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Article originally posted on InfoQ. Visit InfoQ

Vertex AI RAG Engine is a managed orchestration service aimed to make it easier to connect large language models (LLMs) to external data sources to be more up-to-date, generate more relevant responses, and hallucinate less.

According to Google, its new RAG Engine is the “sweet spot” for developers using Vertex AI to implement a RAG-based LLM, providing a balance between the ease of use of Vertex AI Search and the power of a custom RAG pipeline built using lower-level Vertex AI APIs such as Text Embedding API, Ranking API, etc.

The overall workflow supported by Vertex AI RAG Engine includes distinct steps for data ingestion from a number of different sources; data transformation, such as splitting data into chunks previous to indexing; embedding, which provides a numerical representation of text to capture its semantics and context; data indexing to build a corpus optimized for search; retrieval of relevant information from the knowledge based on a user’s prompt; and, last, a generation step where the original user query is augmented with the retrieved information.

Using Vertex AI RAG Engine you can easily integrate all those steps into your solution. The easiest way to start with Vertex AI RAG Engine is through its Python bindings which are part of the google-cloud-aiplatform package. After setting up a Google Cloud project and initializing the Vertex AI engine, you can easily create a corpus from your own local files or documents in Google Cloud Storage or Google Drive by using the upload_file or import_file methods.

# Currently supports Google first-party embedding models
EMBEDDING_MODEL = "publishers/google/models/text-embedding-004"  # @param {type:"string", isTemplate: true}
embedding_model_config = rag.EmbeddingModelConfig(publisher_model=EMBEDDING_MODEL)

rag_corpus = rag.create_corpus(
    display_name="my-rag-corpus", embedding_model_config=embedding_model_config
)

rag_file = rag.upload_file(
    corpus_name=rag_corpus.name,
    path="test.txt",
    display_name="test.txt",
    description="my test file",
)

Once you have a corpus, you create a retrieval tool which is then connected to the LLM to expose a new endpoint you can use to query the augmented model:

# Create a tool for the RAG Corpus
rag_retrieval_tool = Tool.from_retrieval(
    retrieval=rag.Retrieval(
        source=rag.VertexRagStore(
            rag_corpora=[rag_corpus.name],
            similarity_top_k=10,
            vector_distance_threshold=0.5,
        ),
    )
)

# Load tool into Gemini model
rag_gemini_model = GenerativeModel(
    "gemini-1.5-flash-001",  # your self-deployed endpoint
    tools=[rag_retrieval_tool],
)

response = rag_gemini_model.generate_content("What is RAG?")

According to Google, Vertex AI RAG Engine is particularly convenient for use cases like personalized investment advice and risk assessment, accelerated drug discovery and personalized treatment plans, and enhanced due diligence and contract review.

Retrieval Augmented Generation (RAG) is a technique often use to “ground” a large language model, that is, making it fitter to a particular use case or enterprise environment. RAG consists of retrieving information relevant to a particular task from a source that was not accessible to the model during training and feed it to the model along with a prompt. Alternatively, a model can be “grounded” through fine-tuning, a process whereas the external data is used to retrain the model so it is available for each query even when not specified at the prompt level.

Grounding a model enables it to better understand the context of a query and to have additional task-specific information available so it can generate a better response. More specifically in the context of enterprise data, grounding aims to circumvent a limitation of general LLMs by providing access to private data behind firewalls in a safe way.

Posted on mongodb google news. Visit mongodb google news

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

Posted on mongodb google news. Visit mongodb google news

In the dynamic landscape of global technology infrastructure, the successful management of complex database systems supporting multiple digital services stands as a testament to technical excellence and strategic oversight. As a senior technical consultant deployed by Tata Consultancy Services (TCS) to Apple Inc., Dheeraj Yadav’s leadership from June 2018 to December 2023 in Apple’s Information Systems and Technology (IS&T) team has set new benchmarks for enterprise-scale database management and operational excellence. 

 The scope of this role was particularly challenging, encompassing database support for Apple’s premium digital services including Apple One, Apple TV+, Apple Music, Apple Arcade, Apple Fitness+, Apple Books, and the App Store. As a Senior Database Administrator, Yadav faced the complex task of ensuring seamless database operations for services that serve millions of users globally while maintaining the highest standards of performance and reliability. 

 At the heart of this success story was Yadav’s comprehensive expertise in Oracle Database management, which formed the cornerstone of his contributions to Apple’s infrastructure. His mastery of Oracle technologies, comprising 80% of his focus, proved instrumental in managing critical enterprise data operations. Through expert implementation of Oracle’s advanced features, including Oracle Golden Gate for real-time data replication and sophisticated performance tuning techniques, he established robust and highly available database systems that supported Apple’s mission-critical applications. 

 Complementing his Oracle expertise, Yadav also contributed to Apple’s database innovation through his work with MongoDB and CockroachDB, which comprised 20% of his technical focus. His experience with MongoDB’s distributed architecture helped support specific use cases requiring flexible data models and horizontal scaling. During his engagement with CockroachDB, he successfully implemented its distributed architecture for select applications requiring strong consistency and geographic distribution. 

 Through careful orchestration of Oracle database systems on AWS, supplemented by MongoDB deployments, Yadav ensured that Apple’s digital services maintained optimal performance levels while scaling to meet growing global demand. Most notably, this was achieved while supporting the company’s rapid expansion of digital services and maintaining the high-performance standards expected of Apple’s technology infrastructure. 

 Stakeholder management played a crucial role in his success. Working within Apple’s IS&T team, Yadav collaborated effectively with multiple technical teams, service owners, and business stakeholders. His approach to database management and system optimization proved particularly valuable in supporting both customer-facing services and internal business operations. 

 The measured outcomes of his work were substantial. His expertise in Oracle database management, complemented by strategic use of MongoDB’s distributed architecture, contributed significantly to the reliability and scalability of Apple’s global technical infrastructure. The systems under his management supported millions of transactions daily while maintaining the high performance and reliability standards essential for Apple’s premium digital services. 

 For Dheeraj Yadav personally, this role as a TCS professional consultant at Apple represented a significant milestone in his career trajectory. His ability to manage complex database systems at global scale while maintaining focus on business objectives demonstrated the rare combination of technical expertise and strategic thinking essential for modern enterprise technology management. His success in this role has positioned him as a key professional in enterprise database administration, particularly in Oracle database systems with valuable expertise in NoSQL solutions. 

 The success in managing database operations for Apple’s digital services has become a model for enterprise-scale database management. By leveraging both traditional Oracle capabilities and modern database technologies while ensuring reliable support for critical business systems, Yadav’s work exemplified the power of strategic technical management in supporting global digital operations. 

 Looking ahead, the implications of his contributions extend beyond immediate technical operations. His work demonstrates how strategic database management across multiple database paradigms can support the growth and reliability of global digital services. As organisations worldwide grapple with similar challenges in managing complex digital infrastructures, Yadav’s approach stands as a blueprint for successful enterprise database administration. 

 About Dheeraj Yadav 

 Dheeraj Yadav is a seasoned Database Administrator with over 18 years of expertise, primarily focused on Oracle Database versions 9i through 19c, which comprises 80% of his technical proficiency. His remaining expertise spans MongoDB and CockroachDB implementations. Leading critical database operations for Fortune 500 clients in the technology and healthcare sectors, Dheeraj’s experience encompasses both on-premises Oracle solutions and cloud implementations on AWS, where he has successfully deployed and managed Oracle and MongoDB systems. His particular strength lies in Oracle Golden Gate configurations, performance tuning, and complex database migrations, complemented by practical experience with MongoDB’s distributed architecture. As an AWS Certified Cloud Practitioner, he effectively bridges the gap between traditional database management and modern cloud infrastructure, ensuring seamless integration and optimal performance across platforms. 

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

Posted on mongodb google news. Visit mongodb google news

Cantor Fitzgerald began coverage on shares of MongoDB (NASDAQ:MDB – Free Report) in a research report sent to investors on Friday, Marketbeat Ratings reports. The firm issued an overweight rating and a $344.00 price target on the stock.

A number of other equities research analysts have also recently weighed in on MDB. DA Davidson upped their price target on MongoDB from $340.00 to $405.00 and gave the stock a “buy” rating in a report on Tuesday, December 10th. Wells Fargo & Company upped their price target on MongoDB from $350.00 to $425.00 and gave the stock an “overweight” rating in a report on Tuesday, December 10th. Rosenblatt Securities started coverage on MongoDB in a report on Tuesday, December 17th. They issued a “buy” rating and a $350.00 price target on the stock. Mizuho upped their price target on MongoDB from $275.00 to $320.00 and gave the stock a “neutral” rating in a report on Tuesday, December 10th. Finally, Needham & Company LLC upped their price target on MongoDB from $335.00 to $415.00 and gave the stock a “buy” rating in a report on Tuesday, December 10th. Two research analysts have rated the stock with a sell rating, four have issued a hold rating, twenty-two have given a buy rating and two have given a strong buy rating to the stock. Based on data from MarketBeat, the company presently has an average rating of “Moderate Buy” and a consensus price target of $363.93.

Get Our Latest Analysis on MDB

MongoDB Trading Up 2.1 %

NASDAQ MDB opened at $253.11 on Friday. The business’s 50 day moving average is $278.24 and its two-hundred day moving average is $269.19. MongoDB has a 12-month low of $212.74 and a 12-month high of $509.62. The stock has a market capitalization of $18.85 billion, a price-to-earnings ratio of -92.38 and a beta of 1.25.

MongoDB (NASDAQ:MDB – Get Free Report) last released its earnings results on Monday, December 9th. The company reported $1.16 earnings per share (EPS) for the quarter, topping the consensus estimate of $0.68 by $0.48. MongoDB had a negative return on equity of 12.22% and a negative net margin of 10.46%. The company had revenue of $529.40 million for the quarter, compared to analysts’ expectations of $497.39 million. During the same quarter in the previous year, the firm posted $0.96 EPS. The firm’s quarterly revenue was up 22.3% on a year-over-year basis. Equities analysts forecast that MongoDB will post -1.79 earnings per share for the current fiscal year.

Insiders Place Their Bets

In related news, Director Dwight A. Merriman sold 3,000 shares of the stock in a transaction on Monday, November 4th. The stock was sold at an average price of $269.57, for a total value of $808,710.00. Following the transaction, the director now directly owns 1,127,006 shares of the company’s stock, valued at approximately $303,807,007.42. This trade represents a 0.27 % decrease in their position. The sale was disclosed in a filing with the SEC, which can be accessed through the SEC website. Also, CFO Michael Lawrence Gordon sold 5,000 shares of the stock in a transaction on Monday, December 16th. The stock was sold at an average price of $267.85, for a total value of $1,339,250.00. Following the completion of the transaction, the chief financial officer now directly owns 80,307 shares in the company, valued at $21,510,229.95. This trade represents a 5.86 % decrease in their ownership of the stock. The disclosure for this sale can be found here. Insiders have sold a total of 24,821 shares of company stock valued at $6,831,215 in the last quarter. 3.60% of the stock is currently owned by company insiders.

Institutional Inflows and Outflows

Several hedge funds have recently added to or reduced their stakes in the stock. Nisa Investment Advisors LLC raised its position in MongoDB by 428.0% in the fourth quarter. Nisa Investment Advisors LLC now owns 5,755 shares of the company’s stock valued at $1,340,000 after purchasing an additional 4,665 shares during the period. Sanders Morris Harris LLC purchased a new position in MongoDB in the fourth quarter valued at about $2,794,000. Covea Finance purchased a new position in MongoDB in the fourth quarter valued at about $3,841,000. Park Avenue Securities LLC raised its position in MongoDB by 24.3% in the fourth quarter. Park Avenue Securities LLC now owns 1,723 shares of the company’s stock valued at $401,000 after purchasing an additional 337 shares during the period. Finally, Assenagon Asset Management S.A. raised its position in MongoDB by 11,057.0% in the fourth quarter. Assenagon Asset Management S.A. now owns 296,889 shares of the company’s stock valued at $69,119,000 after purchasing an additional 294,228 shares during the period. 89.29% of the stock is owned by hedge funds and other institutional investors.

About MongoDB

(Get Free Report)

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

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

Posted on mongodb google news. Visit mongodb google news

MongoDB, Inc. (NASDAQ:MDB – Get Free Report) has been given a consensus rating of “Moderate Buy” by the thirty brokerages that are presently covering the company, Marketbeat.com reports. Two equities research analysts have rated the stock with a sell recommendation, four have assigned a hold recommendation, twenty-two have issued a buy recommendation and two have assigned a strong buy recommendation to the company. The average 1-year target price among brokers that have issued ratings on the stock in the last year is $363.93.

A number of brokerages recently issued reports on MDB. The Goldman Sachs Group increased their target price on shares of MongoDB from $340.00 to $390.00 and gave the stock a “buy” rating in a research report on Tuesday, December 10th. Robert W. Baird raised their price objective on shares of MongoDB from $380.00 to $390.00 and gave the stock an “outperform” rating in a report on Tuesday, December 10th. Canaccord Genuity Group boosted their target price on MongoDB from $325.00 to $385.00 and gave the stock a “buy” rating in a report on Wednesday, December 11th. Oppenheimer raised their price target on MongoDB from $350.00 to $400.00 and gave the company an “outperform” rating in a report on Tuesday, December 10th. Finally, Loop Capital boosted their price objective on MongoDB from $315.00 to $400.00 and gave the stock a “buy” rating in a research note on Monday, December 2nd.

Get Our Latest Stock Analysis on MongoDB

Insider Activity at MongoDB

In related news, CAO Thomas Bull sold 1,000 shares of MongoDB stock in a transaction dated Monday, December 9th. The stock was sold at an average price of $355.92, for a total transaction of $355,920.00. Following the completion of the transaction, the chief accounting officer now owns 15,068 shares in the company, valued at approximately $5,363,002.56. This trade represents a 6.22 % decrease in their ownership of the stock. The transaction was disclosed in a document filed with the SEC, which is accessible through this hyperlink. Also, Director Dwight A. Merriman sold 1,045 shares of the company’s stock in a transaction that occurred on Monday, January 13th. The shares were sold at an average price of $242.67, for a total value of $253,590.15. Following the completion of the sale, the director now owns 85,652 shares in the company, valued at $20,785,170.84. This represents a 1.21 % decrease in their position. The disclosure for this sale can be found here. Over the last quarter, insiders have sold 24,821 shares of company stock valued at $6,831,215. 3.60% of the stock is owned by insiders.

Institutional Inflows and Outflows

A number of institutional investors and hedge funds have recently bought and sold shares of MDB. Nisa Investment Advisors LLC lifted its holdings in shares of MongoDB by 3.8% during the third quarter. Nisa Investment Advisors LLC now owns 1,090 shares of the company’s stock worth $295,000 after buying an additional 40 shares in the last quarter. Hilltop National Bank raised its stake in MongoDB by 47.2% in the 4th quarter. Hilltop National Bank now owns 131 shares of the company’s stock worth $30,000 after purchasing an additional 42 shares in the last quarter. Tanager Wealth Management LLP boosted its holdings in MongoDB by 4.7% in the 3rd quarter. Tanager Wealth Management LLP now owns 957 shares of the company’s stock valued at $259,000 after purchasing an additional 43 shares during the period. Rakuten Securities Inc. grew its position in shares of MongoDB by 16.5% during the 3rd quarter. Rakuten Securities Inc. now owns 332 shares of the company’s stock worth $90,000 after purchasing an additional 47 shares in the last quarter. Finally, Prime Capital Investment Advisors LLC increased its holdings in shares of MongoDB by 5.2% during the third quarter. Prime Capital Investment Advisors LLC now owns 1,190 shares of the company’s stock worth $322,000 after purchasing an additional 59 shares during the period. Institutional investors and hedge funds own 89.29% of the company’s stock.

MongoDB Stock Performance

NASDAQ:MDB opened at $253.11 on Monday. The firm’s 50-day moving average is $278.24 and its 200-day moving average is $269.19. MongoDB has a 52-week low of $212.74 and a 52-week high of $509.62. The company has a market cap of $18.85 billion, a price-to-earnings ratio of -92.38 and a beta of 1.25.

MongoDB (NASDAQ:MDB – Get Free Report) last issued its earnings results on Monday, December 9th. The company reported $1.16 EPS for the quarter, topping the consensus estimate of $0.68 by $0.48. MongoDB had a negative return on equity of 12.22% and a negative net margin of 10.46%. The firm had revenue of $529.40 million during the quarter, compared to analysts’ expectations of $497.39 million. During the same period last year, the business earned $0.96 earnings per share. MongoDB’s quarterly revenue was up 22.3% compared to the same quarter last year. Equities analysts predict that MongoDB will post -1.79 EPS for the current year.

About MongoDB

(Get Free Report

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

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

Article originally posted on InfoQ. Visit InfoQ

This week’s Java roundup for January 13th, 2025 features news highlighting: JDK 24 in Rampdown Phase Two; Spring Framework 6.2.2; JobRunr 7.4.0; Micrometer Metrics 1.15.0-M1 and Micrometer Tracing 1.5.0-M1; and Infinispan joins the Commonhaus Foundation.

JDK 24

Build 32 of the JDK 24 early-access builds was made available this past week featuring updates from Build 31 that include fixes for various issues. Further details on this release may be found in the release notes.

As per the JDK 24 release schedule, Mark Reinhold, chief architect, Java Platform Group at Oracle, formally declared that JDK 24 has entered Rampdown Phase Two. This means that: no additional JEPs will be added for JDK 24; and there will be a focus on the P1 and P2 bugs which can be fixed via the Fix-Request Process. Late enhancements are still possible, with the Late-Enhancement Request Process, but Reinhold states that “the bar is now extraordinarily high.” The final set of 24 features for the GA release in March 2025 will include:

JDK 25

Build 6 of the JDK 25 early-access builds was also made available this past week featuring updates from Build 5 that include fixes for various issues. More details on this release may be found in the release notes.

For JDK 24 and JDK 25, developers are encouraged to report bugs via the Java Bug Database.

Jakarta EE

In his weekly Hashtag Jakarta EE blog, Ivar Grimstad, Jakarta EE Developer Advocate at the Eclipse Foundation, provided an update on Jakarta EE 11, writing:

The latest news about the refactoring of the TCK for Jakarta EE 11 is that the team is getting closer to releasing Jakarta EE Web Profile 11. There are just a small number of tests remaining to be refactored, and the rewriting of the TCK User Guide has started.

The discussions around Jakarta EE 12 are gaining momentum. Check out the EE12 labeled issues in the Jakarta EE Platform GitHub Issue Tracker. Feel free to add new issues or contribute to the discussions of those already created.

You can also join the Jakarta EE Future Directions interest group and participate in high-level discussions about how the platform should evolve. If you’re not able to join the bi-weekly calls (calendar), you can always join the mailing list and participate there as well.

The road to Jakarta EE 11 included four milestone releases, the release of Core Profile in December 2024, and the potential for release candidates as necessary before the GA releases of the Platform and Web Profile in 1Q2025.

Spring Framework

The release of Spring Framework 6.2.2 delivers bug fixes, improvements in documentation, dependency upgrades and new features such as: a change to the BeanOverrideHandler class to track only qualifier annotations to align with the Spring Boot QualifierDefinition class; and elevate the @MockitoBean annotation to support both fields and types to align with the now-deprecated Spring Boot @MockBean annotation. This version will be included in the upcoming releases of Spring Boot 3.4.2 and 3.5.0-M1. More details on this release may be found in the release notes.

Spring Data 2024.1.2 and 2024.0.8, both service releases, ship with bug fixes, dependency upgrades and and respective dependency upgrades to sub-projects such as: Spring Data Commons 3.4.2 and 3.3.8; Spring Data MongoDB 4.4.2 and 4.3.8; Spring Data Elasticsearch 5.4.2 and 5.3.8; and Spring Data Neo4j 7.4.2 and 7.3.8. These versions will be included in the upcoming releases of Spring Boot and 3.4.2 and 3.3.8.

The Spring AI MCP team has released version 0.5.0 to provide new features such as: enhancements to the transport layer with a new HttpServletSseServerTransport class and a blocking queue-based implementation of the Spring Framework SseEmitter.SseEventBuilder interface; and a new Bill of Materials. More details on this release may be found in the release notes.

Quarkus

Quarkus 3.17.7, the sixth maintenance release (3.17.1 was skipped due to a regression), features notable changes such as: a resolution to an issue when a method is annotated with @PermissionsAllowed with multiple values, the parameters in the @PermissionChecker annotation is not correctly matched; and a switch to execute the MongoDB Connection Health Check on startup instead of when the application tries to store the first record in the database. More details on this release may be found in the changelog.

Micrometer

The first milestone release of Micrometer Metrics 1.15.0 provides bug fixes, improvements in documentation, dependency upgrades and new features such as: use of the failWithActualExpectedAndMessage(), defined in the AssertJ AbstractAssert class, where possible; and a consistent use of the Java String methods, toLowerCase() and toUpperCase(), with the Java Locale.ROOT for improved security. Further details on these releases may be found in the release notes.

Similarly, versions 1.14.3 and 1.13.10 of Micrometer Metrics ship with dependency upgrades and resolutions to notable issues such as: a NullPointerException when applying aspects on methods that return a CompletableFuture; and a performance regression from the remove() method, defined in the MeterRegistry class, with a significant amount of registered meters. More details on these releases may be found in the release notes for version 1.14.3 and version 1.13.10.

The first milestone release of Micrometer Tracing 1.5.0 delivers bug fixes, dependency upgrades and new features: avoid creating superfluous copies of instances of the OtelSpan class; and the addition of local service name setting and retrieving for FinishedSpan to complement the remote service name. More details on this release may be found in the release notes.

Similarly, versions 1.4.2 and 1.3.8 of Micrometer Tracing provide: dependency upgrades to Micrometer Metrics 1.14.3 and 1.13.10, respectively, and a resolution to a NullPointerException when an instance of the OtelTraceContextBuilder class set the parentId and sampled fields as @Nullable to align with the same fields in the TraceContext interface. More details on these releases may be found in the release notes for version 1.4.2 and version 1.3.8.

Piranha Cloud

The release of Piranha 25.1.0 delivers many dependency upgrades and notable changes such as: a new SecurityConstraint class to complement their own SecurityManager API; and a resolution to an instance of the GrizzlyHttpServer class to not indefinitely suspend on asynchronous requests. Further details on this release may be found in the release notes, documentation and issue tracker.

Project Reactor

Project Reactor 2024.0.2, the second maintenance release, providing dependency upgrades to reactor-core 3.7.2, reactor-netty 1.2.2, reactor-pool 1.1.1. There was also a realignment to version 2024.0.2 with the reactor-addons 3.5.2, reactor-kotlin-extensions 1.2.3 and reactor-kafka 1.3.23 artifacts that remain unchanged. More details on this release may be found in the changelog.

Similarly, Project Reactor 2023.0.14, the fourteenth maintenance release, provides dependency upgrades to reactor-pool 1.0.9 and reactor-netty 1.1.26. There was also a realignment to version 2023.0.12 with the reactor-core 3.6.13, reactor-addons 3.5.2, reactor-kotlin-extensions 1.2.3 and reactor-kafka 1.3.23 artifacts that remain unchanged. Further details on this release may be found in the changelog.

JobRunr

The release of JobRunr 7.4.0 ships with: support for JDK 24, Spring Boot 3.4 and Kotlin 2.1. Enhancements include: wait for all instances of the RecurringJobPostProcessor class to finish before starting Spring Boot and the BackgroundJobServer class; and ensure that the correct casing is used in all SQL scripts. There was also a resolution to skip collection validation if an instance of the MongoDBStorageProvider class is configured with the NO_VALIDATE option. More details on this release may be found in the release notes.

OpenXava

The release of OpenXava 7.4.5 provides bug fixes, improvements in documentation, dependency upgrades and enhancements such as: a new filterByContentInAnyProperty() method, added to the Tab class, to filter a string value in any column; and a new isJavaIdentifier() method, added to the Strings utility class, to determine the existence of a Java identifier. More details on this release may be found in the release notes.

Commonhaus Foundation

The Commonhaus Foundation, a non-profit organization dedicated to the sustainability of open source libraries and frameworks, has announced that Infinispan has joined the foundation this past week. In a blog post published in mid-January 2025, Tristan Tarrant, Senior Principal Software Engineer at Red Hat and Infinispan Project Lead, described their rationale to transition to the foundation, writing:

Commonhaus just ticks all the right boxes for us: its lightweight governance is ideal. We get all the benefits of being part of an awesome foundation, with all the benefits of running the project on our own terms.

Commonhaus is also the home to a number of “friend projects”: Hibernate, Quarkus, Debezium, Jackson all play a key role in our software and it’s great that we share a “common home.”

Other notable projects that have joined the foundation include: JReleaser, JBang, OpenRewrite, SDKMAN, EasyMock, Objenesis and Feign.

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MongoDB (NASDAQ:MDB) was named a “top pick” by Morgan Stanley in its 2025 generative artificial intelligence outlook, but Fortinet (NASDAQ:FTNT) was removed as a cybersecurity “top pick” due to its late 2024 stock price increase.

“From the perspective of a

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

Posted on mongodb google news. Visit mongodb google news

Cantor Fitzgerald upgraded shares of MongoDB (NASDAQ:MDB – Free Report) to a strong-buy rating in a research report report published on Thursday, MarketBeat Ratings reports. They currently have $344.00 target price on the stock.

Other equities research analysts also recently issued research reports about the stock. Stifel Nicolaus lifted their price target on shares of MongoDB from $325.00 to $360.00 and gave the stock a “buy” rating in a research note on Monday, December 9th. Truist Financial reaffirmed a “buy” rating and set a $400.00 price target (up previously from $320.00) on shares of MongoDB in a research note on Tuesday, December 10th. DA Davidson lifted their price target on shares of MongoDB from $340.00 to $405.00 and gave the stock a “buy” rating in a research note on Tuesday, December 10th. Needham & Company LLC lifted their price target on shares of MongoDB from $335.00 to $415.00 and gave the stock a “buy” rating in a research note on Tuesday, December 10th. Finally, Piper Sandler reaffirmed an “overweight” rating and set a $425.00 price target on shares of MongoDB in a research note on Tuesday, December 10th. Two analysts have rated the stock with a sell rating, four have assigned a hold rating, twenty-two have given a buy rating and two have issued a strong buy rating to the stock. Based on data from MarketBeat, the company currently has a consensus rating of “Moderate Buy” and an average price target of $363.93.

Read Our Latest Stock Report on MDB

MongoDB Stock Up 2.1 %

Shares of MDB opened at $253.11 on Thursday. The firm has a market cap of $18.85 billion, a PE ratio of -92.38 and a beta of 1.25. The company’s fifty day moving average is $278.24 and its two-hundred day moving average is $269.17. MongoDB has a fifty-two week low of $212.74 and a fifty-two week high of $509.62.

MongoDB (NASDAQ:MDB – Get Free Report) last announced its quarterly earnings results on Monday, December 9th. The company reported $1.16 earnings per share for the quarter, beating analysts’ consensus estimates of $0.68 by $0.48. The company had revenue of $529.40 million for the quarter, compared to analyst estimates of $497.39 million. MongoDB had a negative net margin of 10.46% and a negative return on equity of 12.22%. MongoDB’s revenue for the quarter was up 22.3% on a year-over-year basis. During the same quarter last year, the business earned $0.96 EPS. As a group, equities research analysts anticipate that MongoDB will post -1.79 earnings per share for the current year.

Insider Transactions at MongoDB

In related news, insider Cedric Pech sold 287 shares of the stock in a transaction that occurred on Thursday, January 2nd. The shares were sold at an average price of $234.09, for a total transaction of $67,183.83. Following the completion of the sale, the insider now directly owns 24,390 shares of the company’s stock, valued at $5,709,455.10. This represents a 1.16 % decrease in their ownership of the stock. The transaction was disclosed in a document filed with the Securities & Exchange Commission, which is available at the SEC website. Also, CFO Michael Lawrence Gordon sold 5,000 shares of the stock in a transaction that occurred on Monday, December 16th. The shares were sold at an average price of $267.85, for a total transaction of $1,339,250.00. Following the sale, the chief financial officer now directly owns 80,307 shares of the company’s stock, valued at approximately $21,510,229.95. This represents a 5.86 % decrease in their ownership of the stock. The disclosure for this sale can be found here. In the last three months, insiders sold 24,821 shares of company stock valued at $6,831,215. Company insiders own 3.60% of the company’s stock.

Institutional Investors Weigh In On MongoDB

A number of large investors have recently made changes to their positions in the company. Jennison Associates LLC increased its stake in MongoDB by 23.6% in the 3rd quarter. Jennison Associates LLC now owns 3,102,024 shares of the company’s stock valued at $838,632,000 after buying an additional 592,038 shares during the period. Geode Capital Management LLC increased its stake in MongoDB by 2.9% in the 3rd quarter. Geode Capital Management LLC now owns 1,230,036 shares of the company’s stock valued at $331,776,000 after buying an additional 34,814 shares during the period. Westfield Capital Management Co. LP increased its stake in MongoDB by 1.5% in the 3rd quarter. Westfield Capital Management Co. LP now owns 496,248 shares of the company’s stock valued at $134,161,000 after buying an additional 7,526 shares during the period. Thrivent Financial for Lutherans increased its stake in MongoDB by 1,098.1% in the 2nd quarter. Thrivent Financial for Lutherans now owns 424,402 shares of the company’s stock valued at $106,084,000 after buying an additional 388,979 shares during the period. Finally, Holocene Advisors LP increased its stake in MongoDB by 22.6% in the 3rd quarter. Holocene Advisors LP now owns 362,603 shares of the company’s stock valued at $98,030,000 after buying an additional 66,730 shares during the period. Institutional investors and hedge funds own 89.29% of the company’s stock.

About MongoDB

(Get Free Report)

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

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

Posted on mongodb google news. Visit mongodb google news

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

Posted on mongodb google news. Visit mongodb google news

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The whispers are turning into roars.

Artificial intelligence isn’t science fiction anymore.

It’s the revolution reshaping every industry on the planet.

From driverless cars to medical breakthroughs, AI is on the cusp of a global explosion, and savvy investors stand to reap the rewards.

Here’s why this is the prime moment to jump on the AI bandwagon:

Exponential Growth on the Horizon: Forget linear growth – AI is poised for a hockey stick trajectory.

Imagine every sector, from healthcare to finance, infused with superhuman intelligence.

We’re talking disease prediction, hyper-personalized marketing, and automated logistics that streamline everything.

This isn’t a maybe – it’s an inevitability.

Early investors will be the ones positioned to ride the wave of this technological tsunami.

Ground Floor Opportunity: Remember the early days of the internet?

Those who saw the potential of tech giants back then are sitting pretty today.

AI is at a similar inflection point.

We’re not talking about established players – we’re talking about nimble startups with groundbreaking ideas and the potential to become the next Google or Amazon.

This is your chance to get in before the rockets take off!

Disruption is the New Name of the Game: Let’s face it, complacency breeds stagnation.

AI is the ultimate disruptor, and it’s shaking the foundations of traditional industries.

The companies that embrace AI will thrive, while the dinosaurs clinging to outdated methods will be left in the dust.

As an investor, you want to be on the side of the winners, and AI is the winning ticket.

The Talent Pool is Overflowing: The world’s brightest minds are flocking to AI.

From computer scientists to mathematicians, the next generation of innovators is pouring its energy into this field.

This influx of talent guarantees a constant stream of groundbreaking ideas and rapid advancements.

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The future is powered by artificial intelligence, and the time to invest is NOW.

Don’t be a spectator in this technological revolution.

Dive into the AI gold rush and watch your portfolio soar alongside the brightest minds of our generation.

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Our research team has identified a hidden gem – an AI company with cutting-edge technology, massive potential, and a current stock price that screams opportunity.

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