Month: June 2023

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As of June 22, 2023, MongoDB, a software company specializing in database technology, has made significant strides in the market. The company’s Q1 earnings report for the year 2024 exceeded expectations, with analysts pleasantly surprised by the number of new customers added to the platform. In fact, MongoDB’s revenue saw an impressive 29% increase year over year for the quarter ending April 30, leading the company to raise its fiscal 2024 forecast for both revenue and income.

What’s more, MongoDB’s strong first-quarter results were highlighted by a whopping 40% growth in Atlas revenue and the most net new customer additions in over two years. This impressive performance was reflected in the company’s cash flow, with MongoDB generating $53.7 million of cash from operations compared to $11.6 million in the year-ago period. Free cash flow was also up, with $51.8 million for the three months ended April 30, 2023, compared to free cash flow of $8.4 million in the year-ago period.

Following the announcement of these impressive earnings, shares of MongoDB rose as much as 22% in extended trading. It’s clear that the company’s innovative products and strong performance have made it a major player in the database software market.

MDB Stock Opens Lower with Lower Volume Traded, but Shows Significant Earnings Growth Improvement

On June 22, 2023, MDB stock opened at 366.22, down 21.75 from the previous close of 387.97. The volume traded was 163,846, which is significantly lower than the average volume of 1,817,239 over the past three months. The market cap of MDB is $26.8B.

MDB’s earnings growth last year was -5.89%, but this year it has improved significantly to +89.49%. The expected earnings growth for the next five years is +8.00%. The revenue growth for the last year was +46.95%.

MDB’s P/E ratio is NM, which means it is not meaningful. The price/sales ratio is 11.45, and the price/book ratio is 35.89.

In comparison to other technology services companies, HubSpot Inc (HUBS) saw a 2.09% increase, while ANSYS Inc (ANSS) and Take-Two Interactive (TTWO) saw no change in their stock prices.

MDB’s next reporting date is on August 31, 2023, with an EPS forecast of $0.44. The annual revenue for the last year was $1.3B, and the annual profit was -$345.4M, resulting in a net profit margin of -26.90%.

MDB operates in the packaged software industry and is headquartered in New York, New York.

MongoDB Inc: Stock Price Increases and Positive Outlook for Investors

MongoDB Inc, a leading database management company, has been performing well in the stock market, with a steady increase in its stock price over the past few months. As of June 22, 2023, the median target price for MDB stock is $400.00, with a high estimate of $430.00 and a low estimate of $210.00. This indicates a +3.10% increase from the last price of $387.97.

The positive outlook for MDB stock is supported by the current consensus among 26 polled investment analysts, who recommend buying the stock.

Looking at the company’s financial performance, MongoDB Inc has reported earnings per share of $0.44 and sales of $393.9M for the current quarter. The company is set to report its earnings on August 31, 2023, which will provide further insights into its financial performance.

Overall, the positive outlook for MDB stock is driven by the company’s strong market position in the database management industry and its ability to deliver innovative solutions to its customers. As more companies embrace digital transformation and cloud-based solutions, MongoDB Inc is well-positioned to benefit from this trend and continue to deliver value to its shareholders.

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2023-06-22

MongoDB Relational Migrator makes it significantly faster and easier to migrate from legacy database technologies to MongoDB Atlas with no downtime using intelligent data schema and code recommendations

Accenture, Capgemini, Globant, Nationwide Building Society, Powerledger, and Tech Mahindra among customers and partners modernizing applications with MongoDB Relational Migrator

NEW YORK, June 22, 2023 /PRNewswire/ — MongoDB, Inc. (NASDAQ: MDB) today at its developer conference MongoDB.local NYC announced the general availability of MongoDB Relational Migrator, a new tool that simplifies application migration and transformation—from legacy relational to modern document-based data models—providing organizations a streamlined way to improve operational efficiency and get more out of their data. Data is 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 leverage new technologies using a fully managed, multi-cloud developer data platform with best-in-class security, resilience, and performance. Already in use by tens of thousands of customers and millions of developers around the world, MongoDB Atlas’s flexible document model and scale-out capabilities are helping customers build modern applications that leverage the latest technologies, empowering them to reimagine business operations and end-user experiences. Now, with MongoDB Relational Migrator, more organizations across all industries can quickly, easily, cost-effectively, and with little-to-no risk migrate from legacy databases and embrace the future. To get started with MongoDB Relational Migrator, visit mongodb.com/products/relational-migrator.

Organizations today have a clear imperative—modernize legacy applications to prepare their businesses for the future. New technologies like generative AI and large language models (LLMs) are another wave in a series of innovations over the past few decades that are opening up new possibilities for what’s possible with software and data for business operations and end-user experiences. Organizations of all sizes want to be able to make use of new technologies to transform their businesses. However, many companies remain locked-in to legacy relational databases in the backend of their applications, limiting their ability to adapt and modernize. 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 an era 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, 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 specialized 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. Together, these challenges often prevent even the most well-funded and technologically savvy organizations from being able to cost-effectively migrate and modernize their applications so they can be ready for the future.

With MongoDB Relational Migrator, customers can migrate and modernize legacy applications without the time, cost, and risk typically associated with these projects—making it significantly faster and easier to optimize business operations and inspire developer innovation. MongoDB Relational Migrator analyzes legacy databases, automatically generates new data schema and code, and then executes a seamless migration to MongoDB Atlas with no downtime required. Customers can quickly get started by simply connecting MongoDB Relational Migrator to their existing application database (e.g., Oracle, Microsoft SQL Server, MySQL, and PostgreSQL) for assessment. After analyzing 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 optimized code for working with data in the new, modernized application. Customers can then run the modernized application in a testing environment to ensure it is operating as intended before deploying it to production. Using MongoDB Relational Migrator, organizations of all shapes and sizes can eliminate the barriers and heavy lifting associated with migrating and modernizing applications to ensure they are better equipped to build the next generation of highly engaging, mission-critical applications.

“Customers often tell us it’s crucial that they modernize 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. “With MongoDB Relational Migrator, customers can now realize the full potential of software, data, and new technologies like generative AI by migrating and modernizing their legacy applications with a seamless, zero-downtime migration experience and without the heavy lifting. It’s now easier than ever to modernize applications and create innovative end-user experiences at the speed and scale that modern applications require with MongoDB Atlas.”

Customers that want a tailored modernization experience can work with MongoDB Professional Services and MongoDB Ecosystem Partners (e.g., Accenture, Capgemini, Globant, and Tech Mahindra) to unlock what’s possible with the next generation of software and data.

Accenture is a global professional services company with leading capabilities in digital, cloud, and security. “Together, Accenture and MongoDB provide unparalleled expertise to help customers modernize their environments and adopt a cloud-first approach throughout their organizations. Our partnership helps enterprises unlock value from data by modernizing and building new applications faster,” said Stephen Meyer, Associate Director, Cloud First Software Engineering, NoSQL Lead at Accenture. “Along with Accenture’s own capabilities and solutions, the release of MongoDB Relational Migrator will enable customers to accelerate their modernization strategies.”

Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. “Capgemini’s collaboration with MongoDB has been a stepping stone to enhance strong migration offerings and modernizing legacy systems. This has enabled customers to reap the benefits of new technology and helped them build the next generation of applications,” said Prasad Bakshi, Global Head of the Database Migration Practice at Capgemini. “Coupled with Capgemini’s proprietary Data Convert & Compare (DCC) accelerator, MongoDB Relational Migrator will enable us to provide unique database migration as-a-service capabilities to our customers. We’re excited to be able to accelerate the modernization journey for organizations of all shapes and sizes.”

Globant is a digitally native company focused on reinventing businesses through innovative technology solutions. “By leveraging MongoDB, our customers have seen immense benefits including accelerated development, transformation, cost savings and legacy modernization,” said Nicolás Ávila, Chief Technology Officer for North America at Globant. “We are seeing more and more customers leverage MongoDB’s Relational Migrator to migrate from traditional, relational databases to MongoDB Atlas with no downtime, making it a seamless and efficient solution. We look forward to using MongoDB tools to build more unique, modern digital experiences for our customers that help them reinvent their industries and outpace their competition.” 

Nationwide is the world’s largest building society as well as one of the largest savings providers and a top-three provider of mortgages in the UK. “Recently, I had the chance to employ MongoDB’s Relational Migrator and I was genuinely amazed by its outstanding performance,” said Peter Madeley, Senior Software Engineer at Nationwide Building Society. “The user interface of the tool is intuitively designed and the entity relationship diagrams proved to be invaluable in offering a detailed visual representation of my data structures. This migrator not only streamlines the transition from relational data to a document model, but it also ensures data integrity and offers a high degree of adaptability.”

Founded in 2016, Powerledger develops software solutions for the tracking, tracing, and trading of renewable energy. “We needed to demonstrate our platform’s ability to ingest a much higher volume of data and cater to the one billion users we aim to serve in the future, which required a level of scalability and flexibility that our previous relational database couldn’t offer,” said Dr. Vivek Bhandari, CTO at Powerledger. “Migrating an entire database is a pretty bold and risky endeavor. Our main priorities—and challenges—were to do a complete data platform migration, as well as add in scalability and flexibility without disrupting the platform or hindering data security. Amazingly, using MongoDB Relational Migrator, we didn’t experience any disruption or downtime.”

Tech Mahindra is a leading provider of digital transformation, consulting, and business re-engineering services and solutions. “The partnership with MongoDB helps unlock the full potential of data, data transformation, migration, and data consistency,” said Kunal Purohit, Chief Digital Services Officer at Tech Mahindra. “Tech Mahindra and MongoDB, together, will navigate the vast sea of information, harness its power, and chart a course towards industry-wide transformation journeys. Our enterprise customers can hugely benefit from this tool by leveraging its readily available migration interfaces, which in turn will help them quickly onboard the required data interfaces onto the target platform.”

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

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SOURCE MongoDB, Inc.

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MongoDB , Inc. (NASDAQ:) today at its developer conference MongoDB.local NYC announced new capabilities for the world’s most popular NoSQL database for building modern applications faster and with less heavy lifting. The new tools announced today empower developers to do their best work—including additional programming language support for using infrastructure-as-code (IaC) to deploy MongoDB Atlas on AWS and building server-side applications with Kotlin, streamlined capabilities for MongoDB Atlas Kubernetes Operator, and the general availability of the the PyMongoArrow library for more efficient data analysis using Python. Together, these new capabilities expand MongoDB’s mission to meet developers where they are by integrating the tools they love in a single developer data platform to make it faster and easier to build applications. To learn more about building with MongoDB, visit mongodb.com.

Tens of thousands of customers and millions of developers rely on MongoDB Atlas every day as their preferred developer data platform to power applications because of its flexible data model, speed to deploy new features, and performance at scale. Developers also love using MongoDB Atlas because it eliminates the undifferentiated heavy lifting of infrastructure management and enables going from idea to innovation quickly with a unified developer data platform. However, for certain use cases, developers want to use specialized tools with MongoDB Atlas to better integrate their established workflows and have more granular control over the operational experience. For example, developers who prefer provisioning MongoDB Atlas clusters with IaC on AWS or building server-side applications want to use their programming language of choice. Further, many developers want to use the MongoDB Atlas command line interface (CLI) for more tasks in their specific workflows, while other developers want to be able to use additional programming languages for data science, machine learning, and application-driven analytics.

The new features and integrations announced today reinforce MongoDB’s commitment to providing the best developer experience for building and iterating on ideas rather than wrangling tooling:

• Additional options for deploying MongoDB Atlas on AWS: Developers can now use additional programming languages to create, manage, and update MongoDB Atlas resources using IaC with the AWS Cloud Development Kit (CDK). MongoDB provides support for IaC on AWS with the AWS CloudFormation Public Registry, AWS Partner Solution Deployments, and the AWS CDK. However, many developers want to use additional programming languages beyond Javascript and Typescript. Now, developers can use IaC with the AWS CDK to manage MongoDB Atlas resources with C#, Go, Java, and Python—making it easier for developers to streamline workflows using a wider variety of programming languages and reduce the amount of time they spend managing infrastructure. To get started, visit mongodb.com/atlas/aws-cloudformation
• Expanded programming language support for server-side Kotlin: The Kotlin Driver for MongoDB now allows developers to build highly performant server-side applications on MongoDB using Kotlin. Previously, developers could use the MongoDB Realm Kotlin SDK for client-side development, but server-side developers relied on a community-created driver without official MongoDB support or had to write extensive custom code. As a result, developers faced longer software development cycles to build server-side Kotlin applications on MongoDB and risked application reliability without a fully supported MongoDB Kotlin driver. Now, with the Kotlin Driver for MongoDB, developers can use a best-in-class Kotlin experience for server-side application development to get from idea to production more quickly and reliably. To get started, visit mongodb.com/docs/drivers/kotlin/coroutine/current/.
• Streamlined MongoDB Atlas Kubernetes Operator functionality: Developers use the MongoDB Atlas Kubernetes Operator to manage projects and database clusters, reduce the effort required to automate configuration and management of MongoDB Atlas, and take advantage of containerized application development. However, developers want a simpler way to install and set up the MongoDB Atlas Kubernetes Operator to reduce friction and manage applications more quickly. Using the MongoDB Atlas CLI, developers can now install the MongoDB Atlas Kubernetes Operator and generate security credentials for quick and easy setup to reduce operational overhead. Developers then have the option to import existing MongoDB Atlas projects and deployments with a single command. This new enhancement dramatically simplifies how developers use Kubernetes with MongoDB Atlas and streamlines their workflows by providing greater speed when working with containers. To get started, visit mongodb.com/kubernetes/atlas-operator.
• Easier data processing and analytics on MongoDB using Python: With the general availability of the open source PyMongoArrow library maintained by MongoDB, developers and data analysts can use a Python-based analytics stack to extract insights from data on MongoDB and build data-driven applications more quickly. Building modern, intelligent applications that take advantage of application-driven analytics requires harnessing insights from application data and incorporating those insights back into applications to adjust business logic in real time. The PyMongoArrow library allows developers to efficiently convert data stored on MongoDB using popular frameworks (e.g., Apache Arrow Tables, Pandas DataFrames, or Numpy Arrays) and will receive ongoing development and support from MongoDB as the needs of Python-based analytics stacks evolve. With PyMongoArrow, developers, data scientists, and machine learning practitioners have the tools they need to more efficiently manipulate and analyze data on MongoDB with Python to reduce software-development friction. To get started, visit mongo-arrow.readthedocs.io/en/latest.

“Our long-term vision is to create a developer data platform that removes as much builder friction as possible and makes it easier for developers to do what they do best—build,” said Andrew Davidson, Senior Vice President of Product at MongoDB. “Developers choose MongoDB Atlas because it’s the best place to quickly build applications that can make the most out of their data. We continually hear from developers that they want to be able to use even more tools seamlessly on MongoDB Atlas, so with these additional integrations and expanded features, we are taking another step in fulfilling our mission to meet developers where they are and to provide the best possible building experience with the least amount of friction.”

RedMonk is an industry analyst firm that exists to help companies understand and work with developers. “While the explosion in database, development, and infrastructure tooling in recent years has put more and more resources into the hands of developers, it’s also led to a heavily fragmented and inefficient developer experience,” said Stephen O’Grady, Principal Analyst at RedMonk. “Increasingly, however, enterprises are focusing on opportunities to thoughtfully retool their workflows to make them faster and more integrated. The C-suite is making these investments not just because it benefits developers by delivering a higher quality and lower friction experience, but because having more efficient developers that are able to iterate more quickly is the single best mechanism for improving an organization’s overall velocity.”

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.

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MongoDB Atlas Vector Search dramatically simplifies bringing generative AI and semantic search into applications for highly engaging end-user experiences

MongoDB Atlas Search Nodes now provide dedicated infrastructure for search use cases so customers can scale independently of their database to manage unpredictable spikes and high-throughput workloads with greater flexibility and operational efficiency

MongoDB Atlas Stream Processing transforms building event-driven applications that react and respond in real-time by unifying how developer teams work with data-in-motion and data-at-rest

MongoDB Atlas Time Series collections now make time series workloads more efficient at scale for use cases from predictive maintenance for factory equipment to automotive vehicle-fleet monitoring to financial trading platforms

New multi-cloud options for MongoDB Atlas Online Archive and Atlas Data Federation now enable customers to seamlessly tier and query data in Microsoft Azure and in addition to Amazon Web Services

Beamable, Pureinsights, Anywhere Real Estate, and Hootsuite among customers and partners building next-generation applications using new MongoDB Atlas capabilities

NEW YORK, June 22, 2023 /PRNewswire/ — MongoDB, Inc. (NASDAQ: MDB) today at its developer conference MongoDB.local NYC announced five new products and features for its industry-leading developer data platform, MongoDB Atlas, that make it significantly faster and easier for customers to build modern applications, for any workload or use case. The new products and features include: generative AI capabilities with MongoDB Atlas Vector Search for highly relevant information retrieval and personalization, MongoDB Atlas Search Nodes for dedicated resources with search workloads at enterprise scale, MongoDB Atlas Stream Processing for high-velocity streams of complex data, significant scaling and efficiency improvements for MongoDB Time Series collections, and new capabilities using MongoDB Atlas Data Federation for querying data and isolating workloads on Microsoft Azure. Together, these new features for MongoDB Atlas enable businesses to dramatically improve operational efficiency and speed up their pace of innovation by standardizing many types of workloads on a single developer data platform across the enterprise. To learn more about MongoDB Atlas, visit mongodb.com/atlas.

Organizations today face an inflection point with the explosion of new technology like generative AI and the exponential growth of different types of data being generated in real-time. Organizations of all sizes want to be able to take advantage of new technology like large language models (LLMs) and process streams of real-time data to provide highly engaging end-user experiences and take autonomous action on that data more quickly to build new classes of applications. The choice of a database is fundamental to ensuring not only the success of an application but also how fast it can be built, deployed, and continually updated. Organizations want a unified, fully managed platform for their developer teams that makes it easy to build, deploy, and scale modern applications seamlessly.

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. Tens of thousands of customers and millions of developers rely on MongoDB Atlas every day so they can innovate more quickly, efficiently, and cost-effectively with modern applications for virtually every use case across an organization. As the use of MongoDB Atlas has rapidly grown, customers have asked for even more integrated capabilities to meet the growing demands of their businesses and end-users, and MongoDB is meeting that demand:

• Integrate AI-powered search and personalization into applications on MongoDB Atlas: MongoDB Atlas Vector Search enables organizations to more quickly and easily build next-generation applications that use generative AI to dramatically enhance end-user experiences and improve productivity across teams. Generative AI is creating a once-in-a-generation shift in how end-users interact with applications. Organizations want to be able to use technology based on generative AI—like LLMs—but find it difficult to integrate into applications because most existing technology stacks lack the flexibility to store and process different types of data. For example, LLMs require data in the form of vectors, which are geometric representations of data (e.g., text, images, and audio). These types of AI models measure the similarity between vectors to probabilistically construct sentences from prompts, generate images from captions, or return search results that are more accurate and contain greater context than traditional search engines. To store vectors so LLMs can use them, some organizations have begun using specialized databases. However, single-purpose databases for use cases like vector stores or time series applications are often bolted on to existing technology stacks, resulting in more administrative complexity, an educational burden on developers, and longer time to value. With MongoDB Atlas Vector Search, customers can power a range of new workloads from semantic search with text to image search and comparison to highly personalized product recommendations using a single, familiar, unified platform across an entire organization—all with minimal developer friction. MongoDB Atlas Vector Search also allows customers to 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. Because MongoDB Atlas uses a highly flexible and scalable document-based data model that supports data of virtually any type, customers can also easily manage the outputs of LLMs using MongoDB Atlas for use cases like caching common search requests for faster results at less cost. MongoDB Atlas Vector Search is integrated with the open source LangChain and LlamaIndex frameworks with tools for accessing and managing LLMs for a wide variety of applications. Customers can use these frameworks to access LLMs from MongoDB Partners (e.g., AWS, Databricks, Google Cloud, Microsoft Azure, MindsDB) and model providers (e.g., Anthropic, Hugging Face, and OpenAI) to generate vector embeddings and build AI-powered applications on MongoDB Atlas. To learn more, visit mongodb.com/products/platform/atlas-vector-search.

• Isolate and scale search workloads on MongoDB Atlas: MongoDB Atlas Search Nodes provide dedicated infrastructure for customers to scale search workloads independent of their database, enabling workload isolation, resource optimization, and better performance at scale. Today, customers use MongoDB Atlas Search to quickly and easily build relevance-based search capabilities directly into applications for a variety of use cases (e.g., personalized recommendations, product catalog and content search, multimedia management, and geospatial applications) using a seamlessly integrated developer experience. However, customers that have scaled their search workloads with MongoDB Atlas Search have asked for the ability to access and control dedicated resources to run search workloads independent of the database. With MongoDB Atlas Search Nodes, customers can now use dedicated infrastructure to seamlessly scale their MongoDB Atlas Vector Search and MongoDB Atlas Search workloads with greater flexibility and control to provide end-users the best relevance-based and AI-powered search experiences. To learn more, visit mongodb.com/atlas/search.

• Process high-velocity streams of complex data with MongoDB Atlas: MongoDB Atlas Stream Processing transforms the way organizations can process streaming data to engage end-users and speed up operations. Real-time streaming data (e.g., data coming from IoT devices, end-user browsing behaviors, inventory feeds) is critical to modern applications because it gives organizations the ability to engage end-users with real-time experiences as behaviors change and optimize business operations as conditions change. Streaming data is rich, heterogeneous, and constantly changing—requiring a flexible and scalable data model that can quickly evolve as conditions change. For this reason, rigid and inflexible relational data schemas are less ideal for working with real-time data that can keep up with ground truth. To incorporate streaming data into applications today, many developer teams must use specialized programming languages, libraries, application programming interfaces (APIs), and drivers bolted onto existing technology stacks. This creates a complex and fragmented development experience with teams having to learn how to use different tools for ever-changing use cases, leading to longer development cycles and increased costs. As a result, developers working with streaming data often face a level of complexity that leads to a slower pace of innovation and a risk to the business of falling behind the competition. With MongoDB Atlas Stream Processing, customers now have a single interface to easily extract insights from high-velocity and high-volume streaming data. MongoDB Atlas Stream Processing works with any type of data, and with its flexible data model, enables customers to build highly engaging applications that can analyze data in real time to adjust application behavior and inform business operations (e.g., highly personalized promotional offers, real-time inventory management, fraud prevention). MongoDB’s flexible data model can also be easily changed over time as needs evolve to ensure applications are consistently providing an optimized experience for end-users and making business operations more efficient. With MongoDB Atlas Stream Processing, organizations can now do significantly more with their data in less time and with no heavy lifting. To learn more, visit mongodb.com/products/platform/atlas-stream-processing.

• Scale with greater flexibility using MongoDB Time Series collections: Workload scalability and data flexibility for MongoDB Time Series collections now make it easier to handle enterprise-scale time series workloads and provide the option to modify data that has already been ingested. Time series workloads can grow quickly in use cases where, for example, millions of devices are sending data to a database for processing. Once data is ingested, time series databases typically do not allow that data to be modified. If there was an error in the data before it was ingested into the database, that means future analyses would be flawed. Further, because time series data evolves as real-world conditions change, a flexible data model is required to ensure it can effectively be put to use with the ability to quickly map new relationships between data, generate forecasts, and update the business logic of applications or make operations more efficient. Now, MongoDB Time Series collections provide scaling enhancements and the ability to modify time series data—giving customers more control over their data at scale. These new capabilities result in better storage efficiency and improved query speeds for the most demanding time series workloads while helping customers meet strict data governance requirements. Together, these new enhancements to MongoDB Time Series collections give customers the scalability and flexibility required for mission-critical time series workloads. To get started, visit mongodb.com/time-series. 

• Tier and query data on Microsoft Azure with MongoDB Atlas Online Archive and Atlas Data Federation: New multi-cloud options bring Microsoft Azure support to MongoDB Atlas Online Archive and Atlas Data Federation in addition to Amazon Web Services (AWS). Customers today use MongoDB Atlas Online Archive to automatically tier Atlas databases to the most cost-effective cloud object storage option while retaining the ability to query. By adding support for Microsoft Azure, customers can now more easily keep their entire workloads in the same cloud. Atlas Data Federation provides a seamless way to read and write data from Atlas databases and cloud object stores. This dramatically simplifies how customers can generate datasets from Atlas to feed downstream applications and systems that leverage cloud storage. Now, by adding support for Microsoft Azure Blob Storage, customers can work with Azure data in addition to AWS. To learn more, visit mongodb.com/atlas/data-federation.

“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, President and CEO at MongoDB. “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.”

LangChain is a framework designed to simplify the creation of applications using large language models. “By modularizing the components of an application powered with a large language model, LangChain aims to make the app building experience flexible, yet cohesive,” said Harrison Chase, Co-Founder and CEO at LangChain. “Similarly, MongoDB Atlas brings the database and vector store in one place—thus also providing flexibility and cohesiveness. Together, we’re a natural fit to enhance developer productivity, which has been demonstrated by the organic community enthusiasm which has already led to several integrations.”

LlamaIndex is a data framework to help customers build large language model apps. “We are excited to be partnering with MongoDB, as we share the same goal of simplifying how developers build modern apps with LLMs that are connected to external or proprietary data,” said Jerry Liu, CEO at LlamaIndex. “With the announcement of MongoDB Atlas Vector Search, developers can now easily store all the requisite data, from context chunks to indexes and vectors, in one platform while connecting it to their preferred LLM with LlamaIndex.”

Nomic is a company that helps improve AI explainability and accessibility. “MongoDB and Nomic is a powerful combination of technologies that allows you to store and search across vectors with MongoDB and visualize them with Nomic,” said Brandon Duderstadt, CEO at Nomic. “Using the two technologies together you can get a deeper understanding of your data with Nomic and then operationalize it in a battle tested platform with MongoDB Atlas.”

Beamable is a technology company that provides a full-stack, live operations platform that allows game developers to both build and operate live games with 32 games currently live and dozens more in active development. “We built our platform on MongoDB Atlas due to its workload versatility, and ability to easily scale vertically and horizontally,” said Ali El Rhermoul, CTO at Beamable. “We’ve been evaluating MongoDB Atlas Vector Search capabilities in conjunction with OpenAI Embeddings for use in generative AI applications, and were impressed by how trivial it was to set up and use. This means we and our game developer community can build novel AI-powered experiences on Beamable, with familiar technology and without expanding the technology stack.”

Pureinsights is an independent search technology and services company that partners with MongoDB to help customers deploy search-based applications on MongoDB Atlas. “We’ve been working with Atlas Vector Search while in private preview and are excited to be partnering with MongoDB to help enable this new capability for our customers,” said Kamran Khan, CEO at Pureinsights. “Being able to store and use vectors within the MongoDB Atlas platform powers new workloads and exciting AI-powered experiences that users want, like semantic search and generative answers.”

Anywhere Real Estate is the parent company of some of the world’s leading real estate brokerage brands and service businesses. “Our development teams were spending too much time doing undifferentiated work managing our previous search solution, and we are currently rolling out our new solution powered by MongoDB Atlas and Atlas Search to our brand portfolio, which includes Better Homes and Gardens Real Estate, CENTURY 21, Coldwell Banker, Corcoran, ERA, and Sotheby’s International Realty,” said Damian Ng, Senior Vice President of Technology at Anywhere Real Estate. “Atlas Search allows us to ingest data from hundreds of Multiple Listing Services sources, aggregate the data, and provide customers with a search solution that efficiently delivers accurate and up-to-date information. Since implementing Atlas Search, we’ve observed a 60 percent improvement in response time for search results, and we’re excited that MongoDB is decoupling its architecture to have dedicated nodes for Atlas Search so we can have even greater flexibility and control with our search workloads.”

Hootsuite is a global leader in social media management that powers social media for brands and organizations around the world, from the smallest businesses to the largest enterprises. “Using Time Series collections with MongoDB Atlas, we were able to build a new feature that processes and stores a high volume of streaming data without ballooning our storage costs,” said Chris Martin, Senior Software Developer at Hootsuite. “It also saved us from provisioning and maintaining a separate database built specifically for the purpose.”

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.

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MongoDB
press@mongodb.com

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MongoDB on Thursday introduced new language support, easier installation of Atlas’ Kubernetes Operator, and a new Kotlin driver for its NoSQL Atlas database-as-a-service — all designed to streamline developer tasks, including work related to infrastructure management.

The new features were launched along with vector search and stream processing capabilities geared toward support for development of generative AI applications.

Noting that many developers want to use programming languages other than Javascript and Typescript to deploy Atlas on AWS, the company said that it was adding support for C#, Go, Java, and Python in order to help developers reduce the amount of time needed to manage infrastructure.

Typically, MongoDB developers have managed infrastructure-as-code (IaC) on AWS via the public cloud provider’s CloudFormation Public Registry, Partner Solution Deployments, and its Cloud Development Kit (CDK).

The company has also added support for Kotlin for developers building server-side applications. Previously, developers could use the MongoDB Realm Kotlin software development kit (SDK) for client-side development, but server-side developers relied on a community-created driver without official MongoDB support, or had to write extensive custom code, the company said.

“As a result, developers faced longer software development cycles to build server-side Kotlin applications on MongoDB and risked application reliability without a fully supported MongoDB Kotlin driver,” it added.

Easier way to install Atlas Kubernetes Operator

MongoDB is also providing an easier way to install the Atlas Kubernetes Operator — a tool that developers use to manage projects and database clusters.

“Using the MongoDB Atlas command line interface (CLI), developers can now install the MongoDB Atlas Kubernetes Operator and generate security credentials quickly in order to reduce operational overhead,” the company said, adding that developers will now have the option to import existing MongoDB Atlas projects and deployments with a single command.

The update, according to the company, is expected to provide greater agility for developers while working with containers.

While the company did not immediately provide information on the availability of the new features, it said that it was making the open source PyMongoArrow library generally available.

The library, according to the company, can be used to convert data stored on MongoDB using popular frameworks such as Apache Arrow Tables, Pandas, DataFrames and Numpy Arrays.

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In an era defined by the rapid advancements of artificial intelligence and the need for scalable distributed systems, MongoDB Inc. is at the forefront, empowering developers and organizations to harness the full potential of these technologies.

At the MongoDB .Local NYC 2023 event, Mark Porter (pictured), chief technology officer of MongoDB, took the stage to share insights on MongoDB’s developer-focused approach, the integration of AI capabilities into its platform and the importance of scalable distributed systems. Porter’s vision and expertise shed light on the future of AI and how MongoDB is positioned to meet the evolving needs of developers and businesses.

“Hundreds of companies use MongoDB as the foundation of their AI apps,” Porter told theCUBE during the event. “And that’s because it’s distributed, it’s scalable, it’s flexible and it’s easy to work against.”

Porter spoke with theCUBE industry analyst John Furrier at the MongoDB .local NYC event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed MongoDB’s role in shaping the future of AI and scalable distributed systems. (* Disclosure below.)

MongoDB’s developer-focused approach and integration of AI

MongoDB .local NYC 2023 kicked off with a focus on developer-first platforms and the power of MongoDB’s distributed system. The company’s document model is an important part of enabling the flexible data structures needed for AI applications. There is a growing trend of enhancing applications with AI, and developers can leverage MongoDB’s distributed document data model to incorporate AI directly into their apps. While MongoDB is not solely an AI company, it serves as the foundation for numerous AI applications due to its scalability, flexibility and ease of use, according to Porter.

The integration of AI capabilities into the MongoDB platform opens up new possibilities for developers to build innovative apps that leverage the power of AI.

“What’s happening is people are enhancing their apps with AI,” Porter said. “If you think about having a document data model and that document data model is distributed across a large MongoDB estate, what developers can now do with the vector search technology released today … they can build apps that incorporate AI directly into their apps.”

MongoDB’s distributed system and its role in future scalability

Also up for discussion was MongoDB’s distributed system and its significance in handling large volumes of data and ensuring scalability. Legacy systems often hit a scalability wall, leading to re-architecting and data fragmentation across different databases.

With MongoDB, scaling out becomes effortless, according to Porter. One example includes MongoDB customers running a thousand nodes with three petabytes of transactionally consistent data, a feat not achievable with legacy systems.

“When you talk about getting to the scale that’s coming up, what’s your reaction to that? What do you see when you hear that phrase? With MongoDB, you can scale out,” Porter added.

By offering a distributed system, MongoDB enables applications to seamlessly run across multiple cloud providers, including Microsoft Azure, AWS cloud and Google Cloud Platform. MongoDB’s approach levels the playing field and promotes honesty and collaboration among cloud providers, Porter explained. As the era of AI continues to evolve, MongoDB remains at the forefront, empowering developers and organizations to leverage the potential of AI while building robust and scalable applications, he added.

“First off, it’s got to be easy to program against. It’s got to all be in one system. We believe that time series and AI and IoT data and tech data should all be in one system that makes it easier,” Porter stated. “The second thing you talk about is value. One of the most interesting things to me is once those things are all in one system, and once that system can scale, the day your app gets featured on the app store or the day you go big is not your biggest day of failure when you need to scale up and you can’t scale up anymore. Well, with MongoDB you can scale out.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the MongoDB .local NYC event:

(* Disclosure: MongoDB Inc. sponsored this segment of theCUBE. Neither MongoDB nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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It was an exciting day for database company MongoDB (NASDAQ:MDB). It stepped up its partnership with Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) and its Google Cloud unit and then showed off some new features on its Atlas system. That flood of good news sent MongoDB up substantially in Thursday afternoon’s trading.

MongoDB stepped up its deal with Google Cloud, which will, in turn, offer more support for “…the next generation of generative AI applications,” noted Google Cloud’s corporate vice president for global ecosystem and channels, Kevin Ichhpurani. The new move brings together MongoDB’s systems with the Vertex AI large language model from Google Cloud.

That’s a good start, but that wasn’t all. MongoDB also showed off a slate of new features geared toward, again, developing generative AI systems. There were five new systems for MongoDB Atlas that represent the next steps toward making MongoDB a complete developer platform. There’s the new Atlas Search system, as well as querying systems and data streaming. MongoDB’s move is seen as a means to fend off several of its competitors in the space, including Databricks—who recently brought out its Lakehouse Apps system—and Snowflake (NYSE:SNOW). Snowflake recently brought out a Native Application Framework which allows users to not only build but also run applications inside the Snowflake Data Cloud platform.

Turning to Wall Street, analysts are enamored by MDB stock, calling it a Strong Buy thanks to a combination of 17 Buy ratings, two Holds, and one Sell. However, MongoDB stock also comes with an average price target of $367.37, which means a downside risk of 5.24%.

Disclosure

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MongoDB Inc. (NASDAQ:MDB) at last check was buoying at $378.23 on Thursday, June 22, with a rise of 1.41% from its closing price on previous day.

Taking a look at stock we notice that its last check on previous day was $372.96 with its price kept floating in the range of $367.60 and $381.9499 on the day. Considering stock’s 52-week price range provides that MDB hit a high price of $398.89 and saw its price falling to a low level of $135.15 during that period. Over a period of past 1-month, stock came adding 33.51% in its value.

With its current market valuation of $26.70 billion. Analysts are in estimates of $0.46 per share for company’s earnings in the current quarter and are expecting its annual EPS growth moving up to $1.54 for 2024 with estimates of that growing to $2.09 in next year. These estimates are suggesting current year growth of 90.10% for EPS and 35.70% growth next year.

Analysts watching the company’s growth closely have provided estimates for its revenue growth with an average revenue estimate of $392.25 million. They suggested that in the process company could generate revenue of as low as $388 million which could climb up to $410 million to hit a high. The average estimate is representing an increase of 29.20% in sales growth from that of posted by the company in the same quarter of last year. In keeping analyst consensus estimate with, company is forecasted to be making an annual revenue of $1.55 billion in 2024, which will be 20.60% more from revenue generated by the company last year.

In last 7 days, analysts came adjusting their opinions about stock’s EPS with no upward and no downward revisions, an indication which could give clearer idea about the company’s short term price movement. In contrast, when we review MDB stock’s current outlook then short term indicators are assigning it an average of 100% Buy, while medium term indicators are categorizing the stock at an average of 100% Buy. Long term indicators are suggesting an average of 100% Buy for it.

According to ratings assigned by 26 analysts at the scale of 1 to 5 with 1.00 representing a strong buy and 5.00 suggesting a strong sell; 4 of them are recommending MongoDB Inc. (MDB) as a Hold, while 17 are in view that stock is a Buy. Recommendation by 0 analysts for the stock is an Underweight while number of those analysts who rated the stock as an Overweight is 4, whereas 1 of them are considering the stock as a Sell. When taken as whole, stock gets a rating of Overweight and that encourages the investors to exploit the opportunity and build their stake up in the company.

Digging deeper we become aware of the PEG ratio of the MDB stock which is currently positioned at 0. It further provides that stock’s current price level is 9.20% away from its 20-day simple moving average and is 32.94% off its SMA50. Its relative strength index (RSI) for 14-periods is oscillating at 68.27 while volatility remained at 3.54% over the past week which changes to 4.49% when measuring it over the past month. Beta is valued at 1.10, while measure of average true range or ATR is currently at 16.35. In predicting price targets of as low as $210.00 and as high as $430.00, analysts are in agreement on assigning the stock over the next 12 months average price target of $376.19. Stock’s current price level is 44.48% above from estimated low price target while it is -13.69% below the estimated high; and even if the MDB’s share succeeded to reach the median price of $400.00, then the outlook of -5.76% could come to the excitement of the investors.

In comparing MongoDB Inc. (MDB)’s stock with other industry players reveals that stock’s latest price change of 1.41% and that of 40.32% over the past 12 months is in competing position with that of Progress Software Corporation (PRGS) which saw its stock price fall by -0.49% in the recent trading and went through an increase of 20.19% in past 12-month trading. Industry’s another major player Pixelworks Inc. (PXLW) has fall -3.74% down in latest trading session, but over the past year has faced afall of -12.30% over the same period. MongoDB Inc. has a P/E ratio of 0 against that of Progress Software Corporation’s 25.81. On the other hand, the S&P 500 Index is down -0.01% in the early deals today while the Dow Jones Industrial was dealinglower at -0.22%.

Having a second look at MongoDB Inc. (NASDAQ:MDB) provides that stock’s average daily trading volume for 3 months was 1.76 million, while it jumped to 1.68 million when we calculate an average volume for past 10 days. Number of outstanding shares of the stock stood at 70.18 million.

The percentage of outstanding shares held by the insiders is 2.60% while it is 92.40% for the institutional holders. The figures also indicate that as of May 30, 2023, number of stock’s short shares was 3.58 million which implies a short ratio of 2.03. This shows down a 5.07% of Short Interest in company’s outstanding shares on the day. In May the standing of shares short improved as it was 3.74 million in the previous month. Addition of 92.15% by stock’s current price to its year-to-date value in today’s trading is likely to be increasing investors’ interest in the stock as it is hinting an extended uptrend.

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<meta name="x-tns-categories" content="AI / Data / Large Language Models“><meta name="x-tns-authors" content="Andrew Brust“>

2023-06-22 12:07:44

Generative AI, Streaming Data, and More Come to MongoDB Atlas

MongoDB adds generative AI, analytics, streaming, mobile, cloud, and container features to Atlas; woos RDBMS devs and data scientists, too.

Jun 22nd, 2023 12:07pm by

Andrew Brust

At its MongoDB .local NYC developer conference today, document database leader MongoDB is announcing a slew of new features for its cloud-based Atlas database service. The new capabilities are focused on new workloads (including real-time streaming data and generative AI), developer productivity and migration of relational databases to MongoDB’s document store platform.

Check out: How MongoDB Brought Its Serverless Database Service to the Cloud

Workload Expansion

MongoDB views its market strategy as one of competing for a broadening array of “workloads.” It sees itself as having penetrated many large accounts but initially for niche applications. Its own version of “land and expand,” therefore, is to grow the number of workloads it’s used for by its many customers and, along the way, grow its appeal to new customers too. As a cloud-first company, MongoDB is initially landing these workloads in its Atlas service.

On the broadened workload front, then, MongoDB is announcing the private preview of Atlas Stream Processing, making a big push towards real-time and streaming data — which can come from logs, financial market data sources, telemetry and observability platforms, or IoT devices. This new capability is built around MongoDB’s core document data model, handling continuous processing of data, validation and stateful windowing.

Stateful abstractions over streaming data make working with it data more like working with conventional data at rest, which is the paradigm most familiar to developers. The trick to enabling developers to build real-time applications is to let them apply their existing skillsets, rather than making them code streaming data apps one way and conventional data apps a different way. As such, MongoDB says Stream Processing is a key part of Atlas, albeit in private preview to start.

In Search of AI

Next on the workload front is the public preview Atlas Vector Search. This feature aims to accommodate requirements for hot new tech, like generative AI/large language models (LLMs) and semantic search. In these use cases, chunks of data — whether they contain text, images, or audio–that can be used to train LLMs are encoded in a single values called vectors.

Atlas will allow vectors to be stored, of course, but that’s the easy part. The big value is that Atlas Vector Search provides for the indexing and querying of vectors, to allow finding similar ones in the database and then use LLMs to, in the company’s words, “probabilistically construct sentences from prompts, generate images from captions, or return search results that are more accurate and contain greater context than traditional search engines.” Atlas Vector Search also allows customers to augment pre-trained LLMs with their own data for more relevant results.

Atlas Vector Search is integrated with the open source LangChain and LlamaIndex frameworks, which will be appreciated by developers who have already come up the generative AI learning curve. The frameworks let such developers access LLMs from cloud providers and model providers like Anthropic, Hugging Face, and OpenAI.

Complementing all this is the new ability to dedicate specific nodes in an Atlas cluster to the Atlas Search service. MongoDB says Atlas Search Nodes, now in public preview, will enable workload isolation, resource optimization, and better performance at scale. Atlas Search Nodes service both Atlas Vector Search and the broader Atlas Search facility.

More Goodies

The streaming data and generative AI features are a big deal, but there’s more in the new capability manifest. Further Atlas enhancements include these nuggets:

• Atlas Time Series collections will now offer improved scalability, and support for deletes — which further enables modification of previously ingested time series data.
• Atlas Device Sync, which allows offline devices to sync with the mothership clutser, will now offer tiered device sync, as a private preview.
• Atlas SQL, which provides a SQL query interface to MongoDB for BI tools, is being brought to general availability (GA), along with MongoDB-developed connectors for Microsoft’s Power BI and Salesforce’s Tableau.
• Atlas Data Federation and Online Archive will enter private preview on Azure (the features had already been available on AWS). Online Archive provides for automatic tiering of Atlas databases to different cloud object storage options, while retaining the ability to query. Atlas Data Federation lets customers mix and match data from Atlas databases and cloud object stores, essentially giving Atlas access to cloud data lakes.
• And last, but certainly not least, Mongo and Atlas will add improvements in query performance and resource efficiency. The company said the platforms now offer up to a 50% improvement for grouping operations on subfields, up to 90% improvement for filtering on complex expressions, and a 4x-30x speedup for lookups in replica sets.

All about Developers

MongoDB has always been a company that has targeted developers as its VIPs, so improvements to the “developer experience” are part of every major product cycle. This time around, the company is announcing support for new programming languages.

For example, the MongoDB Driver for Kotlin is now GA, enabling server-side Kotlin development, in addition to previously-supported client-side development using the Realm Kotlin SDK. MongoDB’s PyMongoArrow library for Python development is now GA as well. The library, which is maintained by MongoDB, let developers convert MongoDB data to Pythonic data structures including Pandas DataFrames, NumPy arrays and Apache Arrow tables. This allows data scientists to work with data in MongoDB directly, while still using the tools, language and libraries to which they are accustomed.

Mobile and Containers

Further, on the programming language support front, MongoDB is adding new versions of its mobile development platform’s Realm SDK for Flutter (in GA) and C++ (in preview). Support for AWS Cloud Formation deployments, to create, manage, and update MongoDB Atlas resources on Amazon Web Services, will now be provided for C#, Go, Java, and Python, in addition to the previously supported JavaScript and TypeScript.

And in the world of containerized development, the Atlas Kubernetes Operator will offer simplified installation and use via the Atlas Command Line Interface (CLI). Specifically, developers can install the Operator, generate security credentials, then import existing MongoDB Atlas projects and deployments with a single CLI command.

Must read:

RDBMS Migration

Moving on to application modernization, and the ability to take over more workloads from relational databases, MongoDB is moving its Relational Migrator tool to GA. Relational Migrator works on a three-step paradigm: (1) analyze the source relational database and design the document schema to which it will be migrated; (2) migrate the data and (3) generate application code to query and maintain the data.

The Relational Migrator supports Oracle Database, Microsoft SQL Server, MySQL, and PostgreSQL source databases and will work with both Atlas and self-managed MongoDB. It can perform one-time “snapshot” migrations, but will also support a continuous migration mode, to facilitate parallel operation of the source and destination systems, using a change data capture (CDC) mechanism. This will keep the source and destination databases in sync, until customers are ready to cut over to the new document-based system completely.

Territorial Aspirations

MongoDB’s announcements cover a huge surface area. The company working to accommodate generative AI, analytics and BI, streaming data, mobile, cloud, and container scenarios. And it’s doing tons of groundwork to woo mobile developers, relational database developers, and data scientists. All up, MongoDB is clearly working to expand its utility, its appeal, and its franchise. MongoDB’s appeal to developers is certainly not in dispute, and this batch of announcements shores it up handily. But the company is clearly serious about winning over new constituencies as well and, in so doing, getting corporate tech leadership to see Atlas as a versatile platform that can service a broad range of needs, not just capably, but productively as well.

MongoDB is a client of Brust’s analyst and advisory firm, Blue Badge Insights.

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