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Java News Roundup: Payara Platform, Jakarta EE 11 Specs, Open Liberty, Micronaut, Quarkus

MMS Founder
MMS Michael Redlich

Article originally posted on InfoQ. Visit InfoQ

This week’s Java roundup for June 17th, 2024 features news highlighting: the Payara Platform release for June 2024; all 16 Jakarta EE 11 specifications having passed their respective reviews; Open Liberty 24.0.0.6; Micronaut 4.5.0; and two Quarkus point releases.

OpenJDK

Christian Stein, Principal Member of Technical Staff at Oracle, has announced that version 7.4.0 of the Regression Test Harness for the JDK, jtreg, released in May 2024, is now the default version for the JDK 24 early-access builds.

JDK 23

Build 28 of the JDK 23 early-access builds was made available this past week featuring updates from Build 27 that include fixes for various issues. Further details on this release may be found in the release notes, and details on the new JDK 23 features may be found in this InfoQ news story.

JDK 24

Build 3 of the JDK 24 early-access builds was also made available this past week featuring updates from Build 2 that include fixes for various issues. Release notes are not yet available.

For JDK 23 and JDK 24, developers are encouraged to report bugs via the Java Bug Database.

Jakarta EE

The final two specifications targeted for Jakarta EE 11, Jakarta Authentication 3.1 and Jakarta Security 4.0, have passed their respective release reviews this past week. This means that all 16 specifications updated for Jakarta EE 11 are now complete!

In his weekly Hashtag Jakarta EE blog, Ivar Grimstad, Jakarta EE Developer Advocate at the Eclipse Foundation, explained that efforts are focused on finalizing the TCK and completing the required changes in the Jakarta EE Platform, Web Profile and Core Profile before the final GA release of Jakarta EE 11.

Spring Framework

It was a busy week over at Spring as the various teams have delivered numerous milestone and point releases on Spring Boot, Spring Framework, Spring Security, Spring Authorization Server, Spring for GraphQL, Spring Session, Spring Integration, Spring Modulith, Spring AMQP, Spring for Apache Kafka, Spring for Apache Pulsar and Spring Tools. More details may be found in this InfoQ news story.

Payara

Payara has released their June 2024 edition of the Payara Platform that includes Community Edition 6.2024.6 and Enterprise Edition 6.15.0 and Enterprise Edition 5.64.0. All three editions feature: optimized Multi-Release JAR class loading for faster application startup and operation; and an improved thread expiration validation to resolve an inconsistent session timeout when using Session Replication with the --lite command line option.

There was also an upgrade to Payara Security Connectors 3.1.1 and 2.7.1 for the version 6 release train, Community and Enterprise, and the version 5 release train, respectively.

Further details on these releases may be found in the release notes for Community Edition 6.2024.6 and Enterprise Edition 6.15.0 and Enterprise Edition 5.64.0.

Helidon

Helidon 4.0.10, the tenth maintenance release, provides notable changes such as: a new inner class, MethodStateCache, defined in the MethodInvoker class that implements a new method caching strategy in fault tolerance; a resolution to handle an invalid end-of-line when parsing HTTP headers and add the appropriate tests; and improvements in validating JWT tokens. More details on this release may be found in the changelog.

Quarkus

Quarkus 3.11.2, the second maintenance release, ships with resolutions to notable issues such as: a NullPointerException due to the setListeners() method, defined in the ShutdownRecorder class, not being called in the when QUARKUS_INIT_AND_EXIT is used; a misspelled URL for a JQuery WebJar resource throws an StringIndexOutOfBoundsException instead of redirecting to an HTTP 404 status code; and a failure in using the Gradle quarkusDev parameter when usage analytics are enabled. Further details on this release may be found in the changelog.

Two days after the release of Quarkus 3.11.2, Quarkus 3.11.3, the third maintenance release, provides dependency upgrades and notable changes such as: compatibility with Maven Daemon (mvnd) 1.0; support for the ISO 8601 date/time format in the HTTP access logs; and a resolution to various issues with the lastModified property using the Quarkus REST extension. More details on this release may be found in the changelog.

Open Liberty

IBM has released version 24.0.0.6 of Open Liberty featuring: faster startup of Spring Boot applications using Spring Boot 3.0 InstantOn with CRaC; and InstantOn support for the Jakarta Messaging specification with IBM MQ and JCache Session Persistence feature.

This release also addresses CVE 2024-22354, a vulnerability affecting IBM WebSphere Application Server 8.5 and 9.0, and IBM WebSphere Application Server Liberty 17.0.0.3 through 24.0.0.5, that are vulnerable to an XML External Entity Injection (XXE) attack when processing XML data. A remote attacker could exploit this vulnerability to expose sensitive information, consume memory resources, or to conduct a server-side request forgery attack.

Micronaut

The Micronaut Foundation has released version 4.5.0 of the Micronaut Framework featuring Micronaut Core 4.5.3, bug fixes, improvements in documentation and updates to modules: Micronaut Data, Micronaut Servlet and Micronaut Micrometer.

This release also introduces new modules: Micronaut JSON Schema, for generating JSON schema definitions from classes at build time; Micronaut Sourcegen, for writing source generators and generating Builder and Wither classes; and Micronaut Guice, that allows the import of existing Guice modules.

Further details on this release may be found in the release notes.

Apache Software Foundation

The twenty-first milestone release of Apache Tomcat 11.0.0 along with point releases, 10.1.25 and 9.0.90, all feature bug fixes and notable changes such as: ensure that static resources deployed via a JAR file remain accessible when the context is configured to use a bloom filter; the default value of the discardFacades attribute, defined in the Connector class, is now true for improved safety; and an update to Commons Daemon 1.4.0. More details on these releases may be found in the release notes for version 11.0.0-M11, version 10.1.25 and version 9.0.90.

The release of Apache Camel 3.21.5 delivers bug fixes and improvements such as: removal of the now deprecated fireEvent() method from the Jakarta CDI BeanManager interface; and an improved JMSCorrelationID message header, defined in the Jakarta Messaging Message interface, to handle message brokers that have bugs. This is the last planned patch release for Camel 3.21 release train. Further details on this release may be found in the release notes.

The release of Apache Maven 3.9.8 ships with bug fixes, dependency upgrades and improvements such as: display the reason(s) why a model builder discards a model; an improvement to the SimplexTransferListener class to handle absent source/target files; and the list of plugins in the validation report are now sorted in alphabetical order. More details on this release may be found in the release notes.

JobRunr

Version 7.2.1 of JobRunr, a library for background processing in Java that is distributed and backed by persistent storage, has been released that primarily fixes a ConcurrentModificationException that may be thrown due to concurrent updates to an instance of a Job class. This completes the transition from Kotlin 1.7 to Kotlin 2.0 by correctly naming the necessary artifact. This version also provides an enhancement that validates an implementation of the JobRequest interface when using the JobBuilder or the RecurringJobBuilder classes. Further details on this release may be found in the release notes.

JHipster

The release of JHipster Lite 1.11.0 ships with bug fixes, dependency upgrades and new features/enhancements such as: a new ElementReplacer interface dedicated to insert text at the end of file; and an improved JHipster Lite logging. More details on this release may be found in the release notes.

Infinispan

Infinispan 15.0.5.Final, the fifth maintenance release, delivers notable changes such as: an optimized lookupResource() method, defined in the ResourceManagerImpl class, for improved processing of resources; a file cleanup in the RocksDB cache store before executing tests; and return an HTTP 400 (Bad Request) response code if a user requests initialization of an internal cache.

OpenXava

The release of OpenXava 7.3.3 ships with bug fixes, dependency upgrades and Maven improvements with new archetypes, openxava-project-management-archetype and openxava-crm-archetype, available in both English and Spanish. Further details on this release may be found in the release notes.

Keycloak

Keycloak 25.0.1, the first maintenance release, provides bug fixes and enhancements: use of a proper Apache FreeMarker template for the refurbished Account and Admin Consoles; and enhanced masking in the CLI with values passed using the --config-keystore parameter.

Gradle

The first release candidate of Gradle 8.9 delivers: an improved error and warning reporting for variant issues during dependency resolution; structural details exposed of Java compilation errors for IDE integrators, allowing for easier analysis and resolving issues; and the ability to display more detailed information about JVMs used by Gradle. More details on this release may be found in the release notes.

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Meet the Database Built for Modern-Day Pressures (And Your AI Dreams Too) | CDOTrends

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Ever wondered how China’s biggest online shopping event, Double 11 (Singles’ Day), doesn’t melt down under the strain of millions of simultaneous transactions? The answer lies in a database called OceanBase.

Now, the namesake company is thinking big. It sees the advantages of bringing its database’s ability to handle Alipay’s extreme demands to the global financial sector, drowning high data loads, volumes and real-time transactions. And it is correct.

The origin story: Born from a scaling crisis

To understand OceanBase, we must first dial back to 11 years ago. Alipay, already a major third-party payment platform, was creaking at its seams. The problem: it’s a monolithic Oracle database.

Like Oracle’s monolithic architecture, legacy systems couldn’t cope with the data volume and request load. They simply weren’t built to scale or gracefully handle multi-region disaster recovery.

But it was not the only concern. “The technical challenges weren’t just about the database,” explains Ted Bai, head of solution architecture for the international business at OceanBase. “The entire infrastructure was a massive fabric under extreme stress.”

For Alipay to dream big, it had to invest more money in hardware and database upgrades. It knew it had to go back to the drawing board. The solution that emerged was OceanBase.

Built from the ground up as a fully distributed database, it shattered performance bottlenecks and enabled seamless scaling.

This wasn’t just theory, either. In 2019, OceanBase made history as the first distributed database to ace the TPC-C benchmark with its V2.2 entry, beating out Oracle using standard x86 servers instead of high-end hardware with a score of 60 million tpmC. In 2020, it set a new record with 700 million tpmC.

OceanBase was the first distributed database to enter the coveted TPC-C rankings, often seen as a Formula 1 race for databases, and the first Chinese database to do so. The leap in performance also highlighted the power of distributed architecture in handling high-volume financial transactions.

The stranglehold of the monolithic databases was now splayed open.

Open source, but not quite forked

Since becoming Alipay’s database of choice, OceanBase has grown into a commercial product. It now serves various industries, from banks and insurers to telecom giants and even the Chinese government.

The architecture has matured to meet the unique needs of each sector, including cloud-based deployments on platforms like AWS and GCP.

OceanBase is open source, a significant difference from the rest of the TPC-C competitors. This decision has been crucial for broader adoption.

“To make it a truly global product, it had to be open source,” Bai says. This fosters community engagement and accelerates innovation, while the enterprise version focuses on Oracle compatibility and advanced security features, catering to the rigorous demands of financial institutions.

Where OceanBase excels

Ask any database engineer, and they will tell you that performance alone is insufficient. It also had to be secure, cost-efficient and flexible.

For financial institutions, security isn’t a feature; it’s table stakes. OceanBase offers transparent data encryption (TDE SSL), granular access controls, and comprehensive audit trails. Its SOC2 and PCI DSS certifications prove it means business, adds Bai.

The bottom line also matters. OceanBase’s multi-tenancy design lets you consolidate multiple databases into a single cluster, maximizing resource utilization, explains Bai. Add to that an innovative storage engine with impressive compression rates, and you’ve got a recipe for significant cost savings.

Lastly, OceanBase caters to a wide range of deployment scenarios, whether you’re fully on-premises, cloud-native, or somewhere in between. This multi-infrastructure approach means you’re not locked into a single vendor, allowing you to choose what works best for your organization.

These features are helping OceanBase make significant inroads across the financial services sector, from established insurers to leading-edge digital banks and Web3 companies.

Why customers are switching

Just ask China Life Insurance, one of the biggest insurers in China.

They migrated hundreds of Oracle database clusters to OceanBase, driven by performance bottlenecks and ballooning costs.

Another primary reason was that they were refactoring their next-generation applications to follow a distributed and microservice design. OceanBase’s native distributed architecture made it the ideal choice.

OceanBase’s seamless Oracle compatibility through features commonly used in the latter made this transition remarkably smooth.

Bai also noted that digital banks are beginning to deploy OceanBase as they shift toward a distributed architecture. The database’s ability to perform rolling upgrades was a major plus compared to traditional databases, which need maintenance shutdowns.

Meanwhile, Bai shared that the company works closely with a Web3 company because of scalability and reliability. In particular, the optimized use of Multi-Paxos allows it to implement multi-replica data synchronization and cluster high availability.

Essentially, Multi-Paxos helps the company avoid split brains: when a network failure at the primary causes a company to make a disaster recovery decision to spin up the standby node, leading to multiple writes and reduced data correctness.

The road ahead

OceanBase isn’t done with its journey. According to Bai, it’s just starting.

Their roadmap includes developments like full-fledged Hybrid Transactional/Analytical Processing (HTAP) capabilities, meaning you can run transactional workloads and complex analytics within the same database. Bai also noted that the company is venturing into the realm of NoSQL with their KV store, providing a high-performance alternative to systems like HBase.

However, the truly groundbreaking development is integrating vector database plugins for generative AI applications, positioning OceanBase not just as a data store but as a powerful engine for FSI’s AI-driven future.

Let Ted Bai and leading FSI companies explain how OceanBase is reshaping the future of financial data management one dataset at a time. Join our upcoming luncheon “Decoding the Data Advantage: How Modern Databases Drive Financial Innovation” on July 10, 2024. To register, email [email protected].

Image credit: iStockphoto/Shutthiphong Chandaeng

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Software-update: DBeaver 24.1.1 – Computer – Downloads – Tweakers

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DBeaver logo (79 pix) Versie 24.1.1 van DBeaver is uitgekomen. Met dit programma kunnen databases worden beheerd. Het kan onder andere query’s uitvoeren en data tonen, filteren en bewerken. Ondersteuning voor de bekende databases, zoals MySQL, Oracle, DB2, SQL Server, PostgreSQL, Firebird en SQLite, is aanwezig. Het is verkrijgbaar in een opensource-CE-uitvoering en drie verschillende commerciële uitvoeringen. Deze voegen onder meer ondersteuning voor verschillende nosql-databases toe, zoals MongoDB, Apache Cassandra en Apache Hive, en bevatten verder extra plug-ins en jdbc-drivers. De changelog voor deze uitgave ziet er als volgt uit:

Changes in DBeaver version 24.1.1:

  • AI: GPT-4o model was supported
  • SQL Editor:
    • Auto-save editor setting was enabled by default
    • ILIKE keyword was supported
    • Support for unsigned numbers in GROUP BY clause was added
    • Syntax highlighting and outline viewer support parameter changes in SQL Processing settings
    • Links to the SQL Editor and Visual Query Builder were added to the Context menu
    • Buttons in the right toolbar were renamed
    • Print mode in ER Diagrams and Execution plan was fixed
    • Saving Diagram outside the workspace was fixed
  • Data Editor:
    • Information about table unique key is displayed in the grid
    • BETWEEN and LIKE keywords were added to filetr autocomplection
  • Data transfer:
    • Export of data with backticks was fixed (thanks to @diashalabi)
    • Export of binary columns in JSON format was fixed
  • General:
    • Ability to restore default settings was fixed in SQL Editor Formatting settings, SQL Editor settings, SQL Editor Commit type, Error handling settings, and Hex Editor default width
    • Creating a view from the UI was fixed
    • Buttons display when creating new dashboard was fixed
    • New table creation was fixed
  • The following drivers were updated:
    • Firebird to version 5.0.4
    • Redshift to 2.1.0.29
    • Informix to 4.50.JC10W1
  • Databases:
    • Altibase: Search for server properties was added (thanks to @zennken)
    • BigQuery: Ability to see bytes billed in Statistics panel in the Data Editor was added
    • Cubrid:
      • Execution plan coloring was enhanced (thanks to @hwany7seo)
      • Ability to create a table via UI was added (thanks to @longhaseng52)
      • DDL display for a view was added together with the ability to display the list of indexes, synonyms, serials, triggers, and procedures (thanks to @longhaseng52)
      • Ping query for keep connection alive was added (thanks to @rathana-pvs)
      • Partition display was added (thanks to @rathana-pvs)
    • PostgreSQL:
      • If database field in connection settings is empty, user name is used as a database name
      • Work of ‘Use fully qualified names’ setting was fixed for views
    • Redis: Key settings were fixed
    • Redshift: Constraint creation was fixed
    • Vertica: Stored procedures handling in SQL Editor was fixed

DBeaver

Versienummer 24.1.1
Releasestatus Final
Besturingssystemen Windows 7, Linux, macOS, Windows 8, Windows 10, Windows 11
Website DBeaver
Download https://dbeaver.io/download/
Licentietype Voorwaarden (GNU/BSD/etc.)

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Spring Ecosystem Releases Focus on Spring Boot, Spring Security and Spring Modulith

MMS Founder
MMS Michael Redlich

Article originally posted on InfoQ. Visit InfoQ

There was a flurry of activity in the Spring ecosystem during the week of June 17th, 2024 highlighting point releases of: Spring Boot 3.3.1 and 3.2.7; Spring Security 6.3.1, 6.2.5 and 5.8.13; Spring Session 3.3.1 and 3.2.4; and Spring Modulith 1.2.1, 1.1.6 and 1.0.9.

Spring Boot

The release of Spring Boot versions 3.3.1 and 3.2.7 deliver improvements in documentation, dependency upgrades and resolutions to notable issues such as: an IllegalArgumentException when trying to use an instance of the Tomcat Http11Nio2Protocol class with Spring Boot-configured SSL; and an instance of the DataSourceProperties class fails to bind if the java.sql module isn’t included. Further details on these releases may be found in the release notes for version 3.3.1 and version 3.2.7.

Spring Framework

Spring Framework 6.1.10, the tenth maintenance release, provides bug fixes (that include regressions from version 6.1.9), improvements in documentation and new features: an instance of the PersistenceExceptionTranslationInterceptor class now defensively retrieves PersistenceExceptionTranslator interface beans to cover scenarios where the translator has not been initialized before shutdown; and support for all “connection reset” exception phrases from the DisconnectedClientHelper class. This version is included in the release of Spring Boot 3.2.7 and 3.3.1. More details on this release may be found in the release notes.

Spring Security

Versions 6.3.1, 6.2.5 and 5.8.13 of Spring Security have been released that ship with bug fixes, dependency upgrades, build updates and new features such as: enhanced logging from within the check() method, defined in the RequestMatcherDelegatingAuthorizationManager class, that did not provide useful information; and an update to the ldap.adoc file to include the required dependencies to avoid issues that developers have experienced while setting up LDAP. Further details on these releases may be found in the release notes for version 6.3.1, version 6.2.5 and version 5.8.13.

Spring Authorization Server

Versions 1.3.1 and 1.2.5 of Spring Authorization Server have been released featuring dependency upgrades and resolutions to issues: a ClassNotFoundException due to AOT hints preventing compilation when using JdbcOAuth2AuthorizationService or JdbcRegisteredClientRepository classes; and authentication for an X509 client certificate enforces the value assigned to the client_id field in the YAML configuration file without first checking on client authentication method. More details on these releases may be found in the release notes for version 1.3.1 and version 1.2.5.

Spring for GraphQL

Versions 1.3.1 and 1.2.7 of Spring for GraphQL have been released providing bug fixes, improvements in documentation, dependency upgrades and new features: support for returning instances of the Reactor Flux class from methods annotated with @EntityMapping to complement existing support for List, Mono and CompletableFuture; and allow the use of GraphQL Java 21.x in the Spring for GraphQL 1.2 release train. These versions are included in the release of Spring Boot 3.2.7 and 3.3.1, respectively. Further details on these releases may be found in the release notes for version 1.3.1 and version 1.2.7.

Spring Session

Versions 3.3.1 and 3.2.4 of Spring Session have been released with dependency upgrades and a new feature that resolves an issue in which a default implementation of the UserDetails interface, User, is returned instead of a user-defined custom implementation. More details on these releases may be found in the release notes for version 3.3.1 and version 3.2.4.

Spring Integration

Versions 6.3.1 and 6.2.6 of Spring Integration have been released featuring bug fixes, improvements in documentation, dependency upgrades and a new feature that provides the ZeroMqMessageHandler class with an optional topic for distributing messages into subscriptions that must be wrapped with an additional empty frame. This would complement the existing default topic. Further details on these releases may be found in the release notes for version 6.3.1 and version 6.2.6.

Spring Modulith

Versions 1.2.1 and 1.1.6 of Spring Modulith have been released featuring: an improved configuration of the ApplicationModuleDetectionStrategy interface via the spring.modulith.detection-strategy property that will accept values direct-sub-packages (default) or explicitly-annotated; a resolution to named interface detection accidentally picking up nested declarations in a nested interfaces scenario; and dependency upgrades to Spring Boot 3.3.1 and 3.2.7, respectively. More details on these releases may be found in the release notes for version 1.2.1 and version 1.1.6.

Spring AMQP

Version 3.1.6 of Spring AMQP has been released featuring dependency upgrades and resolutions to issues: the release() method, defined in the ActiveObjectCounter class, is unreachable due to the SimpleMessageListenerContainer class not having released the consumer variable; and elimination of an interrupted thread after performing target logic by moving the cancelTimeoutTaskIfAny() method, defined in the RabbitFuture class, into a finally block. Further details on this release may be found in the release notes.

Spring for Apache Kafka

Versions 3.2.1 and 3.1.6 of Spring for Apache Kafka have been released providing bug fixes, dependency upgrades and a new feature that adds tracing headers, now mapped to a string, in the AbstractKafkaHeaderMapper class after the migration from Sleuth to Micrometer. These versions are included in the release of Spring Boot 3.2.7 and 3.3.1, respectively. More details on these releases may be found in the release notes for version 3.2.1 and version 3.1.6.

Spring for Apache Pulsar

Versions 1.1.1 and 1.0.7 of Spring for Apache Pulsar have been released featuring numerous dependency upgrades that include: Micrometer Metrics 1.13.1 and 1.12.7, respectively; Reactive Client for Apache Pulsar 0.5.6; and Spring Framework 6.1.9. These versions are included in the release of Spring Boot 3.2.7 and 3.3.1, respectively. Further details on these releases may be found in the release notes for version 1.1.1 and version 1.0.7.

Spring Tools

Less than a week after the release of version 4.23.0, version 4.23.1 of Spring Tools has been released to deliver important fixes such as: adding preferences/settings for enabling/disabling JPQL, HQL and SQL syntax validation as well as severities for syntax problems inside Spring Data queries that were missing; and a StackOverflowException from within the AnnotationHierarchies class upon opening a Spring Boot project in VSCode. More details on this release may be found in the release notes.

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Public Cloud Non-Relational Databases – TIMC

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Public Cloud Non-Relational Databases & NoSQL Database Market is growing at a Robust CAGR during the forecast period 2024-2031. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.

Public Cloud Non-Relational Databases & NoSQL Database Market research report highlights the recent market scenario, growth in the past few years, and opportunities present for manufacturers in the future. In this research for the completion of both primary and secondary details, methods and tools are used. Also, investments instigated by organizations, government, non-government bodies, and institutions are projected in details for better understanding about the market.

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Top Key Players in Global Public Cloud Non-Relational Databases & NoSQL Database Market:

IBM, MongoDB Inc, AWS(Amazon Web Services), Apache Software Foundation, Neo Technologies (Pty) Ltd, InterSystems, Google, Oracle Corporation, Teradata, DataStax, Software AG

The study presents an evaluation of the factors that are expected to inhibit or boost the progress of the global Public Cloud Non-Relational Databases & NoSQL Database market. The global Public Cloud Non-Relational Databases & NoSQL Database market has been examined thoroughly on the basis of key criteria such as end user, application, product, technology, and region. An analysis has been provided in the report of the key geographical segments and their share and position in the market. The estimated revenue and volume growth of the global Public Cloud Non-Relational Databases & NoSQL Database market has also been offered in the report.

Public Cloud Non-Relational Databases & NoSQL Database Market Growth analysis:

The global Public Cloud Non-Relational Databases & NoSQL Database Market growth analysis involves a comprehensive examination of trends, patterns, and factors influencing the expansion of a specific market over time. By gathering and analyzing data from various sources, including market research reports, industry publications, and competitive intelligence, we can identify key drivers of growth, assess historical performance, and forecast future trajectories. Through segmentation analysis, trend monitoring, and competitive landscape assessment, we gain valuable insights into market dynamics and opportunities for expansion.

The  global Public Cloud Non-Relational Databases & NoSQL Database Market report provides insights on the following pointers:
• Market Penetration: Comprehensive information on the product portfolios of the top players in the Public Cloud Non-Relational Databases & NoSQL Database Market.
• Product Development/Innovation: Detailed insights on the upcoming technologies, R&D activities, and product launches in the market.
• Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market.
• Market Development: Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies.
• Market Diversification: Exhaustive information about new products, untapped geographies, recent developments, and investments in the Public Cloud Non-Relational Databases & NoSQL Database Market.

Global Public Cloud Non-Relational Databases & NoSQL Database Market Segmentation:

Market Segmentation: By Type

Key Value Storage Database
Column Storage Database
Document Database
Graph Database

Market Segmentation: By Application

Automatic Software Patching
Automatic Backup
Monitoring And Indicators
Automatic Host Deployment

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Public Cloud Non-Relational Databases & NoSQL Database Market Drivers:
Market drivers are the key factors and forces that shape the growth and direction of a particular market or industry. These drivers can include a wide range of economic, social, technological, and regulatory factors that influence demand, supply, and overall market dynamics. By understanding the primary market drivers, businesses can anticipate trends, identify opportunities, and make informed strategic decisions. Examples of market drivers may include changes in consumer preferences, advancements in technology, shifts in regulatory policies, fluctuations in economic conditions, and competitive pressures.

Public Cloud Non-Relational Databases & NoSQL Database Market Restraints:
The global Public Cloud Non-Relational Databases & NoSQL Database Market restraints can arise from various sources, including regulatory constraints, economic challenges, technological limitations, competitive pressures, and shifts in consumer behavior. Market restraints may impede market expansion, constrain profitability, and create obstacles for businesses seeking to enter or operate within a particular market segment

The cost analysis of the Global Public Cloud Non-Relational Databases & NoSQL Database Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

Reasons for buying this report:

  • It offers an analysis of changing competitive scenario.
  • For making informed decisions in the businesses, it offers analytical data with strategic planning
  • It offers seven-year assessment of Public Cloud Non-Relational Databases & NoSQL Database Market.
  • It helps in understanding the major key product segments.
  • Researchers throw light on the dynamics of the market such as drivers, restraints, trends, and opportunities.
  • It offers regional analysis of Public Cloud Non-Relational Databases & NoSQL Database Market along with business profiles of several stakeholders.
  • It offers massive data about trending factors that will influence the progress of the Public Cloud Non-Relational Databases & NoSQL Database Market.

 Table of Contents

Global Public Cloud Non-Relational Databases & NoSQL Database Market Research Report 2023 – 2030

Chapter 1 Public Cloud Non-Relational Databases & NoSQL Database Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Public Cloud Non-Relational Databases & NoSQL Database Market Forecast

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Kubernetes 1.30 Released with Contextual Logging, Improved Performance, and Security

MMS Founder
MMS Mostafa Radwan

Article originally posted on InfoQ. Visit InfoQ

The Cloud Native Computing Foundation (CNCF) released Kubernetes 1.30, named Uwubernetes in April. The release introduced features such as recursive read-only mounts, job completion policy, and fast recursive SELinux label change.

One of the changes in Kubernetes 1.30 is the overhaul of memory swap support for Linux nodes. This improvement is designed to enhance system stability by providing more control over memory usage. Alongside this, the introduction of a sleep action for the PreStop lifecycle hook offers a simplified native option for managing pod termination activities and ensuring better workload management.

Alpha features in version 1.30 include the integration of the Common Expression Language (CEL) for admission control, which paves the way for more sophisticated policy controls and validation mechanisms in Kubernetes clusters. Furthermore, enhancements to service account tokens through Kubernetes Enhancement Proposals (KEP) aim to provide more secure and manageable service accounts, an essential component for maintaining secure Kubernetes environments.

Kubernetes 1.30 also brings beta support for user namespaces, a Linux feature that isolates container UIDs and GIDs from those on the host, significantly bolstering security measures.

Kat Cosgrove, from the release team, commented on Contextual Logging becoming beta in version 1.30

This enhancement simplifies the correlation and analysis of log data across distributed systems, significantly improving the efficiency of troubleshooting efforts. By offering a clearer insight into the workings of your Kubernetes environments, Contextual Logging ensures that operational challenges are more manageable, marking a notable step forward in Kubernetes observability.

Further scheduling improvements have been made, highlighted by the introduction of MatchLabelKeys for PodAffinity and PodAntiAffinity, which allows for better pod placement strategies.

Also, the decoupling of critical components such as the TaintManager from NodeLifecycleController intends to enhance the overall maintainability of the project.

Additionally, this version presents usability upgrades to the scheduler and new structured authorization configurations, which ensure more sophisticated access controls within Kubernetes environments.

This release also deprecates several outdated features. The regression fixes for open API descriptions of imagePullSecrets and hostAliases fields are noteworthy, as consistency in these fields’ usage is crucial for operational integrity.

Additionally, this version signals the movement away from legacy security configurations in favor of more streamlined and modular approaches.

According to the release notes, Kubernetes version 1.30 has 45 enhancements, including 10 entering alpha, 18 graduating to beta, and 17 becoming generally available.

Earlier this month, the Kubernetes community celebrated 10 years since the first git commit to the project. The event known as KuberTENes was held in many places around the globe with the official one sponsored by the CNCF in Mountain View, CA, and was streamed live on its YouTube channel.

For detailed information on the Kubernetes 1.30 release, users can refer to the official release notes and documentation for a comprehensive overview of the enhancements and deprecations this version presents or watch the recording of the CNCF webinar by the release team. The next release 1.31 is expected in August 2024.

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Big Data and Analytics Market Report Covers Future Trends with Research 2024-2031

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Research Cognizance has published a report that represents the process of collecting, analyzing, and interpreting data about a Big Data and Analytics market. This is a crucial step in developing a business strategy or launching a new product, as it helps companies to gain a deeper understanding of the Big Data and Analytics market, identify customer needs and preferences, and assess competition in the industry.

Get a sample report: https://www.researchcognizance.com/sample-request/102046

Competitive landscape:

This Big Data and Analytics research report highlights the key market players who are succeeding in the market. It tracks their business strategies, financial status, and upcoming products.

Some of the top companies influencing this market are:

Microsoft, MongoDB, Predikto, Informatica, CS, Blue Yonder, Azure, Software AG, Sensewaves, TempoIQ, SAP, OT, IBM, Cyber Group, Splunk

This Big Data and Analytics research report introduces the market by providing an overview that includes definitions, applications, product introductions, developments, challenges, and regions.

Big Data and Analytics Market research is also conducted using various methods including surveys, focus groups, interviews and observations. The data collected is both qualitative (e.g. opinions, attitudes) and quantitative (e.g. statistics, numbers). The Big Data and Analytics market research results are then analyzed to draw conclusions and make informed decisions.

The regional coverage of the Big Data and Analytics market is mentioned in the report, with the main focus being on regions such as North America, South America, the Asia Pacific region, the Middle East and Africa, and Europe.

Get an Exclusive Discount on the first purchase of this report @:

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Segmentation:

The Big Data and Analytics market is segmented by type, product, end-users, etc. Segmentation helps to provide an accurate explanation of the market.

Market Segmentation: By Type

Data Intergration
Data Storage
Data Presentation

Market Segmentation: By Application

LoT
M2M

This report is intended to provide:

  • A qualitative and quantitative analysis of the Big Data and Analytics market of current trends, dynamics, and estimates from 2023 to 2030.
  • Analytical tools such as SWOT analysis and Porter’s Five Forces analysis are used to explain the power of Big Data and Analytics buyers and suppliers, make profit-oriented decisions, and strengthen their business.
  • The in-depth market segmentation analysis helps to identify the prevailing market opportunities.
  • Ultimately, this Big Data and Analytics report will help save you time and money by providing unbiased information under one roof.

 Table of Contents

Global Big Data and Analytics Market Research Report 2023 – 2030

Chapter 1 Big Data and Analytics Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Big Data and Analytics Market Forecast

Get Direct Purchase of this Report: https://www.researchcognizance.com/checkout/102046

Conclusion:

Big Data and Analytics Market research also helps companies identify potential opportunities and threats in the industry, assess demand for a product or service, and determine the optimal strategy. It’s an ongoing process that requires companies to stay up to date with the latest trends and changes in the Big Data and Analytics market in order to remain competitive.

Get in Touch with Us:

Neil Thomas

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NoSQL: ScyllaDB scales flexibly with replication architecture from Google | heise online

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Stylized data center

(Bild: Gorodenkoff/Shutterstock.com)

With the tablet replication architecture, version 6.0 of the NoSQL database aims to distribute its content faster and more flexibly in the cluster.

Scylla has released version 6.0 of its NoSQL database ScyllaDB. The open source database, which has been designed for high performance since its inception, learns a new replication architecture with the current release.

The release also brings improvements with regard to strictly consistent topology updates and consistent updates for access control.

ScyllaDB 6.0 relies on tablets for data distribution in the cluster. Google first introduced the concept for the distributed database system Bigtable [2] and uses it in its successor Spanner. Tablets comprise several rows of tables. The nodes of Bigtable do not contain the data, but only pointers to the tablets.

The database divides the tables into tablets, which it distributes to several nodes in the cluster.

(Image: Scylla [3])

The load balancer distributes the data dynamically and can change its size if necessary, i.e. split larger tablets or merge smaller ones. The load balancer distributes the split tablets independently to different nodes.

In this way, the system can react flexibly to changes in the requests, the content or the available nodes.

If a new node is added, it takes over trays from heavily utilized nodes.

(Image: Scylla)

The tablet architecture is more flexible than the concept of replication with virtual nodes [4], which ScyllaDB adopted from Cassandra and used up to version 5.4.

The current version of ScyllaDB uses the Tablets’ architecture by default. If you want to deactivate it because the architecture does not allow lightweight transactions, among other things, you have to create the respective keyspace with the flag tablets = { 'enabled': false }.

The second major innovation is strictly consistent topology updates. Scylla relies on the Raft algorithm – here the database [5] had already [6] said goodbye to the model Cassandra [7] and its Paxos algorithm in 2023 [8].

ScyllaDB 6.0 activates Raft-based, strictly consistent topology updates for new clusters by default. An article in the documentation explains [9] how to manually convert existing clusters.

In addition to the topology updates, the database now also guarantees strictly consistent authentication updates for Role-Based Access Control (RBAC) and consistent service levels.

The team behind ScyllaDB first [10] presented the NoSQL database at the Cassandra Summit in 2015 [11]. The database was designed for compatibility with Cassandra from the outset, but was intended to be significantly more performant. As it is written in C++, while Cassandra relies on Java, it does not require a Java Virtual Machine (JVM) with the associated garbage collector.

Also for performance reasons, ScyllaDB uses a shared-nothing architecture [12] in which each data fragment receives fixed resources (CPU, RAM, network and persistent memory) to avoid delays and blockages when sharing data between resources.

At the end of 2018, Scylla spoke of an important milestone in the blog [13] for the release of version 3.0 [14], as the company had “surpassed feature parity with Apache Cassandra”. In the meantime, the database has gone its own way in some areas, such as with the new tablets instead of VNodes or with Raft instead of Paxos.

Further innovations in ScyllaDB 6.0, such as the node tool for managing nodes from the command line, can be found on the Scylla blog [15].


URL dieses Artikels:
https://www.heise.de/-9773261

Links in diesem Artikel:
[1] https://www.heise.de/news/NoSQL-ScyllaDB-skaliert-flexibel-mit-Replikationsarchitektur-von-Google-9772579.html
[2] https://static.googleusercontent.com/media/research.google.com/en//archive/bigtable-osdi06.pdf
[3] https://opensource.docs.scylladb.com/stable/architecture/tablets.html
[4] https://docs.datastax.com/en/cassandra-oss/3.0/cassandra/architecture/archDataDistributeVnodesUsing.html
[5] https://opensource.docs.scylladb.com/stable/architecture/raft.html
[6] https://opensource.docs.scylladb.com/stable/architecture/raft.html
[7] https://opensource.docs.scylladb.com/stable/architecture/raft.html
[8] https://opensource.docs.scylladb.com/stable/architecture/raft.html
[9] https://opensource.docs.scylladb.com/master/upgrade/upgrade-opensource/upgrade-guide-from-5.4-to-6.0/enable-consistent-topology.html
[10] https://www.heise.de/news/NoSQL-Datenbank-ScyllaDB-schnell-durch-C-und-shared-nothing-2824561.html?from-en=1
[11] https://www.heise.de/news/NoSQL-Datenbank-ScyllaDB-schnell-durch-C-und-shared-nothing-2824561.html?from-en=1
[12] https://www.scylladb.com/glossary/shared-nothing-architecture/
[13] https://www.scylladb.com/press-release/scylladb-major-release-nosql-database-support-concurrent-oltp-and-olap/
[14] https://www.heise.de/news/Datenbank-ScyllaDB-3-0-positioniert-sich-als-Alternative-zu-Apache-Cassandra-4213934.html?from-en=1
[15] https://www.scylladb.com/2024/06/12/introducing-scylladb-6-0-with-tablets-and-strongly-consistent-topology-updates/
[16] mailto:rme@ix.de

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MongoDB (NASDAQ:MDB) Sets New 12-Month Low at $214.74 – Defense World

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Posted on mongodb google news. Visit mongodb google news

MongoDB, Inc. (NASDAQ:MDBGet Free Report) reached a new 52-week low on Thursday . The stock traded as low as $214.74 and last traded at $218.26, with a volume of 134047 shares. The stock had previously closed at $218.85.

Analyst Upgrades and Downgrades

Several research firms recently weighed in on MDB. Oppenheimer cut their price objective on MongoDB from $480.00 to $300.00 and set an “outperform” rating for the company in a research report on Friday, May 31st. JMP Securities cut their price objective on MongoDB from $440.00 to $380.00 and set a “market outperform” rating for the company in a research report on Friday, May 31st. Bank of America cut their price objective on MongoDB from $500.00 to $470.00 and set a “buy” rating for the company in a research report on Friday, May 17th. Monness Crespi & Hardt raised MongoDB to a “hold” rating in a report on Tuesday, May 28th. Finally, Loop Capital cut their price target on MongoDB from $415.00 to $315.00 and set a “buy” rating for the company in a report on Friday, May 31st. One investment analyst has rated the stock with a sell rating, five have issued a hold rating, nineteen have issued a buy rating and one has issued a strong buy rating to the company’s stock. According to MarketBeat, the company has an average rating of “Moderate Buy” and a consensus price target of $364.11.

Get Our Latest Analysis on MDB

MongoDB Stock Performance

The stock has a 50 day moving average price of $314.74 and a two-hundred day moving average price of $371.91. The company has a current ratio of 4.93, a quick ratio of 4.93 and a debt-to-equity ratio of 0.90. The company has a market cap of $16.69 billion, a price-to-earnings ratio of -80.98 and a beta of 1.13.

Insider Activity

In other news, Director Hope F. Cochran sold 1,174 shares of the stock in a transaction on Monday, June 17th. The stock was sold at an average price of $224.38, for a total value of $263,422.12. Following the transaction, the director now owns 13,011 shares of the company’s stock, valued at approximately $2,919,408.18. The transaction was disclosed in a document filed with the SEC, which is accessible through the SEC website. In related news, Director Hope F. Cochran sold 1,174 shares of the firm’s stock in a transaction on Monday, June 17th. The stock was sold at an average price of $224.38, for a total transaction of $263,422.12. Following the sale, the director now owns 13,011 shares of the company’s stock, valued at approximately $2,919,408.18. The sale was disclosed in a document filed with the Securities & Exchange Commission, which is accessible through this link. Also, CEO Dev Ittycheria sold 17,160 shares of the firm’s stock in a transaction on Tuesday, April 2nd. The stock was sold at an average price of $348.11, for a total value of $5,973,567.60. Following the sale, the chief executive officer now directly owns 226,073 shares in the company, valued at approximately $78,698,272.03. The disclosure for this sale can be found here. Insiders have sold a total of 49,976 shares of company stock valued at $17,245,973 in the last 90 days. 3.60% of the stock is owned by company insiders.

Institutional Trading of MongoDB

Hedge funds have recently added to or reduced their stakes in the business. Vanguard Group Inc. raised its stake in shares of MongoDB by 2.9% in the 4th quarter. Vanguard Group Inc. now owns 6,842,413 shares of the company’s stock valued at $2,797,521,000 after purchasing an additional 194,148 shares during the period. Raymond James & Associates raised its stake in shares of MongoDB by 14.2% in the 4th quarter. Raymond James & Associates now owns 60,557 shares of the company’s stock valued at $24,759,000 after purchasing an additional 7,510 shares during the period. Nordea Investment Management AB raised its stake in shares of MongoDB by 298.2% in the 4th quarter. Nordea Investment Management AB now owns 18,657 shares of the company’s stock valued at $7,735,000 after purchasing an additional 13,972 shares during the period. Assenagon Asset Management S.A. raised its stake in shares of MongoDB by 1,196.1% in the 4th quarter. Assenagon Asset Management S.A. now owns 29,215 shares of the company’s stock valued at $11,945,000 after purchasing an additional 26,961 shares during the period. Finally, Atalanta Sosnoff Capital LLC raised its stake in shares of MongoDB by 24.7% in the 4th quarter. Atalanta Sosnoff Capital LLC now owns 54,311 shares of the company’s stock valued at $22,205,000 after purchasing an additional 10,753 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.

Further Reading



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Fulton Bank N.A. Sells 126 Shares of MongoDB, Inc. (NASDAQ:MDB) – Defense World

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Posted on mongodb google news. Visit mongodb google news

Fulton Bank N.A. trimmed its position in MongoDB, Inc. (NASDAQ:MDBFree Report) by 10.7% in the 1st quarter, according to the company in its most recent disclosure with the Securities & Exchange Commission. The institutional investor owned 1,054 shares of the company’s stock after selling 126 shares during the period. Fulton Bank N.A.’s holdings in MongoDB were worth $378,000 as of its most recent filing with the Securities & Exchange Commission.

Other institutional investors and hedge funds have also bought and sold shares of the company. Vanguard Group Inc. boosted its position in shares of MongoDB by 2.9% in the 4th quarter. Vanguard Group Inc. now owns 6,842,413 shares of the company’s stock worth $2,797,521,000 after purchasing an additional 194,148 shares in the last quarter. Jennison Associates LLC boosted its holdings in MongoDB by 3.3% in the fourth quarter. Jennison Associates LLC now owns 3,856,857 shares of the company’s stock worth $1,576,876,000 after acquiring an additional 122,893 shares in the last quarter. Norges Bank purchased a new stake in shares of MongoDB during the fourth quarter worth about $326,237,000. First Trust Advisors LP increased its holdings in shares of MongoDB by 59.3% during the fourth quarter. First Trust Advisors LP now owns 549,052 shares of the company’s stock valued at $224,480,000 after acquiring an additional 204,284 shares in the last quarter. Finally, Northern Trust Corp raised its position in shares of MongoDB by 5.5% in the 3rd quarter. Northern Trust Corp now owns 448,035 shares of the company’s stock valued at $154,957,000 after purchasing an additional 23,270 shares during the last quarter. 89.29% of the stock is owned by hedge funds and other institutional investors.

MongoDB Trading Up 3.2 %

Shares of NASDAQ MDB opened at $227.55 on Friday. The company has a debt-to-equity ratio of 0.90, a current ratio of 4.93 and a quick ratio of 4.93. MongoDB, Inc. has a one year low of $214.74 and a one year high of $509.62. The firm has a fifty day moving average of $314.74 and a 200 day moving average of $371.91.

Insider Transactions at MongoDB

In related news, Director Hope F. Cochran sold 1,174 shares of MongoDB stock in a transaction dated Monday, June 17th. The stock was sold at an average price of $224.38, for a total value of $263,422.12. Following the sale, the director now directly owns 13,011 shares in the company, valued at approximately $2,919,408.18. The transaction was disclosed in a legal filing with the SEC, which is available at this link. In other MongoDB news, Director Hope F. Cochran sold 1,174 shares of the firm’s stock in a transaction on Monday, June 17th. The stock was sold at an average price of $224.38, for a total value of $263,422.12. Following the transaction, the director now owns 13,011 shares in the company, valued at approximately $2,919,408.18. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is available at the SEC website. Also, Director Dwight A. Merriman sold 2,000 shares of the company’s stock in a transaction on Tuesday, June 4th. The stock was sold at an average price of $234.24, for a total transaction of $468,480.00. Following the sale, the director now directly owns 1,146,784 shares of the company’s stock, valued at approximately $268,622,684.16. The disclosure for this sale can be found here. Insiders sold 49,976 shares of company stock valued at $17,245,973 over the last 90 days. Corporate insiders own 3.60% of the company’s stock.

Analysts Set New Price Targets

A number of brokerages have issued reports on MDB. Bank of America cut their price target on shares of MongoDB from $500.00 to $470.00 and set a “buy” rating on the stock in a research note on Friday, May 17th. Scotiabank cut their target price on MongoDB from $385.00 to $250.00 and set a “sector perform” rating on the stock in a research report on Monday, June 3rd. Truist Financial lowered their price target on MongoDB from $475.00 to $300.00 and set a “buy” rating for the company in a research report on Friday, May 31st. Mizuho cut their price objective on MongoDB from $380.00 to $250.00 and set a “neutral” rating on the stock in a report on Friday, May 31st. Finally, KeyCorp decreased their target price on MongoDB from $490.00 to $440.00 and set an “overweight” rating for the company in a report on Thursday, April 18th. One investment analyst has rated the stock with a sell rating, five have given a hold rating, nineteen have given a buy rating and one has assigned a strong buy rating to the stock. Based on data from MarketBeat.com, the company has an average rating of “Moderate Buy” and a consensus target price of $364.11.

View Our Latest Report on MongoDB

About MongoDB

(Free Report)

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

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Institutional Ownership by Quarter for MongoDB (NASDAQ:MDB)



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