Month: June 2023

Posted on mongodb google news. Visit mongodb google news

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

Posted on mongodb google news. Visit mongodb google news

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

Posted on mongodb google news. Visit mongodb google news

At its MongoDB.local NYC event, MongoDB today announced a slew of product releases and updates. Given the company’s focus on its fully managed Atlas service, it’s no surprise that the majority of news focuses on that platform, with improved support for AI and semantic search workloads, dedicated search nodes to better enable search use cases and new capabilities to process streaming data, among others.

Andrew Davidson, MongoDB’s SVP of product, told me that this is a continuation of the work the company has been doing on Atlas in recent years. “With Atlas, we can deliver capabilities much more quickly,” he said. “We’re able to add the power of search and time series and drive a wider variety of workload shapes.” He argues that as businesses are forced to do more with fewer resources — all while developers are expected to build more applications and do so faster — expanding Atlas’ capabilities is a natural evolution for MongoDB. “We think that this is totally our moment, because we come in with our developer data platform vision, saying: we want to enable a builder to express the vast majority of the features in the vast majority of their applications with respect to their operational data needs. That’s why we keep investing in all of these key primitives and capabilities,” he explained.

Vector search is maybe the most obvious example here. For companies that want to use large language models (LLMs), translating their data into vectors and storing them is key to customizing foundation models for their needs. In addition, vector search also enables new workloads on Atlas, like text-to-image search, for example. “We think that, of course, a developer data platform that specializes in operational data should also be able to then express indexes that let you efficiently query the vector summaries of that data,” said Davidson.

Likewise, stream processing is a capability that hasn’t traditionally been the focus of MongoDB’s document model. For a while now, MongoDB has been offering its Aggregation Framework, which allows developers to perform transformations on a stream of documents that comes out of a database. “We realized, ‘holy moly, that’s a perfect metaphor for being able to conceptualize transformations on a stream coming off Kafka,’” Davidson explained.

Another new feature here is support for querying data in Microsoft Azure Blob Storage with MongoDB Atlas Online Archive and Atlas Data Federation. MongoDB previously launched support for AWS. While MongoDB would obviously prefer it if everybody hosted their data in MongoDB, the reality is that most enterprises will continue to use multiple systems. Atlas Data Federation makes it easy for developers to read and write data from and to Atlas databases and third-party cloud object stores, which then makes it easier for them to generate and combine data streams from multiple data sources to power their applications.

Some of the other new features MongoDB is launching this week include Atlas Search Nodes, which are dedicated nodes for scaling search workloads independent of the database, as well as improvements to how the database handles enterprise-scale time series workloads.

“The new MongoDB Atlas capabilities announced today are in response to the feedback we get from customers all around the world — they love that their teams are able to quickly build and innovate with MongoDB Atlas and want to be able to do even more with it across the enterprise,” said Dev Ittycheria, MongoDB’s president and CEO. “With the new features we’re launching today, we’re further supporting not only customers who are just getting started, but also customers who have the most demanding requirements for functionality, performance, scale and flexibility so they can unleash the power of software and data to build advanced applications to transform their businesses.”

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

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Copyright 2023 PR Newswire. All Rights Reserved
2023-06-22

HONG KONG, June 22, 2023 /PRNewswire/ — Get ready to dive into an extraordinary aquatic experience as SEAKOOL announces its highly-anticipated launch of the D2 Underwater Thruster and P1 Paddleboard Thruster on Indiegogo.

SEAKOOL is a thrilling brand born from HighGreat, the innovative drone manufacturer behind four Guinness world records. With a global reach spanning 300+ cities, HighGreat’s expertise in executing over 5,000 drone formations shines through in our exceptional watersport offering on Indiegogo.

Harnessing cutting-edge technology and a passion for oceanic adventure, SEAKOOL is set to make waves in the industry. From D2’s hands-free experience, and ergonomic dual-propellers of the powerful modular design to the multifunctional P1 that is built to explore what has been impossible with current products on the market. SEAKOOL water gear is meticulously crafted to deliver an adrenaline-pumping experience like no other.

This Indiegogo project aims to bring unprecedented experience, world-record speed, and versatility to water enthusiasts worldwide. The shipping will start on July 2, 2023.

“We are thrilled to embark on this remarkable journey with Indiegogo and introduce the world to our groundbreaking products for watersport lovers,” said SEAKOOL development team. “We poured countless hours of research and development into creating D2 and P1, reinventing the conventional experience into something truly extraordinary. We can’t wait to share it with the Indiegogo community.”

With its official Indiegogo launch just around the corner, SEAKOOL invites all water lovers, thrill-seekers, and adventure enthusiasts to join the project and be part of the movement. Backers can expect exclusive early-bird discounts, limited offerings, and the opportunity to be among the first to experience the exhilaration of SEAKOOL’s water sports gear.

To find out more information about SEAKOOL and its gear, visit SEAKOOL Underwater Thruster on Indiegogo.

About SEAKOOL:

We specialize in groundbreaking propulsion devices that elevate your aquatic adventures. From D2’s dual-propellers to P1’s electric paddleboarding capabilities, our products redefine underwater exploration. Join our community, push the limits, and experience the wonders of the aquatic world like never before.

Photo – https://mobilemonitoringsolutions.com/wp-content/uploads/2023/06/World_s_First_Hands_Free_Underwater_Thrusters_HighGreat_SEAKOOL.jpg 

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

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MongoDB (NASDAQ:MDB) shares rose more than 5% on Thursday after the database company unveiled an expanded partnership with Google’s (GOOG) (GOOGL) cloud unit and several new products and features geared towards its Atlas platform.

MongoDB (MDB) said the expanded deal with Google Cloud will help accelerate the use of generative artificial intelligence and result in the building of new gen-AI applications with expert assistance and professional services via Google (GOOG) (GOOGL) Cloud’s Vertex AI large language models.

“This new initiative from Google Cloud and MongoDB will bring more capabilities, support, and resources to developers building the next generation of generative AI applications,” Kevin Ichhpurani, Corporate Vice President, Global Ecosystem and Channels at Google Cloud said in a statement.

In addition, MongoDB (MDB) made several other announcements at its New York developer conference on Thursday, including the general availability of its app migration tool MongoDB Relational Migrator.

MongoDB (MDB) also said it is adding new four capabilities for developers, including programming language support for resources on Amazon Web Services using infrastructure-as-code.

The company also announced a new program known as MongoDB Atlas for Industries, which lets companies accelerate their cloud adoption and modernization programs, along with an AI innovator program to help with generative AI and five new capabilities for MongoDB Atlas.

More on MongoDB

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MongoDB has been a developer favourite for the longest time. The king of NoSQL database has added Vector Search, which greatly streamlines the integration of generative AI and semantic search into applications. 

MongoDB today announced a suite of new announcements. This includes Google Cloud and MongoDB collaboration on an AI initiative, alongside the introduction of the AI Innovators Programme. As part of this collaboration, they have developed various new offerings for MongoDB and its data developer platform MongoDB Atlas. Additionally, MongoDB has unveiled MongoDB Atlas specifically tailored for industries and financial services. Furthermore, the relational migrator is now widely accessible to everyone.

Read more: The good, the bad and the ugly – the story of MongoDB

Fuelling Generative AI with MongoDB Atlas

The process of converting data into vectors allows for effective clustering and semantic search operations. Similar data points are closer to each other in vector space, facilitating meaningful associations and comparisons.

“MongoDB’s vector search capabilities provide a foundation for generative AI applications by converting diverse data types, such as text, images, videos, and audio files, into numerical vectors. This simplifies AI processing and enables efficient searches based on relevance,” said Himanshumali, principal solutions architect, in an exclusive interaction with AIM.

The generative AI capabilities with MongoDB Atlas Vector Search enables precise information retrieval and personalisation. Moreover, MongoDB Atlas Search Nodes offer dedicated resources for enterprise-scale search workloads. 

Traditionally, when customers use different search engines, there are challenges in synchronising the source database with the search engine, leading to architectural complexity and time delays. 

However, with Atlas, search is part of the same platform, eliminating the need to move data to a different platform. “All search operations can be performed directly on Atlas in real time, providing customers with a seamless search experience,” he added.

“Developers can now lleverage familiar MongoDB query language to perform semantic queries without the need for specialised vector databases or language acquisition,” said Himanshumali.

MongoDB Atlas Stream Processing facilitates the handling of high-velocity streams of intricate data. Streaming data, which is continuously generated and evolving, is crucial for real-time use cases such as personalisation, fraud detection, IoT, and route planning. 

The lifecycle of streaming data involves a source where the data is generated, analytics on the streaming data for insights, and a persistent database to store the data. This integration of data at rest and data in motion provides an elegant and efficient experience for developers, enabling multiple real-time use cases.

Lastly, the introduction of MongoDB Atlas Data Federation empowers users to query data and isolate workloads on Microsoft Azure. Collectively, these innovations empower businesses to optimize operational efficiency and accelerate innovation by consolidating various workloads on a unified developer data platform throughout the organisation.

Additionally, MongoDB has unveiled MongoDB Atlas specifically tailored for industries and financial services. This offering aims to provide resilience, scalability, data privacy, and compliance solutions tailored to the requirements of the financial industry. 

“We have a strong customer base in the financial segment, including a significant number of top banks in North America, supporting over 150,000 transactions per second,” said Himanshumali. 

Harnessing the Best of Google

MongoDB and Google Cloud have joined forces to enhance the adoption of generative AI and facilitate the creation of innovative applications. By leveraging its integrated operational data store, MongoDB Atlas uniquely supports the development of generative AI-powered applications with increased efficiency and simplicity. 

Talking about why Google Cloud is over other potential cloud providers, Himanshumali mentions that Google Cloud’s capabilities for generative AI, which they integrated into the partnership. So developers can now utilise MongoDB Atlas along with Google Cloud’s Vertex AI and access professional services for expedited software development, including quick-start architecture reviews.

Say Goodbye to Relational Database Complexities

Data forms the crux of everything. The buzz around generative AI would have not been there had it not been for these databases that form the heart and soul of the large language models. While we have Redis, Apache Cassandra as the NoSQL heroes, Neo4j, OrientDB is adding to the text-to-anything bandwagon with graph technology.

While graph databases are more suitable for applications that necessitate complex queries about relationships between entities, whereas vector databases are more appropriate for applications that demand similarity searches. Nevertheless, there are instances where both types of databases can be advantageous. 

Take, for instance, a social network which could use a graph database to store user relationships and a vector database to store user characteristics. By doing so, the social network would be able to execute both complex relationship queries and similarity searches.

“Relational databases come with their own set of limitations from the complexities that arise when using multiple niche databases,” said Himanshumali. To solve this, MongoDB has now made its Relational Migrator available for public use.

The migrator identifies workloads, updates schemas, modernizes application code, and migrates data from various relational databases. It supports Oracle, SQL, and Postgres databases and utilises AI to generate code changes, streamlining the development process. 

Accenture, Capgemini, Nationwide Building Society, and Powerledger are among the notable customers and partners already benefiting from MongoDB Relational Migrator. 

Read more: Neo4j’s Role in Fueling Generative AI with Graph Technology 

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

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Expanded partnership between MongoDB and Google Cloud enables developers to use state-of-the-art AI foundation models from Google to build new classes of generative AI applications with expert assistance and professional services

NEW YORK, June 22, 2023 /PRNewswire/ — MongoDB, Inc. (NASDAQ: MDB) today at its developer conference MongoDB.local NYC announced a new initiative in partnership with Google Cloud to help developers accelerate the use of generative AI and build new classes of applications. MongoDB Atlas is the multi-cloud developer data platform used by tens of thousands of customers and millions of developers globally to quickly build and scale applications using a single platform. With an integrated operational data store at its core, MongoDB Atlas is uniquely positioned to help organizations of all shapes and sizes build applications powered by generative AI faster and with less complexity. Developers can now take advantage of MongoDB Atlas and industry-leading partner integrations with Google Cloud’s Vertex AI large language models (LLMs) and new quick-start architecture reviews with MongoDB and Google Cloud professional services to accelerate software development. To get started with MongoDB Atlas on Google Cloud, visit mongodb.com/products/platform/atlas-cloud-providers/google-cloud.

Recent advancements in generative AI technology like LLMs present opportunities to reimagine how end users interact with applications. Developers want to take advantage of generative AI to unlock their creativity and build new classes of applications, but many current solutions require piecing together several different technologies and components or bolting on solutions to existing technology stacks, making software development cumbersome, complex, and expensive. MongoDB and Google Cloud are helping address these challenges by providing a growing set of solutions and integrations to meet developers where they are and enable them to quickly get started building applications that take advantage of new AI technologies:

• Seamlessly use Google Cloud’s Vertex AI foundation models with MongoDB Atlas Vector Search: MongoDB and Google Cloud have partnered since 2018 and have helped thousands of joint customers—including Keller Williams, Rent the Runway, and Ulta—adopt cloud-native data strategies. Earlier this year, that partnership was expanded to include deeper product integrations. Now, developers can use MongoDB Atlas Vector Search with Vertex AI to seamlessly build applications with AI-powered capabilities for highly personalized and engaging end-user experiences. Vertex AI provides the text embedding API to generate embeddings from customer data stored in MongoDB Atlas, combined with the PaLM text models to create advanced functionality like semantic search, classification, outlier detection, AI-powered chatbots, and text summarization. To learn more about building applications on MongoDB Atlas with Vertex AI, visit mongodb.folloze.com/google-cloud-ai.
• Build AI applications faster with hands-on assistance from experts: From ideation to execution, the MongoDB and Google Cloud professional services teams can help rapidly prototype applications by providing expertise on data schema and indexing design, query structuring, and fine-tuning AI models to build a strong foundation for applications. The Vertex AI platform caters to the full range of AI use cases from advanced AI and data science practitioners with end-to-end AI/ML pipelines to business users who can create out-of-the-box experiences leveraging the foundational models to generate content for language, image, speech, and code. Developers can also tune models to further improve the performance of the model for specific tasks. Google Cloud and MongoDB are working closely together under the Built with Google Cloud AI program to make these experiences even more seamless with Google’s Generative AI capabilities built right into MongoDB Atlas. When applications are ready for production, the MongoDB and Google Cloud professional services teams can optimize applications for performance and help solve future problems through quick iteration to get new features into production more quickly. To get started, visit mongodb.com/products/consulting.

“With the shift in technology powered by generative AI taking place today, the future of software and data is now, and we’re making it more evenly distributed for developers with MongoDB Atlas,” said Alan Chhabra, Executive Vice President of Worldwide Partnerships at MongoDB. “This shift begins with developers, and we want to democratize access to game-changing technology so all developers can build the next big thing. With MongoDB Atlas and our strategic partnership with Google Cloud, it’s now easier for organizations of all shapes and sizes to incorporate AI into their applications and embrace the future.”

“Generative AI represents a significant opportunity for developers to create new applications and experiences and to add real business value for customers,” said Kevin Ichhpurani, Corporate Vice President, Global Ecosystem and Channels at Google Cloud. “This new initiative from Google Cloud and MongoDB will bring more capabilities, support, and resources to developers building the next generation of generative AI applications.”

One AI enables businesses to deploy the world’s best language AI capabilities, tuned to their unique needs, in days. “We needed a platform that allowed us to build agile, data-driven, and scalable software, and MongoDB Atlas on Google Cloud was the obvious choice,” said Amit Ben, Founder & CEO at One AI. “The flexibility and scalable document data model MongoDB Atlas provides enabled us to do hundreds of small incremental changes and additions to our platform in the last year alone, allowing rapid development and quick adaptation to shifting and growing needs with zero effort and no need for any migration or compatibility issues. We are looking forward to the additional support MongoDB and Google Cloud will provide as we grow and scale our company.”

MongoDB Developer Data Platform

MongoDB Atlas is the leading multi-cloud developer data platform that accelerates and simplifies building with data. MongoDB Atlas provides an integrated set of data and application services in a unified environment to enable developer teams to quickly build with the capabilities, performance, and scale modern applications require.

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 new capabilities for MongoDB Atlas to build new classes of applications. 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.

Media Relations

MongoDB

press@mongodb.com

View original content to download multimedia:https://www.prnewswire.com/news-releases/mongodb-launches-ai-initiative-with-google-cloud-to-help-developers-build-ai-powered-applications-301857950.html

SOURCE MongoDB, Inc.

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

Posted on mongodb google news. Visit mongodb google news

Expanded partnership between MongoDB and Google Cloud enables developers to use state-of-the-art AI foundation models from Google to build new classes of generative AI applications with expert assistance and professional services 

NEW YORK, June 22, 2023 /PRNewswire/ — MongoDB, Inc. (NASDAQ: MDB) today at its developer conference MongoDB.local NYC announced a new initiative in partnership with Google Cloud to help developers accelerate the use of generative AI and build new classes of applications. MongoDB Atlas is the multi-cloud developer data platform used by tens of thousands of customers and millions of developers globally to quickly build and scale applications using a single platform. With an integrated operational data store at its core, MongoDB Atlas is uniquely positioned to help organizations of all shapes and sizes build applications powered by generative AI faster and with less complexity. Developers can now take advantage of MongoDB Atlas and industry-leading partner integrations with Google Cloud’s Vertex AI large language models (LLMs) and new quick-start architecture reviews with MongoDB and Google Cloud professional services to accelerate software development. To get started with MongoDB Atlas on Google Cloud, visit mongodb.com/products/platform/atlas-cloud-providers/google-cloud.

Recent advancements in generative AI technology like LLMs present opportunities to reimagine how end users interact with applications. Developers want to take advantage of generative AI to unlock their creativity and build new classes of applications, but many current solutions require piecing together several different technologies and components or bolting on solutions to existing technology stacks, making software development cumbersome, complex, and expensive. MongoDB and Google Cloud are helping address these challenges by providing a growing set of solutions and integrations to meet developers where they are and enable them to quickly get started building applications that take advantage of new AI technologies:

• Seamlessly use Google Cloud’s Vertex AI foundation models with MongoDB Atlas Vector Search: MongoDB and Google Cloud have partnered since 2018 and have helped thousands of joint customers—including Keller Williams, Rent the Runway, and Ulta—adopt cloud-native data strategies. Earlier this year, that partnership was expanded to include deeper product integrations. Now, developers can use MongoDB Atlas Vector Search with Vertex AI to seamlessly build applications with AI-powered capabilities for highly personalized and engaging end-user experiences. Vertex AI provides the text embedding API to generate embeddings from customer data stored in MongoDB Atlas, combined with the PaLM text models to create advanced functionality like semantic search, classification, outlier detection, AI-powered chatbots, and text summarization. To learn more about building applications on MongoDB Atlas with Vertex AI, visit mongodb.folloze.com/google-cloud-ai.

• Build AI applications faster with hands-on assistance from experts: From ideation to execution, the MongoDB and Google Cloud professional services teams can help rapidly prototype applications by providing expertise on data schema and indexing design, query structuring, and fine-tuning AI models to build a strong foundation for applications. The Vertex AI platform caters to the full range of AI use cases from advanced AI and data science practitioners with end-to-end AI/ML pipelines to business users who can create out-of-the-box experiences leveraging the foundational models to generate content for language, image, speech, and code. Developers can also tune models to further improve the performance of the model for specific tasks. Google Cloud and MongoDB are working closely together under the Built with Google Cloud AI program to make these experiences even more seamless with Google’s Generative AI capabilities built right into MongoDB Atlas. When applications are ready for production, the MongoDB and Google Cloud professional services teams can optimize applications for performance and help solve future problems through quick iteration to get new features into production more quickly. To get started, visit mongodb.com/products/consulting.

“With the shift in technology powered by generative AI taking place today, the future of software and data is now, and we’re making it more evenly distributed for developers with MongoDB Atlas,” said Alan Chhabra, Executive Vice President of Worldwide Partnerships at MongoDB. “This shift begins with developers, and we want to democratize access to game-changing technology so all developers can build the next big thing. With MongoDB Atlas and our strategic partnership with Google Cloud, it’s now easier for organizations of all shapes and sizes to incorporate AI into their applications and embrace the future.”

“Generative AI represents a significant opportunity for developers to create new applications and experiences and to add real business value for customers,” said Kevin Ichhpurani, Corporate Vice President, Global Ecosystem and Channels at Google Cloud. “This new initiative from Google Cloud and MongoDB will bring more capabilities, support, and resources to developers building the next generation of generative AI applications.”

One AI enables businesses to deploy the world’s best language AI capabilities, tuned to their unique needs, in days. “We needed a platform that allowed us to build agile, data-driven, and scalable software, and MongoDB Atlas on Google Cloud was the obvious choice,” said Amit Ben, Founder & CEO at One AI. “The flexibility and scalable document data model MongoDB Atlas provides enabled us to do hundreds of small incremental changes and additions to our platform in the last year alone, allowing rapid development and quick adaptation to shifting and growing needs with zero effort and no need for any migration or compatibility issues. We are looking forward to the additional support MongoDB and Google Cloud will provide as we grow and scale our company.”

MongoDB Developer Data Platform
MongoDB Atlas is the leading multi-cloud developer data platform that accelerates and simplifies building with data. MongoDB Atlas provides an integrated set of data and application services in a unified environment to enable developer teams to quickly build with the capabilities, performance, and scale modern applications require.

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 new capabilities for MongoDB Atlas to build new classes of applications. 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.

Media Relations
MongoDB
press@mongodb.com

View original content to download multimedia:https://www.prnewswire.com/news-releases/mongodb-launches-ai-initiative-with-google-cloud-to-help-developers-build-ai-powered-applications-301857950.html

SOURCE MongoDB, Inc.

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

Posted on mongodb google news. Visit mongodb google news

MongoDB Relational Migrator is GA and more, from Adrian Bridgwater in NY.

Data grows up. More specifically, our application structures extend over time and become more powerfully complex in line with the general weight of evolution in the data platforms that serve them. This core truism of course means that enterprises sometimes need to migrate their data to new databases, new data services, new data automation tools and new clouds.

But as we all know, growing up isn’t easy.

The growing pains experienced in data migration aren’t so much a question of adolescent angst (although there are a lot of reasons to worry about fragility, acceptance and usefulness), the challenges are more centrally down to code library disconnects, schema fragmentation and (especially in the age of multi-cloud) infrastructural misconfigurations.

Aiming to counter these inconvenient truths and provide an acne-free way to grow up in the new data universe is MongoDB, Inc.

New York state of data mine

The company used the New York leg of its developer conference programme (normally called MongoDB World, but this year in the post-pandemic recovery period run as MongoDB .local) this month to announce the general availability of MongoDB Relational Migrator, a new tool that promises to simplify application migration and transformation. This is a technology that works to power migration functions from legacy relational deployments to modern document-based data models. In short, it aims to provide organisations with a streamlined way to improve operational efficiency when faced with what are often inevitable migration responsibilities.

“Customers often tell us it’s crucial that they modernise their legacy applications so they can quickly build new end-user experiences that take advantage of game-changing technologies and ship new features at high velocity. But they also say that it’s too risky, expensive and time-consuming, or that they just don’t know how to get started,” said Sahir Azam, chief product officer at MongoDB.

Data is (of course) the foundation of every application with a large portion of it still residing in legacy relational databases where it can’t easily support emerging applications that make use of new technologies. Rather like the point at which your old laptop, tablet or smartphone fails to be capable of running the 2023 iteration of a favourite application or data service, some portions of legacy data structure simply do not dovetail with ‘modern’ multi-cloud environments typified by their use of Application Programming Interface (API) connections, microservices, serverless provisioning advantage and let’s not even start of generative AI and Large Language Models (LLMs) and open source topographies.

“With MongoDB Relational Migrator, customers can now realise the full potential of software, data and new technologies like generative AI by migrating and modernising their legacy applications with a seamless, zero-downtime migration experience and without the heavy lifting. It’s now easier than ever to modernise applications and create innovative end-user experiences at the speed and scale that modern applications require with MongoDB Atlas,” added Azam.

Okay, so generative AI did have to come in after all, but what matters most here is a question of exactly how enterprises will make use of MongoDB Atlas’s flexible document model and scale-out capabilities. The company says that with MongoDB Relational Migrator, more organisations across all industries can quickly, cost-effectively migrate from legacy databases with little-to-no risk.

Locked-in back-end relief

Pointing once again to the need for progression in light of the new technologies we have seen surfaced over the last couple of decades in particular, MongoDB says that organisations of all shapes and sizes want to be able to make use of new technologies to transform. However, many companies remain locked-in to legacy relational databases in the backend of their applications, limiting their ability to adapt and modernize.

But why are legacy databases so awfully bad? Many argue that legacy software is there for a reason i.e. it still works! Yes there are monolithic instances of applications and database use that need to be broken down like condemned buildings, but many of these older deployments (very often in government and public service institutions) do still work the way they were supposed to.  

Unperturbed by the ‘it still works’ argument, MongoDB insists that these legacy databases are rigid, unadaptable and difficult to use for supporting modern applications because of the complexity involved in mapping relationships between data when application requirements inevitably change. Additionally, because legacy databases were designed for earlier times before the advent of cloud computing, it is difficult to scale these databases without incurring significant costs. As a result, incorporating new technologies, quickly adapting to dynamic market changes, or continuously inventing new experiences for end-users are out of reach.

“For these reasons,” says Azam and team. “Customers are increasingly looking to migrate to a more flexible and scalable document-based data model that is easier to use and adapt. However, there is often considerable time, cost and risk associated with these migrations because they require highly specialised tooling and knowledge to assess existing applications and prepare data for migration. Even then, the migration process can result in data loss, application downtime, and a migrated application that does not function as intended.

Well, MongoDB did slot in the ‘flexible and scalable document-based data model’ line there… which – spoiler alert – is obviously what MongoDB is known for in the first place. That said, as CEO Dev Ittycheria has explained many times, the document model works for developers i.e. it more closely aligns to the ‘way developers think’ when forming programming logic and it has (somewhat undeniably) been a large part of why MongoDB has become as successful as it has.

In terms of further product functionality, with MongoDB Relational Migrator users can migrate and modernise legacy applications without the time, cost and risk typically associated with these projects – making it significantly faster and easier to optimise business operations and encourage developer innovation.

How migration functions actually work

MongoDB Relational Migrator analyses legacy databases, automatically generates new data schema and code and then executes a migration to MongoDB Atlas with no downtime required. Users can get started by connecting MongoDB Relational Migrator to their existing application database (e.g. Oracle, Microsoft SQL Server, MySQL or PostgreSQL) for assessment.

After analysing the application data, MongoDB Relational Migrator suggests a new data schema, transforms and migrates data to MongoDB Atlas with the ability to run continuous sync jobs for zero-downtime migrations and generates optimised code for working with data in the new, modernised application. Users can then run the modernised application in a testing environment to ensure it is operating as intended before deploying it to production.

Using MongoDB Relational Migrator, the company insists that organisations of all shapes and sizes can eliminate the barriers and heavy lifting associated with migrating and modernising applications – and the key phrase there is ‘heavy lifting’ i.e. this falls in line with the imperative to apply automation and autonomous management control at every layer of the IT stack in the current age of technology with our ubiquitous predilection for Artificial Intelligence and all the Machine Learning advantages that lie in wait.

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

Posted on mongodb google news. Visit mongodb google news

MongoDB has added five new capabilities to its popular managed cloud database MongoDB Atlas, including vector search, which will help users looking to build generative AI and other modern data applications onto the platorm – which can run in 110 regions on AWS, Azure, or Google Cloud.

Vector search is a way of finding and retrieving data that uses vector similarity calculations rather than traditional keyword-based search. This opens up ways to query unstructured data including audio and video.

Among other additions to the cloud database announced today, it has also added the ability to support data streaming applications for those looking to build event-driven applications that react and respond in real-time, and the ability to use Kubernetes more efficiently via the MongoDB Atlas CLI – with simpler generation of security credentials and the ability to import existing MongoDB Atlas projects and deployments with a single command.

As Adrian Bridgwater reports separately for The Stack, the company has also taken a new application modernisation and database migration toolkit GA, as it aims to continue wooing enterprise customers off “legacy” databases that are unable to evolve with changing application demands.  

MongoDB vector search in Atlas

Having vector search in MongoDB Atlas will also mean customers can “easily and securely augment the capabilities of pre-trained generative AI models with their own data to provide memory that creates more accurate and relevant results for specific domains or use cases” MongoDB said.

(As Ashok Reddy, CEO of KX, earlier put it: “The majority of information that exists is unstructured – text documents, social media feeds, chat streams, images and video files etc. – but if we ascribe vectors to this information we can start to manage it in new ways across all industries. Vectors enable us to go as granular as we need to for a given subject matter and list several hundred attributes for a chosen data object.”)

The move comes after DataStax’s move to bring vector search to its managed Apache Cassandra platform DataStax Astra DB this month.

The addition of vector search capabilities to both managed Cassandra and MongoDB platforms come as database providers move swiftly to support customers looking for a natural home for data-rich AI applications – and as data companies also look to pull applications closer to the data layer…

MongoDB Atlas: What else is new?

The addition of vector search to MongoDB Atlas was one of a flurry of announcements coming out of the company’s .local event in New York.

It comes on the back of impressively consistent growth for MongoDB – which on June 1 reported Q1 2024 revenues of $368.3 million, up 29% year-on-year and the most net new customer additions in over two years.

Among other updates for customers were the addition of:

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

MongoDB now has over 43,100 customers in 100+ countries.

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