an instance of the IFilter interface; and an instance of the IForwardAction interface does not work if the application is behind a proxy. More details on this release may be found in the release notes.

Gradle

The first release candidate of Gradle 8.4 delivers: initial support for JDK 21 only to compile, test, and run Gradle projects since Kotlin does not yet support JDK 21; improved compilation on Windows OS; a simplified way to create role-focused instances of the Configuration interface using the ConfigurationContainer interface; and improved support for the Kotlin DSL. Further details on this release may be found in the release notes.

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AE Wealth Management LLC purchased a new position in MongoDB, Inc. (NASDAQ:MDB – Free Report) during the 2nd quarter, according to its most recent filing with the Securities and Exchange Commission (SEC). The firm purchased 3,149 shares of the company’s stock, valued at approximately $1,294,000.

Several other institutional investors and hedge funds have also modified their holdings of the business. Bessemer Group Inc. bought a new position in shares of MongoDB during the 4th quarter valued at approximately $29,000. BI Asset Management Fondsmaeglerselskab A S bought a new position in shares of MongoDB in the fourth quarter worth approximately $30,000. Global Retirement Partners LLC boosted its position in shares of MongoDB by 346.7% in the first quarter. Global Retirement Partners LLC now owns 134 shares of the company’s stock worth $30,000 after purchasing an additional 104 shares during the period. Manchester Capital Management LLC purchased a new stake in shares of MongoDB in the first quarter worth $36,000. Finally, Clearstead Advisors LLC bought a new stake in shares of MongoDB during the 1st quarter valued at $36,000. Hedge funds and other institutional investors own 88.89% of the company’s stock.

Insider Activity

In other MongoDB news, CTO Mark Porter sold 2,734 shares of the firm’s stock in a transaction dated Monday, July 3rd. The shares were sold at an average price of $412.33, for a total transaction of $1,127,310.22. Following the transaction, the chief technology officer now owns 35,056 shares of the company’s stock, valued at approximately $14,454,640.48. The transaction was disclosed in a legal filing with the Securities & Exchange Commission, which is available at the SEC website. In related news, CEO Dev Ittycheria sold 50,000 shares of the firm’s stock in a transaction dated Wednesday, July 5th. The stock was sold at an average price of $407.07, for a total transaction of $20,353,500.00. Following the sale, the chief executive officer now owns 218,085 shares of the company’s stock, valued at $88,775,860.95. The sale was disclosed in a filing with the SEC, which is available through the SEC website. Also, CTO Mark Porter sold 2,734 shares of the business’s stock in a transaction dated Monday, July 3rd. The stock was sold at an average price of $412.33, for a total transaction of $1,127,310.22. Following the completion of the transaction, the chief technology officer now owns 35,056 shares of the company’s stock, valued at $14,454,640.48. The disclosure for this sale can be found here. Over the last 90 days, insiders have sold 104,694 shares of company stock worth $41,820,161. Company insiders own 4.80% of the company’s stock.

Analysts Set New Price Targets

A number of equities research analysts have issued reports on MDB shares. Oppenheimer increased their price target on MongoDB from $430.00 to $480.00 and gave the stock an “outperform” rating in a research note on Friday, September 1st. 22nd Century Group reissued a “maintains” rating on shares of MongoDB in a report on Monday, June 26th. Robert W. Baird increased their price objective on shares of MongoDB from $390.00 to $430.00 in a report on Friday, June 23rd. Royal Bank of Canada reiterated an “outperform” rating and set a $445.00 price objective on shares of MongoDB in a research report on Friday, September 1st. Finally, William Blair restated an “outperform” rating on shares of MongoDB in a research report on Friday, June 2nd. One research analyst has rated the stock with a sell rating, three have given a hold rating and twenty-one have given a buy rating to the company’s stock. According to MarketBeat.com, the company currently has a consensus rating of “Moderate Buy” and an average price target of $418.08.

View Our Latest Analysis on MongoDB

MongoDB Price Performance

MongoDB stock opened at $333.31 on Tuesday. The stock has a market cap of $23.78 billion, a price-to-earnings ratio of -96.33 and a beta of 1.11. The firm has a 50-day moving average of $379.25 and a two-hundred day moving average of $322.28. MongoDB, Inc. has a 1 year low of $135.15 and a 1 year high of $439.00. The company has a debt-to-equity ratio of 1.29, a current ratio of 4.48 and a quick ratio of 4.48.

MongoDB (NASDAQ:MDB – Get Free Report) last announced its quarterly earnings results on Thursday, August 31st. The company reported ($0.63) earnings per share (EPS) for the quarter, beating analysts’ consensus estimates of ($0.70) by $0.07. The firm had revenue of $423.79 million during the quarter, compared to analyst estimates of $389.93 million. MongoDB had a negative return on equity of 29.69% and a negative net margin of 16.21%. On average, research analysts forecast that MongoDB, Inc. will post -2.17 earnings per share for the current year.

MongoDB Company Profile

(Free Report)

MongoDB, Inc provides general purpose database platform worldwide. The company offers MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premise, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

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This instant news alert was generated by narrative science technology and financial data from MarketBeat in order to provide readers with the fastest and most accurate reporting. This story was reviewed by MarketBeat’s editorial team prior to publication. Please send any questions or comments about this story to contact@marketbeat.com.

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Cornerstone Wealth Management LLC, a financial services firm, recently disclosed the purchase of a new stake in MongoDB, Inc. during the second quarter. According to the company’s filing with the Securities and Exchange Commission, Cornerstone acquired 706 shares of MongoDB’s stock, valued at approximately $290,000.

MongoDB is a reputable company listed on the NASDAQ stock exchange under the ticker symbol MDB. The company’s shares opened at $335.76 on September 25, 2023. Investors have shown interest in MongoDB due to its robust financial performance and potential growth prospects.

Examining some key financial indicators can provide insights into MongoDB’s current position in the market. The company maintains a debt-to-equity ratio of 1.29, indicating a moderate level of leverage. Additionally, MongoDB exhibits favorable liquidity ratios with a quick ratio and current ratio both standing at 4.48.

To further evaluate its performance, investors often consider moving averages. Currently, MongoDB has a fifty-day simple moving average of $380.77 and a two-hundred-day simple moving average of $321.41. These figures reflect recent trends in the stock’s price and assist investors in analyzing short-term and long-term performance.

Over the past year, MongoDB has experienced fluctuations in its share price ranging from a low of $135.15 to a high of $439.00. This volatility underscores both the opportunities and risks associated with investing in high-growth technology companies like MongoDB.

Cornerstone Wealth Management LLC’s decision to purchase additional shares indicates their confidence in MongoDB’s future prospects as an investment opportunity. However, it is essential for investors to conduct comprehensive research and analysis before making any investment decisions.

It will be interesting to monitor how this new stake by Cornerstone Wealth Management LLC affects MongoDB’s stock performance going forward and whether other institutional investors follow suit based on their assessment of the company’s growth potential.

As always, potential investors are advised to seek guidance from a qualified financial advisor or conduct their own due diligence before making any investment decisions in the stock market.

Growing Interest in MongoDB’s Stock and Insider Trading Activities Spark Investor Attention

September 25, 2023 – In recent months, there have been several interesting developments in MongoDB’s stock activity and insider trading. Hedge funds such as Bessemer Group Inc., BI Asset Management Fondsmaeglerselskab A S, Global Retirement Partners LLC, Manchester Capital Management LLC, and Clearstead Advisors LLC have made significant modifications to their holdings of MongoDB. Institutional investors now own around 88.89% of the company’s stock.

Bessemer Group Inc. acquired a new position in MongoDB during the fourth quarter, investing approximately $29,000. Similarly, BI Asset Management Fondsmaeglerselskab A S also purchased shares worth about $30,000 during the same period. Global Retirement Partners LLC saw a significant boost in its stake in MongoDB by 346.7% during the first quarter after acquiring an additional 104 shares of the company’s stock. Manchester Capital Management LLC also entered the market during the first quarter with a new position valued at approximately $36,000. Lastly, Clearstead Advisors LLC also joined the list of institutional investors by purchasing a new position worth around $36,000 during the first quarter.

These activities by hedge funds and institutional investors indicate that there is growing interest in MongoDB as an investment opportunity. The presence of prominent and experienced financial institutions entering or increasing their positions in the company suggests that they see potential for significant returns.

In other news related to MongoDB, Director Dwight A. Merriman recently sold a total of 4,000 shares of company stock on July 3rd for an average price of $407.11 per share. This transaction amounted to a total value of $1,628,440.00. Following this sale, Merriman now holds 1,214,765 shares directly in the company’s stock with an estimated value of $494,542,979.15. It is worth noting that insiders have collectively sold 104,694 shares of MongoDB stock valued at $41,820,161 over the last ninety days. Currently, insiders own 4.80% of the company’s stock.

Turning to the financial performance of MongoDB, the company recently released its quarterly earnings data as of August 31st. Analysts’ consensus estimates were beaten by a margin of $0.07 per share with an EPS (earnings per share) of ($0.63), compared to an expected value of ($0.70). The company reported a negative net margin of 16.21% and a negative return on equity of 29.69%. Additionally, MongoDB achieved revenue of $423.79 million during the quarter, surpassing analysts’ expectations of $389.93 million.

Analysts have also been closely watching MongoDB’s stock activity and providing their recommendations based on their research findings. Capital One Financial initiated coverage on MongoDB with an “equal weight” rating and set a target price of $396.00 per share in June 2023. Oppenheimer raised their target price from $430.00 to $480.00 and gave the stock an “outperform” rating in September 2023.

Robert W. Baird increased their target price from $390.00 to $430.00 in June 2023, while Truist Financial elevated it to $430.00 in September 2023, both giving the stock a “buy” rating.

Morgan Stanley raised their target price from $440.00 to $480.00 and assigned an “overweight” rating to the stock in September 2023.

In conclusion, MongoDB has captured the attention of hedge funds and institutional investors who have made significant modifications to their holdings recently, reflecting growing interest in this investment opportunity.

Meanwhile, Director Dwight A Merriman’s insider trading activities have drawn attention as he sold shares worth millions of dollars over the past few months. However, it is crucial to note that insider sales are a regular occurrence and do not necessarily indicate negative sentiment towards the company.

MongoDB’s strong quarterly earnings report, beating analysts’ consensus estimates, suggests that the company is in a favorable position in terms of financial performance.

Lastly, research analysts have provided positive recommendations on MongoDB, with most assigning “buy” ratings and setting target prices above the current market value.

Investors may find these recent activities and analyst opinions valuable as they consider their investment decisions regarding MongoDB.

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I recently spoke with the MongoDB team about the much-anticipated MongoDB version 7. One feature they were particularly interested in discussing was Queryable Encryption. Let’s delve into this feature and understand how it aims to shield sensitive data, even when it’s being accessed through queries.

Who is MongoDB?

Located in New York City, MongoDB has created a developer data platform tailored for modern applications’ flexibility and expansion needs. MongoDB is a non-relational document database supporting JSON-like data storage. Its adaptable data model lets you store many types of data, including unstructured data, complemented by comprehensive indexing support and intuitive APIs. With a global presence, MongoDB boasts tens of thousands of clients across more than 100 countries. The company was birthed in 2007 by Dwight Merriman, Eliot Horowitz and Kevin Ryan, who were previously associated with DoubleClick, a digital advertising firm now under Google’s umbrella. The trio created MongoDB to answer the challenges posed by traditional databases in data storage scalability and flexibility.

MongoDB is constructed to naturally scale, accommodating large data volumes by augmenting servers for load distribution. The database’s adaptability facilitates smooth alterations and enhancements without disrupting preexisting data. Given the importance of performance in data management, MongoDB’s document-centric model, coupled with its indexing proficiency, ensures swift data access and modifications. It also incorporates automatic data redundancy and failover mechanisms, guaranteeing consistent availability. MongoDB utilizes a JSON-esque format, BSON, which simplifies the storage and extraction of intricate data constructs. When it comes to support, MongoDB is backed by a strong community, offering an abundance of resources, guides and assistance.

Importance of data protection and security

Most organizations are intensifying measures to secure their data infrastructures. Adhering to regulatory standards is also crucial, especially when dealing with personally identifiable information (PII), protected health information (PHI) and other sensitive data.

How do these institutions safeguard their data? They primarily turn to encryption, a method that transforms critical information into a coded version using cryptographic algorithms. A decryption key, exclusively possessed by the client, is essential to decode and access the original data.

While data can be encrypted during transmission over networks (data in motion), in storage (data at rest) and during processing (data in use), managing encrypted data while it’s being used has significant complexities, as it generally needs to be decrypted for processing. Organizations handling delicate data aim to boost their security by maintaining encryption throughout the data’s lifecycle, even during queries. Until recently, maintaining such continuous encryption required the expertise of specialized cryptographic teams.

The image above provides more details about how Queryable Encryption offers heightened protection for sensitive data, even in cloud settings.

What is Queryable Encryption?

Queryable Encryption offers a means to privately query encrypted data without the initial step of decryption. Relying on an advanced encrypted search algorithm, servers can handle queries on this data, all while keeping the data’s content concealed, both from the database itself and from the service operators. Data stays encrypted continuously, even under search conditions.

When a query is made, MongoDB sends the encrypted findings to a software driver, which then undergoes decryption at the user’s end, ensuring consistent security throughout any form of access. This cutting-edge technology is pivotal for safeguarding confidential data and finds its place in sectors like finance, healthcare and government. Besides eliminating the decryption step during searches, the new version of MongoDB stands out for its adaptability, efficacy and intuitive design.

Queryable encryption use cases

As concerns over data security increase for organizations, the prominence of Queryable Encryption technology will continue to rise. Since MongoDB released this innovation, the company has been working with key customers in leading industries on implementation. Let me provide a few examples.

• Financial institutions can use Queryable Encryption to secure customer details like credit cards and Social Security numbers.

• Healthcare entities can use Queryable Encryption to protect patient data, including medical records and lab results.

• Government agencies can use Queryable Encryption to protect the confidentiality of law enforcement records and classified intel.

• E-commerce platforms can use Queryable Encryption to help guard customers’ purchase histories and contact data.

One of the first companies to deploy the technology is the big automaker Renault. It has begun using MongoDB Queryable Encryption for ensuring data protection and meeting security and compliance requirements.

Competitive arena

MongoDB’s Queryable Encryption is a notable advancement in data protection, yet it’s essential to recognize its constraints. MongoDB has made it easier for developers to implement this feature so customers can easily protect their sensitive data without being experts in cryptography. Yet there are certain MongoDB functionalities, like complex aggregations and geospatial queries, that may not be compatible as updates in drivers and servers are ongoing. Moreover, encryption can potentially slow down query performance, especially for intricate operations, because it introduces additional processing overhead. While MongoDB offers enhanced security, it’s no silver bullet; vulnerabilities like side-channel attacks and classic application-side risks remain. It’s crucial to weigh these factors when integrating MongoDB’s Queryable Encryption.

While MongoDB alone offers Queryable Encryption, other formidable database players such as Couchbase and Cassandra offer enhanced performance and scalability while being able to manage significant data volumes with a flexible schema. AWS’s DynamoDB provides a robust managed NoSQL solution with global reach, while CouchDB emphasizes document-oriented storage. Oracle’s NoSQL service and Microsoft’s Azure Cosmos DB cater to diverse data models with an emphasis on scalability and global distribution. ArangoDB offers multi-model capabilities, whereas Redis thrives on high-speed in-memory operations. Elasticsearch excels in real-time analytics, and MarkLogic handles large volumes of virtually any type of data. In short, each solution has tailored its offerings to meet specific needs in the ever-evolving database market.

Summary

Queryable Encryption could help businesses revolutionize their data security. It allows developers to implement a progressive encryption method to strengthen their applications’ defenses while complying with privacy regulations like GDPR and enhancing security—all with no cryptography experience required. For now, MongoDB 7.0 allows Queryable Encryption to be used for equality queries on encrypted data. The company says that “future releases will add support to the range, prefix, suffix and substring query types.”

While it is important to be aware of some of the challenges associated with Queryable Encryption, such as performance constraints on intensive queries and compatibility and support issues due to its relative newness, it is without a doubt an influential addition to MongoDB.

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When it comes to deploying Large Language Models (LLMs) in production, the two major challenges originate from the huge amount of parameters they require and the necessity of handling very long input sequences to represent contextual information. Hugging Face has documented a list of techniques to tackle those hurdles based on their experience serving such models.

The three techniques Hugging Face researches Patrick von Platen describes in his article are operating at a reduced numerical precision, using a variation of the attention algorithm known as Flash Attention, and using specialized architectures for inference.

LLMs require huge quantity of VRAM to be loaded, ranging from dozens (bigcode/starcoder) to hundreds of gigabytes (Llama, Bloom, GPT3). A first optimization is possible by switching from float32`` to bfloat16“ precision:

Almost all models are trained in bfloat16 nowadays, there is no reason to run the model in full float32 precision if your GPU supports bfloat16. Float32 won’t give better inference results than the precision that was used to train the model.

This enables halving the overall memory consumption but, unfortunately, required memory could still be too much in many cases. A more aggressive approach consists then in quantizing model weights to 8-bit or 4-bits, which has been shown not to cause a significant performance loss.

Quantization works especially well for text generation since all we care about is choosing the set of most likely next tokens and don’t really care about the exact values of the next token logit distribution.

This will enable further reducing required memory, which makes it possible to run the smaller models on off-the-shelf GPUs with just 16GB of VRAM, albeit at the cost of slightly longer inference time.

Using Flash Attention, a novel algorithm for the self-attention layers LLMs apply to understand the contextual relationships between input tokens, is another key optimization, says von Platen, since it makes it possible to break the quadratic growth of the layers with the number of input tokens.

The algorithm is too complex to summarize here, but it suffices to say that using softmax normalization statistics and some smart mathematics, it provides identical outputs while only requiring memory that grows linearly with input tokens. Inference performance is also benefited thanks to the algorithm using faster SRAM instead of the slower GPU VRAM.

In practice, there is currently absolutely no reason to not use Flash Attention if available. The algorithm gives mathematically the same outputs, and is both faster and more memory-efficient.

The third area where LLMs can be optimized in production is choosing the right architecture for them to be able to handle long text inputs effectively and efficiently. Here recent research can help to make the right choice with two components that quickly become bottlenecks, says von Platen, positional embeddings and the key-value cache.

Positional embeddings provide a cue for an LLM to understand sequence order by encoding the position of each token into a numerical presentation. For LLMs intended to solve tasks requiring handling large text inputs, relative positional embeddings such as RoPE and ALiBi should be used for training.

Both RoPE and ALiBi position encodings can extrapolate to input lengths not seen during training whereas it has been shown that extrapolation works much better out-of-the-box for ALiBi as compared to RoPE.

Both algorithms are already available in a number of current LLMs.

The key-value chat can be used as a means to encode the context of a conversation. The key-value cache grows by one element at each new interaction, which is much more effective than the alternative approach of encoding/decoding the context with each request. von Platen goes into the details of two classes of key-value caches, namely Multi-Query-Attention (MQA) and Grouped-Query-Attention (GQA) to show the advantages they bring.

von Platen’s article cover much more ground than can be summarized here and provides hands-on examples to demonstrate his points, so do not miss his article for the full picture.

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HB Wealth Management LLC purchased a new stake in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) in the 2nd quarter, according to the company in its most recent disclosure with the SEC. The institutional investor purchased 580 shares of the company’s stock, valued at approximately $238,000.

A number of other hedge funds and other institutional investors have also added to or reduced their stakes in the business. Price T Rowe Associates Inc. MD lifted its position in MongoDB by 13.4% in the first quarter. Price T Rowe Associates Inc. MD now owns 7,593,996 shares of the company’s stock worth $1,770,313,000 after buying an additional 897,911 shares during the last quarter. Vanguard Group Inc. boosted its stake in shares of MongoDB by 2.1% during the 1st quarter. Vanguard Group Inc. now owns 5,970,224 shares of the company’s stock worth $2,648,332,000 after purchasing an additional 121,201 shares during the period. State Street Corp grew its holdings in MongoDB by 1.8% during the 1st quarter. State Street Corp now owns 1,386,773 shares of the company’s stock valued at $323,280,000 after purchasing an additional 24,595 shares during the last quarter. 1832 Asset Management L.P. raised its position in MongoDB by 3,283,771.0% in the 4th quarter. 1832 Asset Management L.P. now owns 1,018,000 shares of the company’s stock worth $200,383,000 after purchasing an additional 1,017,969 shares during the period. Finally, Geode Capital Management LLC boosted its position in shares of MongoDB by 3.8% during the first quarter. Geode Capital Management LLC now owns 967,289 shares of the company’s stock valued at $225,174,000 after buying an additional 35,541 shares during the period. 88.89% of the stock is owned by hedge funds and other institutional investors.

Insider Transactions at MongoDB

In other MongoDB news, CEO Dev Ittycheria sold 50,000 shares of the business’s stock in a transaction dated Wednesday, July 5th. The stock was sold at an average price of $407.07, for a total transaction of $20,353,500.00. Following the sale, the chief executive officer now owns 218,085 shares in the company, valued at approximately $88,775,860.95. The transaction was disclosed in a document filed with the SEC, which can be accessed through the SEC website. In other news, CEO Dev Ittycheria sold 50,000 shares of the stock in a transaction on Wednesday, July 5th. The shares were sold at an average price of $407.07, for a total value of $20,353,500.00. Following the sale, the chief executive officer now owns 218,085 shares in the company, valued at $88,775,860.95. The transaction was disclosed in a document filed with the Securities & Exchange Commission, which can be accessed through this hyperlink. Also, CTO Mark Porter sold 2,734 shares of the business’s stock in a transaction on Monday, July 3rd. The shares were sold at an average price of $412.33, for a total transaction of $1,127,310.22. Following the transaction, the chief technology officer now owns 35,056 shares of the company’s stock, valued at $14,454,640.48. The disclosure for this sale can be found here. Over the last ninety days, insiders sold 104,694 shares of company stock worth $41,820,161. Company insiders own 4.80% of the company’s stock.

Analyst Upgrades and Downgrades

A number of equities research analysts recently issued reports on the company. Macquarie raised their price target on MongoDB from $434.00 to $456.00 in a research note on Friday, September 1st. William Blair reissued an “outperform” rating on shares of MongoDB in a research note on Friday, June 2nd. Needham & Company LLC raised their price target on shares of MongoDB from $430.00 to $445.00 and gave the stock a “buy” rating in a research note on Friday, September 1st. Argus upped their target price on shares of MongoDB from $435.00 to $484.00 and gave the stock a “buy” rating in a research note on Tuesday, September 5th. Finally, The Goldman Sachs Group lifted their price target on shares of MongoDB from $420.00 to $440.00 in a research note on Friday, June 23rd. One analyst has rated the stock with a sell rating, three have assigned a hold rating and twenty-one have issued a buy rating to the stock. Based on data from MarketBeat.com, the company presently has a consensus rating of “Moderate Buy” and a consensus target price of $418.08.

Get Our Latest Stock Analysis on MongoDB

MongoDB Trading Up 0.6 %

Shares of NASDAQ:MDB opened at $335.76 on Monday. The business’s 50-day moving average is $380.77 and its 200 day moving average is $321.41. The company has a debt-to-equity ratio of 1.29, a current ratio of 4.48 and a quick ratio of 4.48. MongoDB, Inc. has a 1 year low of $135.15 and a 1 year high of $439.00. The firm has a market capitalization of $23.95 billion, a P/E ratio of -97.04 and a beta of 1.11.

MongoDB (NASDAQ:MDB – Get Free Report) last posted its quarterly earnings data on Thursday, August 31st. The company reported ($0.63) earnings per share (EPS) for the quarter, beating the consensus estimate of ($0.70) by $0.07. The firm had revenue of $423.79 million for the quarter, compared to the consensus estimate of $389.93 million. MongoDB had a negative net margin of 16.21% and a negative return on equity of 29.69%. On average, research analysts predict that MongoDB, Inc. will post -2.17 earnings per share for the current fiscal year.

MongoDB Profile

(Free Report)

MongoDB, Inc provides general purpose database platform worldwide. The company offers MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premise, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

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