Month: December 2024

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MongoDB has announced a significant expansion of its MongoDB AI Applications Program (MAAP) at AWS re:Invent.

The initiative, launched earlier this year, has added global technology leaders Capgemini, Confluent, IBM, QuantumBlack, AI by McKinsey, and Unstructured to its ecosystem.

These partnerships aim to enhance the development and deployment of AI-powered solutions by providing organizations with advanced tools and integration options.

MongoDB has also partnered with Meta, focusing on integrating Meta’s AI models, including Llama, into the MAAP technology stack. The collaboration enables developers to harness Meta’s open-source tools alongside MongoDB’s capabilities, accelerating the delivery of AI applications. Future updates will include seamless mapping from MongoDB databases to LlamaStack APIs, simplifying workflows for developers.

“By joining the MAAP partner network, organizations like Capgemini and IBM bring expertise to help customers navigate the evolving AI landscape,” said Greg Maxson, Senior Director of AI GTM and Strategic Partnerships at MongoDB.

MAAP AI use cases

The MAAP initiative has already facilitated impactful AI use cases:

• CentralReach, a leader in autism and developmental disabilities care, utilized MAAP to optimize its platform by integrating over 4 billion clinical and financial data points. This advancement supports value-based care and clinical efficacy.
• IndiaDataHub, a market data analytics platform, leveraged MAAP and Meta’s AI tools to enhance sentiment analysis and data connectivity.

“Working with MongoDB and Meta has accelerated our AI strategy, helping us deliver timely, high-quality data analytics,” said Pranoti Deshmukh, CTO of IndiaDataHub.

Technical Innovations

The program’s expansion follows the recent introduction of vector quantization in MongoDB Atlas Vector Search. This feature reduces memory and storage requirements while maintaining performance, allowing developers to build scalable AI applications at reduced costs.

MongoDB’s partnerships with over 40 AI companies, including Astronomer and Arize AI, further support a diverse and interoperable ecosystem, enabling businesses to create tailored AI solutions.

Strengthening AI Ecosystems

Through its MAAP Center of Excellence, MongoDB aims to address the challenges in AI model deployment, retrieval techniques, and workflow optimization for over 150 organizations. This cross-functional collaboration underpins the company’s goal of empowering businesses with cutting-edge AI capabilities.

By expanding MAAP and aligning with industry leaders, MongoDB aims to redefine how organizations build and deploy AI-driven applications, ensuring they stay competitive in the rapidly evolving AI landscape.

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AWS has introduced the general availability of Lambda SnapStart for Python and .NET functions, a feature designed to improve the startup performance of serverless applications significantly.

Earlier, the company introduced Lambda SnapStart for Java functions to reduce cold starts. With the SnapStart for Python and .NET functions, this is now also applied for functions written in Python, C#, F#, and Powershell.

Lambda SnapStart optimizes function cold-start latency by initializing environments ahead of time and caching their memory and disk states. This cached environment is then used to resume execution, minimizing delays often caused by cold starts. Channy Yun, a principal developer advocate for AWS cloud, writes:

When you invoke the function version for the first time, and as the invocations scale up, Lambda resumes new execution environments from the cached snapshot instead of initializing them from scratch, improving startup latency.

(Source: AWS News Blog Post)

Marc Brooker, VP/Distinguished Engineer at Amazon Web Services, explains in a LinkedIn blog post:

Each Lambda function runs in one or more Firecracker-based MicroVMs, and each MicroVM has some associated state: memory, device state, CPU registers, and the like. A “snapshot” is when we tell Firecracker to store this state – writing down the memory and other state to a file on disk. This snapshot can be restored on the same physical machine, or a different machine with the same hardware configuration. Restoring is a simple matter of copying that state back into memory, back into the devices, and back into the CPU, then telling the (virtual) CPU that it can go ahead and start running.

Developers can use the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDKs to activate, update, and delete SnapStart for Python and .NET functions. They can activate Lambda functions using Python 3.12 and higher and .NET 8 and higher managed runtimes.

Yan Cui, an AWS Serverless Hero, tweeted:

Wow, SnapStart is now available for Python and .Net functions.

Interesting they didn’t do it for Node, I guess it’s not about popularity, so must be something about Node that doesn’t work well with SnapStart.

Currently, AWS Lambda SnapStart for Python and .NET functions are available in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm) AWS regions.

Lastly, with Python and .NET managed runtimes, SnapStart charges include the caching cost per published function version and restoration costs for each instance. The company recommends deleting unused function versions to lower SnapStart cache costs. Lambda’s pricing details are available on the pricing page.

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Zurcher Kantonalbank Zurich Cantonalbank raised its holdings in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) by 23.3% during the third quarter, according to the company in its most recent 13F filing with the Securities and Exchange Commission. The firm owned 15,143 shares of the company’s stock after purchasing an additional 2,858 shares during the period. Zurcher Kantonalbank Zurich Cantonalbank’s holdings in MongoDB were worth $4,094,000 as of its most recent filing with the Securities and Exchange Commission.

Other hedge funds also recently modified their holdings of the company. Jennison Associates LLC increased its stake in MongoDB by 23.6% in the third quarter. Jennison Associates LLC now owns 3,102,024 shares of the company’s stock valued at $838,632,000 after purchasing an additional 592,038 shares in the last quarter. Swedbank AB raised its stake in MongoDB by 156.3% during the 2nd quarter. Swedbank AB now owns 656,993 shares of the company’s stock worth $164,222,000 after buying an additional 400,705 shares during the period. Westfield Capital Management Co. LP lifted its holdings in MongoDB by 1.5% during the third quarter. Westfield Capital Management Co. LP now owns 496,248 shares of the company’s stock worth $134,161,000 after acquiring an additional 7,526 shares in the last quarter. Thrivent Financial for Lutherans grew its stake in MongoDB by 1,098.1% in the second quarter. Thrivent Financial for Lutherans now owns 424,402 shares of the company’s stock valued at $106,084,000 after acquiring an additional 388,979 shares during the period. Finally, Blair William & Co. IL increased its holdings in shares of MongoDB by 16.4% in the second quarter. Blair William & Co. IL now owns 315,830 shares of the company’s stock worth $78,945,000 after acquiring an additional 44,608 shares in the last quarter. Institutional investors own 89.29% of the company’s stock.

Insider Buying and Selling

In related news, CRO Cedric Pech sold 302 shares of the business’s stock in a transaction on Wednesday, October 2nd. The stock was sold at an average price of $256.25, for a total transaction of $77,387.50. Following the sale, the executive now owns 33,440 shares in the company, valued at $8,569,000. The trade was a 0.90 % decrease in their position. The sale was disclosed in a legal filing with the SEC, which is available at this hyperlink. Also, CAO Thomas Bull sold 1,000 shares of MongoDB stock in a transaction dated Monday, September 9th. The shares were sold at an average price of $282.89, for a total value of $282,890.00. Following the sale, the chief accounting officer now directly owns 16,222 shares in the company, valued at $4,589,041.58. The trade was a 5.81 % decrease in their ownership of the stock. The disclosure for this sale can be found here. In the last 90 days, insiders have sold 23,600 shares of company stock worth $6,569,819. Corporate insiders own 3.60% of the company’s stock.

MongoDB Price Performance

Shares of NASDAQ MDB traded down $1.14 during mid-day trading on Tuesday, reaching $324.01. The company had a trading volume of 917,469 shares, compared to its average volume of 1,447,469. The business’s 50-day simple moving average is $284.45 and its 200 day simple moving average is $270.10. MongoDB, Inc. has a one year low of $212.74 and a one year high of $509.62. The company has a quick ratio of 5.03, a current ratio of 5.03 and a debt-to-equity ratio of 0.84.

Wall Street Analysts Forecast Growth

MDB has been the topic of several recent analyst reports. Oppenheimer lifted their price objective on MongoDB from $300.00 to $350.00 and gave the company an “outperform” rating in a report on Friday, August 30th. Wells Fargo & Company upped their price target on shares of MongoDB from $300.00 to $350.00 and gave the company an “overweight” rating in a report on Friday, August 30th. Truist Financial raised their price objective on shares of MongoDB from $300.00 to $320.00 and gave the stock a “buy” rating in a report on Friday, August 30th. Wedbush raised MongoDB to a “strong-buy” rating in a research note on Thursday, October 17th. Finally, Morgan Stanley increased their price objective on MongoDB from $320.00 to $340.00 and gave the stock an “overweight” rating in a report on Friday, August 30th. One investment analyst has rated the stock with a sell rating, five have given a hold rating, nineteen have assigned a buy rating and one has assigned a strong buy rating to the company’s stock. According to MarketBeat, MongoDB has an average rating of “Moderate Buy” and an average target price of $343.83.

Get Our Latest Stock Analysis on MDB

About MongoDB

(Free Report)

MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides 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-premises, 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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LAS VEGAS, Dec. 2, 2024 — MongoDB, Inc. today at AWS re:Invent announced that a new cohort of organizations have joined the MongoDB AI Applications Program (MAAP) ecosystem of leading AI and tech companies. By lending their experience and expertise to MAAP, Capgemini, Confluent, IBM, QuantumBlack, AI by McKinsey, and Unstructured will offer customers additional integration and solution options, boosting the value customers receive from MAAP. Since it was launched earlier this year, MAAP has already made an impact, helping customers like CentralReach—which provides an AI-powered autism care and intellectual and developmental disabilities (IDD) platform—innovate with AI.

The MAAP Center of Excellence Team, a cross-functional group of AI experts at MongoDB, has collaborated with partners and customers across industries to overcome an array of technical challenges, empowering organizations of all sizes to build and deploy AI applications. The expansion of the MongoDB AI Applications Program follows the introduction of vector quantization to MongoDB Atlas Vector Search (which reduces vector sizes while preserving performance—at lower cost), as well as new integrations with leading AI and technology companies.

MongoDB is also collaborating with Meta on Llama to support developers in their efforts to build more efficiently and to best serve customers. Currently, both enterprise and public sector customers are leveraging Llama and MongoDB to build innovative, AI-enriched applications, accelerating progress toward business goals. In the coming months, MongoDB plans to implement turnkey mapping from its database to the LlamaStack APIs, empowering developers to deliver solutions to market more quickly and efficiently.

“At the beginning of 2024, many organizations saw the immense potential of generative AI, but were struggling to take advantage of this new, rapidly evolving technology. And 2025 is sure to bring more change—and further innovation,” said Greg Maxson, Senior Director of AI GTM and Strategic Partnerships at MongoDB. “The aim of MAAP, and of MongoDB’s collaborations with industry leaders like Meta, is to empower customers to use their data to build custom AI applications in a scalable, cost-effective way. By joining the MAAP partner network, Capgemini, Confluent, IBM, QuantumBlack, AI by McKinsey, and Unstructured are helping the program evolve to meet the ever-changing AI landscape, and offering customers an array of leading solutions.”

Launched in the summer of 2024—with founding members Accenture, Anthropic, Anyscale, Arcee AI, AWS, Cohere, Credal, Fireworks AI, Google Cloud, gravity9, LangChain, LlamaIndex, Microsoft Azure, Nomic, PeerIslands, Pureinsights, and Together AI—the MongoDB AI Applications Program is designed to help organizations unleash the power of their data and to take advantage of rapidly advancing AI technologies. It offers customers an array of resources to put AI applications into production: reference architectures and an end-to-end technology stack that includes integrations with leading technology providers, professional services, and a unified support system to help customers quickly build and deploy AI applications.

Because the AI landscape and customer expectations of AI continue to evolve, MongoDB has carefully grown the MAAP program—and the MAAP ecosystem of companies—to best meet customer needs. By working with AI industry leaders, MongoDB has gained a unique understanding of both the technology and implementation partners that can best help customers build AI applications, and has built the MAAP partner network accordingly.

New MAAP Partners Look Forward to Helping Customers Build AI Applications

A global consulting and technology services company, Capgemini offers integrated solutions for digital transformation, blending expertise with breakthrough technology. Confluent, meanwhile, is a cloud-native data streaming platform that allows users to stream, connect, process, and govern data in real time.

“Business leaders are increasingly recognizing generative AI’s value as an accelerator for driving innovation and revenue growth. But the real opportunity lies in moving from ambition to action at scale. We are pleased to continue working with MongoDB to help deliver tangible value to clients and drive competitive advantage by leveraging a trustworthy data foundation, thereby enabling gen AI at scale,” said Niraj Parihar, CEO of Insights & Data Global Business Line and Member of the Group Executive Committee at Capgemini. “MAAP helps clients build gen AI strategy, identify key use cases, and bring solutions to life, and we look forward to being a key part of this for many organizations.”

“Enterprise AI strategy is inextricably dependent upon fresh, trusted data about the business. Without real-time datasets, even the most advanced AI solutions will fail to deliver value,” said Shaun Clowes, Chief Product Officer at Confluent. “Seamlessly integrated with MongoDB and Atlas Vector Search, Confluent’s fully managed data streaming platform enables businesses to build the trusted, always-up-to-date data foundation essential for powering gen AI applications.”

Unstructured is the leading provider of ETL for LLMs, making it easy for enterprises to utilize their unstructured data with gen AI systems.

“Like MongoDB, we understand that data is essential to harnessing the power of gen AI,” said Brian Raymond, Founder and CEO of Unstructured. “We are excited to join the MongoDB AI Applications Program to bring our expertise in ingesting and preprocessing complex unstructured data for vector databases. The gen AI-ready data we continuously deliver and write to vector databases like MongoDB is essential to enabling our users to counter hallucinations, allowing the LLMs and AI projects that MAAP customers are working on to leverage sensitive, internal data while keeping models and projects up-to-date.”

Collaborating to Make an Impact with AI

Providing customers direct support from technical subject matter experts has been integral to MAAP’s success. Since the program’s inception, the MAAP Center of Excellence team—highly skilled AI experts from MAAP partners and groups across MongoDB—has worked with more than 150 organizations on a range of technical challenges, including model and technology stack evaluation, chunking strategies, advanced retrieval techniques, and the establishment of agentic workflows. Example projects include working on sound diagnostic-based maintenance recommendations for a large manufacturer, and customer service automations for companies across industries.

A recent example of how MAAP enables organizations to build with AI is IndiaDataHub, which is on a mission to build India’s largest market data and analytics platform.

Since the company’s founding, MongoDB Atlas has been the platform’s operational database for some of its key datasets, and earlier this year, IndiaDataHub joined MAAP to access AI expertise, in-depth support, and a full spectrum of technologies to enhance AI functionality within its analytics platform. This includes connecting relevant data in MongoDB with Meta’s AI models to perform sentiment analysis on text datasets.

“Data is the oil that will fuel the growth of the modern Indian economy,” said Pranoti Deshmukh, Chief Technology Officer at IndiaDataHub. “Working with MongoDB, the MAAP ecosystem, and Meta’s AI tools, we’ve been able to accelerate our AI strategy to make high-quality, timely data and analytics available to everyone in India who needs it. The professional support and deep AI expertise we’ve received through the MAAP program have been outstanding.”

“We are thrilled to see how many enterprises are leveraging our open source AI models to build better solutions for their customers and solve the problems their teams are facing everyday,” said Ragavan Srinivasan, VP of Product at Meta. “Leveraging our family of Meta models and the end-to-end technology stack offered by the MongoDB AI Applications Program demonstrates the incredible power of open source to drive innovation and collaboration across the industry.”

Another success story is CentralReach, which provides an AI-powered electronic medical record (EMR) platform that is designed to improve outcomes for children and adults diagnosed with autism and related intellectual and developmental disabilities (IDD).

Prior to working with MongoDB and MAAP, CentralReach was looking for an experienced partner to further connect and aggregate its more than 4 billion financial and clinical data points across its suite of solutions.

CentralReach leveraged MongoDB’s document model to aggregate the company’s diverse forms of information from assessments to clinical data collection, so the company could build rich AI-assisted solutions on top of its database. Meanwhile, MAAP partners helped CentralReach to design and optimize multiple layers of its comprehensive buildout. All of this will enable CentralReach to support initiatives such as value-based outcome measurement, clinical supervision, and care delivery efficacy. With these new data layers in place, providers will be able to make substantial improvements to their clinical delivery to optimize care for all those they serve.

“As a mission-driven organization, CentralReach is always looking to innovate on behalf of the clinical professionals—and the more than 350,000 autism and IDD learners—that we serve globally,” said Chris Sullens, CEO of CentralReach. “So being able to lean on MongoDBs database technology and draw on the collective expertise of the MAAP partner network—in addition to MongoDB’s tech expertise and services—to help us improve outcomes for our customers and their clients worldwide has been invaluable.”

The expansion of the MongoDB AI Applications Program builds on recent AI-related announcements from MongoDB.

In October, MongoDB announced vector quantization capabilities in MongoDB Atlas Vector Search. By reducing vector storage and memory requirements while preserving performance, these capabilities empower developers to build AI-enriched applications with more scale—and at a lower cost.

Outside of MAAP, since the start of the year MongoDB has built partnerships with more than 40 leading AI companies, enabling additional flexibility and choice for customers. Recent collaborations include those with Astronomer, Arize AI, Baseten, CloudZero, Modal, and ObjectBox. By working closely with its AI partners on product launches, integrations, and real-world challenges, MongoDB is able to bring a better understanding of AI to joint customers, deliver interoperability for end-to-end workflows, and to give them the resources and confidence they need to move forward with this groundbreaking technology.

To learn more about building AI-powered apps with MongoDB, please see MongoDB’s library of articles, tutorials, analyst reports, and white papers. And for more on the MongoDB AI Applications program, see the MAAP webpage.

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, MongoDB’s developer data platform is a database with an integrated set of related services that allow development teams to address the growing requirements for a wide variety of applications, all in a unified and consistent user experience. MongoDB has more than 50,000 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.

Source: MongoDB

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MongoDB (NASDAQ:MDB – Free Report) had its price objective raised by Loop Capital from $315.00 to $400.00 in a research report report published on Monday morning,Benzinga reports. The firm currently has a buy rating on the stock.

Other equities research analysts also recently issued research reports about the company. Wells Fargo & Company raised their price objective on MongoDB from $300.00 to $350.00 and gave the company an “overweight” rating in a research report on Friday, August 30th. Wedbush raised shares of MongoDB to a “strong-buy” rating in a report on Thursday, October 17th. Citigroup upped their price objective on shares of MongoDB from $350.00 to $400.00 and gave the company a “buy” rating in a report on Tuesday, September 3rd. UBS Group lifted their price target on MongoDB from $250.00 to $275.00 and gave the stock a “neutral” rating in a research report on Friday, August 30th. Finally, Needham & Company LLC upped their target price on MongoDB from $290.00 to $335.00 and gave the company a “buy” rating in a research report on Friday, August 30th. One investment analyst has rated the stock with a sell rating, five have assigned a hold rating, nineteen have issued a buy rating and one has issued a strong buy rating to the stock. Based on data from MarketBeat, the stock has a consensus rating of “Moderate Buy” and an average price target of $343.83.

View Our Latest Research Report on MDB

MongoDB Stock Performance

MDB stock opened at $325.15 on Monday. The company has a debt-to-equity ratio of 0.84, a current ratio of 5.03 and a quick ratio of 5.03. MongoDB has a 1-year low of $212.74 and a 1-year high of $509.62. The business has a 50-day moving average price of $284.45 and a 200 day moving average price of $270.10. The company has a market capitalization of $24.02 billion, a P/E ratio of -107.67 and a beta of 1.15.

Insiders Place Their Bets

In related news, CRO Cedric Pech sold 302 shares of the firm’s stock in a transaction dated Wednesday, October 2nd. The stock was sold at an average price of $256.25, for a total transaction of $77,387.50. Following the completion of the sale, the executive now directly owns 33,440 shares of the company’s stock, valued at approximately $8,569,000. This trade represents a 0.90 % decrease in their position. The sale was disclosed in a document filed with the Securities & Exchange Commission, which can be accessed through this hyperlink. Also, Director Dwight A. Merriman sold 3,000 shares of the business’s stock in a transaction dated Wednesday, October 2nd. The shares were sold at an average price of $256.25, for a total transaction of $768,750.00. Following the completion of the sale, the director now owns 1,131,006 shares in the company, valued at approximately $289,820,287.50. This trade represents a 0.26 % decrease in their ownership of the stock. The disclosure for this sale can be found here. Over the last 90 days, insiders have sold 23,600 shares of company stock valued at $6,569,819. Insiders own 3.60% of the company’s stock.

Institutional Investors Weigh In On MongoDB

A number of institutional investors and hedge funds have recently made changes to their positions in MDB. Atria Investments Inc grew its position in MongoDB by 1.2% in the 1st quarter. Atria Investments Inc now owns 3,259 shares of the company’s stock valued at $1,169,000 after acquiring an additional 39 shares in the last quarter. Cetera Investment Advisers increased its stake in MongoDB by 327.6% during the 1st quarter. Cetera Investment Advisers now owns 10,873 shares of the company’s stock worth $3,899,000 after buying an additional 8,330 shares during the period. Cetera Advisors LLC lifted its holdings in MongoDB by 106.9% during the 1st quarter. Cetera Advisors LLC now owns 1,558 shares of the company’s stock worth $559,000 after buying an additional 805 shares in the last quarter. Fulton Bank N.A. boosted its position in MongoDB by 7.7% in the 2nd quarter. Fulton Bank N.A. now owns 1,135 shares of the company’s stock valued at $284,000 after buying an additional 81 shares during the period. Finally, Harbor Capital Advisors Inc. grew its stake in shares of MongoDB by 26.2% in the second quarter. Harbor Capital Advisors Inc. now owns 3,579 shares of the company’s stock worth $895,000 after acquiring an additional 742 shares in the last quarter. 89.29% of the stock is owned by hedge funds and other institutional investors.

MongoDB Company Profile

(Get Free Report)

MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides 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-premises, 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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The addition of AI services to the company’s Capella cloud database and development platform will provide developers with more control over data, development workflows and AI models.

Couchbase is adding new artificial intelligence capabilities to its Capella cloud database that the company says will streamline the development of agentic AI applications.

The new Capella AI Services, unveiled Monday, provide developers with simplified data integration workflows and more control over data throughout the development lifecycle including putting agentic applications into production, according to Couchbase.

The AI services also help developers mitigate data security and privacy risks by keeping data and large language models – including LLMs running outside of an organization – close together.

[Related: Meeting The Exploding Demand For Data: The 2024 CRN Big Data 100]

With the new Capella AI Services, Couchbase, based in Santa Clara, Calif., is looking to provide data management capabilities needed for the growing wave of AI and generative AI applications development.

“This release is all about our offering, really targeted at developers building AI and agentic or multi agentic applications,” said Matt McDonough, Couchbase senior vice president of product and partners, in an interview with CRN. “There’s a lot of enthusiasm around AI agents, but the industry as a whole lacks well-defined best practices for building and deploying these agentic applications.”

The new AI services come on the heels of Couchbase’s September announcement of expanded columnar and vector search functionality in the Capella database-as-a-service for developing next-generation adaptive applications – including those with AI functionalities. Capella is based on Couchbase’s NoSQL database server.

“The key is [to] make it simple for developers to build, test and deploy AI agents without having to use disparate platforms,” McDonough said. “And do this in a way that reduces latency, operational costs [and] keeping models and data close together as part of this whole agentic AI software development life cycle.”

The new AI services are also an enabler for Retrieval-Augmented Generation workflows that move proprietary data into LLMs, according to the executive.

The new AI services include model hosting, automated vectorization, unstructured data preprocessing and AI agent catalog services – all of which allow developers to prototype, build, test and deploy AI agents. In addition to keeping models and data close together, the services help organizations reduce development complexity, avoid excess latencies and high operational costs often experienced when introducing new technology components and workflows, according to Couchbase.

“The greatest strength of AI is its ability to process unstructured data,” McDonough said. AI agents can take unstructured information, such as a transcription of a meeting, and autonomously incorporate it into operational applications and workflows. But AI agents need flexible databases with the ability to work with complex data types and unstructured data – such as PDF documents and audio files – to be effective, he said.

Couchbase ISV and systems integrator partners will particularly benefit from the new AI Services, McDonough said. ISV partners who develop their applications on the Capella platform can better meet customer requests to add AI agent capabilities to those applications. And global and regional systems integrators can use the new functionality to expand the range of development services they can provide their customers, he said.

The new Capella AI Services include:

• Model services that provide managed endpoints for leading LLMs and embedding models, and capabilities such as prompt and conversation caching, guardrails and keyword filtering to support RAG and agentic workflows.
• Unstructured data services that extract, clean, chunk and transform unstructured documents into JSON, preparing them for vectorization. It also extracts structured information from complex documents and makes it queryable in Capella.
• Vectorization services that automate vectorization and indexing of data stored in Capella.
• AI agent catalog services that accelerate agentic application development by providing a central repository for tools, metadata, prompts and audit information for LLM flow, traceability and governance.
• Capella AI functions that enable AI-driven data analysis directly into application workflows using familiar SQL++ syntax.

The AI services are currently in private preview and are slated to be generally available as part of the Capella cloud database in 2025.

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Datadog has announced its latest advancements in monitoring capabilities for Amazon Web Services (AWS) and database technologies, expanding support and integration for a variety of platforms.

Datadog, known for its monitoring and security platform tailored for cloud applications, has strengthened its investment in AWS monitoring. It now offers over 100 AWS service integrations, including tools for AI/ML applications, as well as serverless and containerised environments. Prominent companies such as AppFolio, andsafe, Asana, Maersk, Cash App, Sweetgreen, The PlayStation Network, and Twilio leverage Datadog for monitoring their AWS environments.

Addressing the ongoing trend of enterprises adopting AI/ML, cloud migration, and serverless technologies, Yanbing Li, Chief Product Officer at Datadog, stated, “We continue to see companies rely on Datadog for enterprise-scale observability at an accelerated rate. Trends like AI/ML, cloud migration, serverless and containers – and the need to monitor and optimise resources for all these areas – have helped to accelerate this growth as companies search to better understand their LLM usage, infrastructure performance and cloud costs.”

The expanded AWS integrations include AWS Trainium and AWS Inferentia for ML performance monitoring, Amazon Q for integrated querying in AWS Management Console, Amazon Bedrock for AI model monitoring, and Amazon SageMaker for metrics visualisation.

Kyle Triplett, VP of Product at AppFolio, commented on the benefits experienced with Datadog’s capabilities: “The Datadog LLM Observability solution helps our team understand, debug and evaluate the usage and performance of our GenAI applications. With it, we are able to address real-world issues, including monitoring response quality to prevent negative interactions and performance degradations, while ensuring we are providing our end users with positive experiences.”

James Adams, Machine Learning Engineering Manager at Cash App, shared insights on their usage of AWS and Datadog: “We explored a bunch of different hosted solutions and found that SageMaker solved all the problems that we were encountering. And we did some stress testing with it and it held up to the traffic that we expected to be sending through the system. With Datadog, it has all these AI integrations—including SageMaker—that we’re using heavily.”

Marcel Drechsler, Senior Cloud Solutions Engineer at andsafe, highlighted the efficiency gains from Datadog’s tools: “andsafe has been all in on Amazon Web Services since day one and our infrastructure is based on microservices which are running on Amazon EKS. To monitor the resource consumption, we are utilising the container monitoring tools of Datadog. As a result, we were able to decrease the resource consumption and make the process much faster.”

On the database management front, Datadog has unveiled new capabilities for monitoring MongoDB, thus supporting the five most popular database types, including MongoDB, Postgres, MySQL, SQL Server, and Oracle. This expansion aims to provide comprehensive observability, enabling users to troubleshoot and optimise queries, ensuring high availability and efficiency across database environments.

Omri Sass, Director of Product Management at Datadog, emphasised the importance of robust database monitoring, saying, “Replication failures or misconfigurations can result in significant downtime and data inconsistencies for companies, which may impact their application performance and reliability. That’s why maintaining high availability across clusters with multiple nodes and replicas is critical. With support for the top five database types in the industry, Datadog Database Monitoring gives teams complete visibility into their databases, queries and clusters so that they can maintain performant databases and tie them to the health of their applications and success of their businesses.”

Will Winn, Senior Director of Partners at MongoDB, added, “As enterprises take advantage of today’s increasingly data-intensive workloads, it’s critical that they have the tools needed to deploy high-performing applications with complete confidence. Customers trust MongoDB for its superior performance and flexibility, and now that Datadog Database Monitoring supports MongoDB, ensuring high availability and seamless performance of MongoDB database clusters is even easier.”

Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

Database Software Market Share

The Business Research Company recently released a comprehensive report on the Global Database Software Market Size and Trends Analysis with Forecast 2024-2033. This latest market research report offers a wealth of valuable insights and data, including global market size, regional shares, and competitor market share. Additionally, it covers current trends, future opportunities, and essential data for success in the industry.

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According to The Business Research Company’s, The database software market size has grown rapidly in recent years. It will grow from $162.25 billion in 2023 to $186.97 billion in 2024 at a compound annual growth rate (CAGR) of 15.2%. The growth in the historic period can be attributed to technological development, digitization, economic growth, and increased simplicity of software.

The database software market size is expected to see rapid growth in the next few years. It will grow to $332.77 billion in 2028 at a compound annual growth rate (CAGR) of 15.5%. The growth in the forecast period can be attributed to increase in amount of data generated and increase in data driven businesses. Major trends in the forecast period include investing in security measures to counter cyber threats, adding a mobile-friendly version to the product list, offering the products and solutions on a subscription or pay-per-usage payment models, and investing in htap systems.

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Market Drivers and Trends:

Businesses that engage with various types of data for analysis and making future decisions in today’s data-driven world must be able to store data without difficulties and with simplicity. The rise of data-driven firms and e-commerce companies will significantly drive and fuel the database software market on a global basis. For instance, according to Forrester, businesses that rely on database management software tools to make decisions are 58% more likely to beat their revenue goals than non-data driven companies. Therefore, this continued increase in data driven businesses drives the database software market.

With Big Data gaining traction, database software companies are focusing on products to store and process Big Data. Databases such as SQL or NoSQL are tools to store, process and analyze Big Data. Efficient processing and storing of big data enable organizations to gather key insights and patterns from the available data and helps them in making intelligent decisions based on the same. Database software companies are therefore investing in adding new features to enable faster and more effective management of huge volumes of data. Some of the major database software tools include AWS DynamoDB, Azure Cosmos DB, and Amazon DocumentDB.

Key Benefits for Stakeholders:

• Comprehensive Market Insights: Stakeholders gain access to detailed market statistics, trends, and analyses that help them understand the current and future landscape of their industry.
• Informed Decision-Making: The reports provide crucial data that support strategic decisions, reducing risks and enhancing business planning.
• Competitive Advantage: With in-depth competitor analysis and market share information, stakeholders can identify opportunities to outperform their competition.
• Tailored Solutions: The Business Research Company offers customized reports that address specific needs, ensuring stakeholders receive relevant and actionable insights.
• Global Perspective: The reports cover various regions and markets, providing a broad view that helps stakeholders expand and operate successfully on a global scale.

Major Key Players of the Market:

Oracle Corporation, Microsoft Corporation, IBM, SAP SE, Amazon.com Inc., Teradata Corporation, MongoDB, Mark Logic, Couch Base, Redis Labs Ltd., Fujitsu, Amazon Web Services (AWS), Rocket Software, Pivotal, Software AG, PingCAP, Huawei, Datastax, InterSystems, SQLite, MariaDB, ScienceSoft, AI Software, SoftwareONE, Cyber Infrastructure Inc., 2Base Technologies Pvt. Ltd, Webtonic Solutions, Cygnet Infotech LLC

Database Software Market 2024 Key Insights:

• The database software market size is expected to see rapid growth in the next few years. It will grow to $332.77 billion in 2028 at a compound annual growth rate (CAGR) of 15.5%.
• Data-Driven Businesses Fueling The Growth Of The Database Software Market
• Database Software Companies Adapt To The Big Data Era With Enhanced Solutions
• North America was the largest region in the database software market in 2023

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The Business Research Company (www.thebusinessresearchcompany.com) is a leading market intelligence firm renowned for its expertise in company, market, and consumer research. With a global presence, TBRC’s consultants specialize in diverse industries such as manufacturing, healthcare, financial services, chemicals, and technology, providing unparalleled insights and strategic guidance to clients worldwide.

This release was published on openPR.

Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

The most recent trading session ended with MongoDB (MDB) standing at $325.15, reflecting a +0.82% shift from the previouse trading day’s closing. This move outpaced the S&P 500’s daily gain of 0.25%. Meanwhile, the Dow lost 0.29%, and the Nasdaq, a tech-heavy index, added 0.97%.

Coming into today, shares of the database platform had gained 18.63% in the past month. In that same time, the Computer and Technology sector gained 0.06%, while the S&P 500 gained 3.51%.

The investment community will be closely monitoring the performance of MongoDB in its forthcoming earnings report. The company is scheduled to release its earnings on December 9, 2024. On that day, MongoDB is projected to report earnings of $0.69 per share, which would represent a year-over-year decline of 28.13%. Our most recent consensus estimate is calling for quarterly revenue of $495.48 million, up 14.44% from the year-ago period.

For the entire fiscal year, the Zacks Consensus Estimates are projecting earnings of $2.43 per share and a revenue of $1.93 billion, representing changes of -27.03% and +14.52%, respectively, from the prior year.

Investors should also take note of any recent adjustments to analyst estimates for MongoDB. These latest adjustments often mirror the shifting dynamics of short-term business patterns. As a result, upbeat changes in estimates indicate analysts’ favorable outlook on the company’s business health and profitability.

Based on our research, we believe these estimate revisions are directly related to near-team stock moves. To benefit from this, we have developed the Zacks Rank, a proprietary model which takes these estimate changes into account and provides an actionable rating system.

The Zacks Rank system, spanning from #1 (Strong Buy) to #5 (Strong Sell), boasts an impressive track record of outperformance, audited externally, with #1 ranked stocks yielding an average annual return of +25% since 1988. Over the past month, the Zacks Consensus EPS estimate has moved 0.68% higher. Right now, MongoDB possesses a Zacks Rank of #3 (Hold).

Looking at valuation, MongoDB is presently trading at a Forward P/E ratio of 132.53. This represents a premium compared to its industry’s average Forward P/E of 34.47.

One should further note that MDB currently holds a PEG ratio of 12.74. The PEG ratio is akin to the commonly utilized P/E ratio, but this measure also incorporates the company’s anticipated earnings growth rate. As the market closed yesterday, the Internet – Software industry was having an average PEG ratio of 2.46.

Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

Datadog has expanded its Database Monitoring platform to include MongoDB database observability.

The addition of MongoDB to Datadog’s monitoring capabilities means it now supports the top five database types, alongside Postgres, MySQL, SQL Server, and Oracle.

Datadog’s Director of Product Management, Omri Sass, explained the importance of this development: “With support for the top five database types in the industry, Datadog Database Monitoring gives teams complete visibility into their databases, queries and clusters so that they can maintain performant databases and tie them to the health of their applications and success of their businesses.”

Traditional monitoring tools often limit organisations to monitor either databases or applications.

This can lead to inefficiencies in troubleshooting, prolonged downtimes, and compromised customer experiences. Datadog Database Monitoring offers a solution by allowing application developers and database administrators to monitor inefficient queries consistently and efficiently across various database environments.

The tool provides insights into database loads, identifies long-running and blocking queries, and offers specific execution details to optimise database and query performance, helping prevent costly incidents.

Omri Sass emphasised the critical nature of high availability across database clusters: “Replication failures or misconfigurations can result in significant downtime and data inconsistencies for companies, which may impact their application performance and reliability.”

“That’s why maintaining high availability across clusters with multiple nodes and replicas is critical.”

Datadog Database Monitoring aims to ensure high availability by offering comprehensive data on clusters and critical metrics, such as queries per second and replication details.

This allows users to detect possible issues proactively and take preventative actions.

The platform also optimises query and database performance by tracking key performance metrics like latency and execution time. These insights can help detect long transactions and missing indices, providing recommendations to resolve such issues.

By integrating database and application performance metrics, Datadog accelerates root cause analysis, contributing to faster issue resolution.

With MongoDB’s modern document model, the new support from Datadog aids users in optimising their deployments by analysing resource usage more effectively.

Will Winn, Senior Director of Partners at MongoDB, commented on the integration: “As enterprises take advantage of today’s increasingly data-intensive workloads, it’s critical that they have the tools needed to deploy high-performing applications with complete confidence.

Customers trust MongoDB for its superior performance and flexibility, and now that Datadog Database Monitoring supports MongoDB, ensuring high availability and seamless performance of MongoDB database clusters is even easier.”

Datadog’s addition of MongoDB monitoring is now available to users.

The development underpins the commitment to enhance database performance and operational efficiency across various platforms, addressing the complex needs of businesses managing data-intensive workloads.

Article originally posted on mongodb google news. Visit mongodb google news