Jennison Associates LLC Raises Position in MongoDB, Inc. (NASDAQ:MDB) – MarketBeat

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Jennison Associates LLC grew its holdings in MongoDB, Inc. (NASDAQ:MDBFree Report) by 23.6% in the third quarter, according to its most recent disclosure with the Securities and Exchange Commission (SEC). The institutional investor owned 3,102,024 shares of the company’s stock after buying an additional 592,038 shares during the quarter. Jennison Associates LLC owned 4.20% of MongoDB worth $838,632,000 as of its most recent SEC filing.

Other large investors have also made changes to their positions in the company. Prospera Private Wealth LLC purchased a new stake in shares of MongoDB during the third quarter worth approximately $100,000. B. Metzler seel. Sohn & Co. Holding AG purchased a new position in shares of MongoDB in the third quarter worth about $4,366,000. Banque Cantonale Vaudoise boosted its position in MongoDB by 16.8% during the third quarter. Banque Cantonale Vaudoise now owns 2,508 shares of the company’s stock worth $678,000 after acquiring an additional 360 shares during the last quarter. Tidemark LLC increased its holdings in shares of MongoDB by 542.5% in the third quarter. Tidemark LLC now owns 681 shares of the company’s stock valued at $184,000 after purchasing an additional 575 shares during the last quarter. Finally, Swiss National Bank boosted its stake in MongoDB by 1.1% during the third quarter. Swiss National Bank now owns 217,700 shares of the company’s stock worth $58,855,000 after buying an additional 2,300 shares during the last quarter. 89.29% of the stock is owned by institutional investors and hedge funds.

Wall Street Analysts Forecast Growth

Several research firms recently weighed in on MDB. Scotiabank lifted their target price on shares of MongoDB from $250.00 to $295.00 and gave the company a “sector perform” rating in a report on Friday, August 30th. Barclays increased their target price on shares of MongoDB from $290.00 to $345.00 and gave the company an “overweight” rating in a research report on Friday, November 15th. Morgan Stanley upped their price target on shares of MongoDB from $320.00 to $340.00 and gave the company an “overweight” rating in a research report on Friday, August 30th. Stifel Nicolaus raised their price objective on shares of MongoDB from $300.00 to $325.00 and gave the stock a “buy” rating in a research report on Friday, August 30th. Finally, UBS Group upped their price target on MongoDB from $250.00 to $275.00 and gave the stock a “neutral” rating in a research note on Friday, August 30th. One investment analyst has rated the stock with a sell rating, five have issued a hold rating, nineteen have given a buy rating and one has assigned a strong buy rating to the company. Based on data from MarketBeat, the stock has an average rating of “Moderate Buy” and an average price target of $336.54.

View Our Latest Stock Analysis on MongoDB

MongoDB Stock Performance

MDB stock traded down $6.60 during mid-day trading on Wednesday, reaching $282.55. 822,863 shares of the stock traded hands, compared to its average volume of 1,428,410. The company has a quick ratio of 5.03, a current ratio of 5.03 and a debt-to-equity ratio of 0.84. The business has a 50-day moving average of $278.01 and a two-hundred day moving average of $272.52. The firm has a market capitalization of $20.87 billion, a PE ratio of -95.74 and a beta of 1.15. MongoDB, Inc. has a 52 week low of $212.74 and a 52 week high of $509.62.

MongoDB (NASDAQ:MDBGet Free Report) last issued its earnings results on Thursday, August 29th. The company reported $0.70 EPS for the quarter, topping the consensus estimate of $0.49 by $0.21. MongoDB had a negative return on equity of 15.06% and a negative net margin of 12.08%. The firm had revenue of $478.11 million during the quarter, compared to analysts’ expectations of $465.03 million. During the same period last year, the business earned ($0.63) earnings per share. MongoDB’s quarterly revenue was up 12.8% compared to the same quarter last year. As a group, equities analysts predict that MongoDB, Inc. will post -2.39 EPS for the current fiscal year.

Insiders Place Their Bets

In other news, Director Dwight A. Merriman sold 3,000 shares of the company’s stock in a transaction that occurred on Wednesday, October 2nd. The stock was sold at an average price of $256.25, for a total transaction of $768,750.00. Following the sale, the director now owns 1,131,006 shares in the company, valued at $289,820,287.50. This represents a 0.26 % decrease in their position. The sale was disclosed in a document filed with the SEC, which is available at this link. Also, CFO Michael Lawrence Gordon sold 5,000 shares of the stock in a transaction on Monday, October 14th. The shares were sold at an average price of $290.31, for a total transaction of $1,451,550.00. Following the completion of the sale, the chief financial officer now owns 80,307 shares of the company’s stock, valued at approximately $23,313,925.17. This represents a 5.86 % decrease in their position. The disclosure for this sale can be found here. Over the last quarter, insiders have sold 25,600 shares of company stock worth $7,034,249. Insiders own 3.60% of the company’s stock.

MongoDB Profile

(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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How to Mask Data for Testing in MongoDB – Security Boulevard

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Masking data for safe, compliant use in testing environments is not as straightforward as it seems, especially when using schemaless, unstructured databases like MongoDB. Personally identifiable information (PII) can be scattered throughout your document-based data in ways that are hard to predict—so hard that it simply isn’t a challenge most teams offering data de-identification solutions are willing to take on. (Spoiler alert: Tonic.ai isn’t “most teams.”)

Let’s explore how to mask data for testing in MongoDB, plus what makes this such a challenging nut to crack. Then, we’ll wrap up with a quick demonstration of how to mask MongoDB data using Tonic.

MongoDB’s Masked Data Challenges

MongoDB is a NoSQL database system that stores data as documents. Its document database system operates without structure or schema, and each copy can contain numerous types of data as the data level progresses.

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MongoDB’s flexible storage system enables efficiency for scaling apps because it stores large amounts of data within clusters defined in millions of nodes. However, this document-based storage system presents significant challenges when de-identifying and masking data.

Challenge 1: Unstructured data

The first challenge is its unstructured nature. Since MongoDB is schemaless, each field in a data collection can represent any one of the various data types. Furthermore, this type can change with each document level. A field may exist as an integer in one document level and a string in another. This lack of consistency presents an obstacle with masking data and generating production-like data for testing.


Challenge 2: MongoDB’s JSON storage format 

MongoDB’s JSON storage format poses another data masking challenge. The JSON format houses various forms of data, from names to license plate numbers and other types that are less easily quantifiable. These high-level nested document fields create complex hierarchies, which complicate the level of granularity needed to generate realistically represented test data.

Challenge 3: Time required to build

Even for a relational database management system (RDBMS), it takes a significant amount of time and resources to create an infrastructure capable of generating test data that perfectly mimics production data. MongoDB’s various formats and versions significantly increase that time. Your de-identification infrastructure requires generators that can track and mask each version and document format. Hardly a walk in the park.

Finding an Effective Data Masking Solution

Generating MongoDB data is challenging because an effective solution needs to:

  • Detect and locate PII across each document in the entire collection.
  • Mask the data according to type, even though key types may vary across document levels with the same key.
  • Provide complete visibility into your document collection to observe and check each document during the generation process.

Assume a sample analytics collection with multiple documents contains documents A and B with a plate_number key. However, the plate_number key in document A is an integer, while the one in document B is a string. An efficient solution should know that it needs to mask the integer plate_number key with an integer and the string key with a string.

How Tonic Masks MongoDB Data

Tonic enables aggregating document collections in MongoDB to de-identify sensitive information and generate realistic, useful document-based data while eliminating the risk of PII slipping through. The Tonic interface provides a comprehensive view of the entire data generation process.

Let’s take a look at how it works:

A schemaless data-capturing method

Tonic curbs the complexity of document storage databases by employing a schemaless data-capturing method. We create a hybrid document model representing the entirety of the documents in your collection, then transfer the model to lower environments — like your staging environment. After connecting, Tonic scans the source database and automatically creates this hybrid document while capturing all edge cases and leaving no room for PII leaks.

Granular NoSQL data

By mixing and matching our 50+ generators, the platform masks NoSQL data with a high degree of granularity to mirror the complexity of your data. Regardless of how unstructured or varied your document database is, Tonic can accommodate, masking several data types in a document — even within the same fields.

Then, Tonic’s user-friendly interface enables you to preview the newly masked data, giving you a comprehensive view of each document so you can refine your data along the way.

Consistent data across databases

Tonic’s cross-database support helps achieve consistent test data throughout your data ecosystem. By connecting natively to other database types like Redshift, PostgreSQL, and Oracle and matching input-to-output generated data types across these databases, Tonic produces realistic test data that preserves relationships across databases.

Additionally, organizations can use Tonic with Mongo as a NoSQL interface, freeing teams to mask data stored in homegrown, document-based DB solutions.

Now that we understand the value Tonic brings to the question of how to mask data for testing in MongoDB, let’s explore how to put it into action.

How to mask PII data in MongoDB with Tonic

Integrating with MongoDB is simple — Tonic can connect natively to MongoDB via an Atlas connection string.

First, create a destination database in MongoDB to store the generated data. We’ll call our database tonic_mongo_integration.

Then, log into the Tonic platform and create a workspace. The workspace stores your connection information, data jobs, and other relevant information.

Within your workspace configuration, enter your MongoDB Connection String for your source and your destination. These connection strings enable Tonic to grab the data from your source Mongo instance and then store masked data in your destination Mongo instance. Next, enter the name of your MongoDB Database that will hold the fake data.

Then, hop into the Privacy Hub in the left side menu. The Privacy Hub performs a scan of all the documents in your collection. It flags data that it identifies as sensitive and makes recommendations for which generators to apply to protect that data.

You can apply generators directly within Privacy Hub or click into the Collection View in the left side menu to see your hybrid document in full.

In the Collection drop-down menu, select customers. This gives a preview of the collection’s fields. You can set the view mode as Single Document, which then shows each document in your collection.

Alternatively, you can view a Hybrid Document that shows all of the fields that appear across the documents in the collection.

Once you’ve applied the generators you need to safely and realistically mask your sensitive data, it’s time to generate. Click Generate Data in the upper right.

To view the status of your data generation, click Jobs in the left-hand side menu. We’re showing a Completed status, so let’s check our database and compare our MongoDB data.

Here’s the original data in our source DB. Note the various address fields.

And here’s the fake data in our destination DB. Notice the new, realistic address fields.

And there you have it! Real fake document-based data in MongoDB for all your testing needs. 

How to mask data for testing in MongoDB with Tonic

Tonic’s unique data masking solution for MongoDB enables developers to test their products efficiently with high-quality, realistic document-based data.

But don’t take our word for it. Check out what eBay had to say on the eBayTech blog about Tonic’s role in creating high-quality NoSQL data for their staging environments. If you’re looking for a similar solution for your NoSQL data, let’s chat.

*** This is a Security Bloggers Network syndicated blog from Expert Insights on Synthetic Data from the Tonic.ai Blog authored by Expert Insights on Synthetic Data from the Tonic.ai Blog. Read the original post at: https://www.tonic.ai/blog/how-to-mask-data-for-testing-in-mongodb

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Beyond Open Banking – Exploring the Move to Open Finance – Finextra Research

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  • How far along is the financial services industry on the journey from open banking to open finance?
  • As the drive to digital increases, the barriers to entry lower. What are the modern data architecture changes needed to not just keep up with open, embedded ecosystems, but also increased competition?
  • What do companies operating under open finance rules need to consider when sharing financial data with third-party providers?
  • And what are the technical challenges financial institutions are facing in the open era? How can they overcome legacy systems to embrace the open finance revolution?
  • Regulations like FiDA are adding another layer of complexity. How can institutions unlock the potential of open data while adhering to the highest regulatory standards?

The financial industry has made significant progress in financial data access through open banking and progress will expand beyond payments with regulations like Payment Services Directive 2 (PSD2) to what is explored in the PSR and PSD3. Open banking has marked the beginning of this new era, and now the shift toward open finance is the essential next phase in this transition, with incoming regulations like Financial Data Access (FiDA) covering new areas including mortgages, pensions, investments, and savings.

While these initiatives hold promise, a successful open finance framework will require many financial institutions – who still rely on outdated systems and processes that may not natively support the flexible data access requirements of FiDA – to review their data architectures. If institutions can’t adhere to strict geographic data regulations (with certain jurisdictions enforcing that data remains within specific regions), guarantee real-time data availability, or scale in real-time to handle increased API traffic, further data modernisation efforts will be required.

How can financial institutions navigate this complex web of open architecture, flexible infrastructure, data requirements and regulation? Modern ecosystems require modern solutions, so organisations need to re-think their approach to data, not just to be able to facilitate open finance, but also to stay relevant in an increasingly competitive landscape.

Sign up for this Finextra webinar, hosted in association with MongoDB, to join our panel of industry experts who will discuss how the move to open finance can be managed effectively with the right data architecture in place.

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DBHawk Flies with Text-to-SQL, SOC 2 Compliance – Datanami

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Customers will be able to interact with their database using natural language thanks to the new text-to-SQL function in DBHawk, a database access tool developed by Datasparc. The company also announced its SOC 2 Type II compliance and an expansion of its partnership with IBM.

DBHawk is Datasparc’s handy database tool that lets different users accomplish a range of different database tasks. For instance, data analysts can use it to write and execute SQL queries against dozens of databases, including relational and NoSQL databases. Data engineers can use it to perform joins and schedule SQL queries. Administrators can also use it to create tables or views, among other capabilities.

Its new text-to-SQL feature lets users interact with SQL databases using natural language. It uses AI to convert the natural language query into SQL, which is then executed against the database. Datasparc says the feature will be useful for expanding database access to people who aren’t SQL experts.

Datasparc showcased the new text-to-SQL capability at the recent PASS Data Community Summit, which took place last month in Seattle, Washington. “We are thrilled to share our latest advancements in AI-powered data analytics at the PASS Data Summit,” said Datasparc CEO Manish Shah. “Our text-to-SQL AI feature is a game-changer, and we can’t wait to demonstrate its capabilities to the data community.”

The San Diego, California company also announced that it recently obtained SOC 2 Type II compliance, which indicates that it passed an audit by the American Institute of Certified Public Accountants (AICPA) that tested its privacy and security controls. DBHawk is available as a software as a service (SaaS) product, which makes the SOC 2 Type II certification especially important. It’s also available as an enterprise product that customers can install on-prem or in virtual private cloud (VPC) environments.

Lastly, Datasparc announced it has expanded its partnership with IBM. Becoming an IBM Partner Plus shows that its dedicated to supporting IBM customers, in particular customers running the z/OS mainframe and LUW (Linux, Unix, and Windows) versions of the Db2 database.

Related Items:

DBHawk Partners with Microsoft Azure, Lands Patent for Sensitive Data

DBHawk Enjoys Growth in the Cloud

Web-Based Query Tool Touches Multiple DBs


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Microsoft Introduces Local Emulator for Azure Service Bus Wanted by Developers for Years

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In response to developer feedback, Microsoft launched a local Azure Service Bus emulator. According to the company, the emulator promises to simplify the creation and testing of Azure Service Bus applications by offering a localized environment free from network or cloud-related constraints.

The Azure Service Bus is a managed message broker enabling reliable application communication. Its features include queues and topics for efficient handling, load balancing, transactional reliability, and safe data routing for decoupling services.

Despite its robust capabilities, developers often face challenges testing against cloud-based Service Bus instances due to latency, costs, and cloud dependencies. This local emulator addresses these hurdles head-on.

The company designed the emulator with developer convenience in mind, offering several benefits like:

  • Optimized Development Loop: Developers can test and iterate quickly without relying on cloud deployments, drastically reducing the development cycle time.
  • Cost Efficiency: Since the emulator runs locally, it eliminates cloud usage costs for testing and development scenarios.
  • Isolated Environment: Local testing ensures no interference from other cloud-based activities, allowing precise troubleshooting and debugging.
  • Pre-Migration Testing: Developers can trial Azure Service Bus using their existing AMQP-based applications before committing to a full cloud migration.

The emulator is platform-independent and accessible as a Docker image from the Microsoft Artifact Registry. Developers can deploy it quickly using docker compose or automated scripts available in Microsoft’s Installer repository.

While the emulator replicates much of the Azure Service Bus’s functionality, some features are unavailable:

  • Azure-specific integrations like virtual networks, Microsoft Entra ID, and activity logs.
  • Advanced capabilities like autoscaling, geo-disaster recovery, and large message handling.
  • Persisted data: Container restarts reset data and entities.

Furthermore, the emulator is tailored for local development and lacks several high-level Azure Service Bus cloud service features. It does not support a UI portal, visual metrics, or advanced alerting capabilities.

The emulator enforces quotas like the cloud service, such as:

  • Maximum of 50 queues/topics per namespace.
  • Message size capped at 256 KB.
  • Namespace size is limited to 100 MB.

Configuration changes must be pre-defined in config.json and applied before restarting the container.

Developers have long anticipated the local Service Bus emulator for a while. Vincent Kok, a freelance .NET developer, wrote in a post on LinkedIn:

Initially, Microsoft rejected the idea of setting up a local development for Azure Service Bus. The official answer from Microsoft was to use cloud instances of Azure ServiceBus. However, this approach requires each developer to create their own Service Bus namespace to ensure isolated development and testing. Alternatively, developers can share a single Service Bus namespace, but this introduces the risk of messages published by one developer being consumed by another, which is not very practical.

And:

Today, six years after that GitHub issue was first opened, the wait is finally over! Microsoft has released a local emulator for Azure Service Bus, enabling developers to build and test applications locally without needing to spin up cloud instances of Service Bus.

Furthermore, on X, Dave Callan, a Microsoft MVP, tweeted:

It’s so amazing that this is finally here.

We can use the emulator to develop and test code against the service in isolation, free from cloud interference.

Lastly, the emulator is compatible with the latest service bus client SDKs.

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Swiss National Bank Buys 2,300 Shares of MongoDB, Inc. (NASDAQ:MDB) – MarketBeat

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Swiss National Bank increased its position in shares of MongoDB, Inc. (NASDAQ:MDBFree Report) by 1.1% in the 3rd quarter, according to the company in its most recent 13F filing with the Securities & Exchange Commission. The institutional investor owned 217,700 shares of the company’s stock after acquiring an additional 2,300 shares during the quarter. Swiss National Bank owned about 0.29% of MongoDB worth $58,855,000 as of its most recent SEC filing.

Other large investors have also recently modified their holdings of the company. MFA Wealth Advisors LLC acquired a new stake in MongoDB in the 2nd quarter worth approximately $25,000. J.Safra Asset Management Corp lifted its stake in shares of MongoDB by 682.4% during the second quarter. J.Safra Asset Management Corp now owns 133 shares of the company’s stock worth $33,000 after buying an additional 116 shares during the period. Quarry LP grew its holdings in shares of MongoDB by 2,580.0% during the second quarter. Quarry LP now owns 134 shares of the company’s stock valued at $33,000 after buying an additional 129 shares during the last quarter. Hantz Financial Services Inc. acquired a new position in shares of MongoDB in the 2nd quarter valued at $35,000. Finally, GAMMA Investing LLC increased its position in shares of MongoDB by 178.8% in the 3rd quarter. GAMMA Investing LLC now owns 145 shares of the company’s stock valued at $39,000 after acquiring an additional 93 shares during the period. Institutional investors own 89.29% of the company’s stock.

Insider Transactions at MongoDB

In other news, CRO Cedric Pech sold 302 shares of the business’s stock in a transaction that occurred on Wednesday, October 2nd. The shares were sold at an average price of $256.25, for a total value of $77,387.50. Following the sale, the executive now directly owns 33,440 shares of the company’s stock, valued at approximately $8,569,000. The trade was a 0.90 % decrease in their position. The transaction was disclosed in a legal filing with the Securities & Exchange Commission, which is available at this hyperlink. Also, CAO Thomas Bull sold 1,000 shares of the company’s stock in a transaction that occurred on Monday, September 9th. The stock was sold at an average price of $282.89, for a total transaction of $282,890.00. Following the completion of the transaction, the chief accounting officer now owns 16,222 shares in the company, valued at approximately $4,589,041.58. This represents a 5.81 % decrease in their ownership of the stock. The disclosure for this sale can be found here. Insiders sold 25,600 shares of company stock worth $7,034,249 in the last 90 days. Insiders own 3.60% of the company’s stock.

Analyst Upgrades and Downgrades

A number of research analysts have recently weighed in on MDB shares. UBS Group increased their price objective on MongoDB from $250.00 to $275.00 and gave the company a “neutral” rating in a report on Friday, August 30th. Mizuho lifted their price objective on shares of MongoDB from $250.00 to $275.00 and gave the company a “neutral” rating in a research note on Friday, August 30th. Needham & Company LLC increased their price objective on shares of MongoDB from $290.00 to $335.00 and gave the stock a “buy” rating in a research note on Friday, August 30th. Wells Fargo & Company boosted their target price on shares of MongoDB from $300.00 to $350.00 and gave the company an “overweight” rating in a research report on Friday, August 30th. Finally, Wedbush raised MongoDB to a “strong-buy” rating in a research report on Thursday, October 17th. One equities research analyst has rated the stock with a sell rating, five have assigned a hold rating, nineteen have assigned a buy rating and one has assigned a strong buy rating to the company. Based on data from MarketBeat, the company has a consensus rating of “Moderate Buy” and a consensus target price of $336.54.

Check Out Our Latest Report on MongoDB

MongoDB Stock Performance

NASDAQ MDB opened at $289.15 on Wednesday. The company has a debt-to-equity ratio of 0.84, a quick ratio of 5.03 and a current ratio of 5.03. The stock’s fifty day moving average is $278.06 and its two-hundred day moving average is $273.04. The firm has a market capitalization of $21.36 billion, a P/E ratio of -95.74 and a beta of 1.15. MongoDB, Inc. has a fifty-two week low of $212.74 and a fifty-two week high of $509.62.

MongoDB (NASDAQ:MDBGet Free Report) last announced its quarterly earnings results on Thursday, August 29th. The company reported $0.70 earnings per share for the quarter, beating the consensus estimate of $0.49 by $0.21. MongoDB had a negative net margin of 12.08% and a negative return on equity of 15.06%. The firm had revenue of $478.11 million for the quarter, compared to analysts’ expectations of $465.03 million. During the same quarter in the previous year, the company posted ($0.63) EPS. The company’s revenue was up 12.8% compared to the same quarter last year. On average, research analysts anticipate that MongoDB, Inc. will post -2.39 earnings per share for the current year.

MongoDB Company Profile

(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

Institutional Ownership by Quarter for MongoDB (NASDAQ:MDB)

Before you consider MongoDB, you’ll want to hear this.

MarketBeat keeps track of Wall Street’s top-rated and best performing research analysts and the stocks they recommend to their clients on a daily basis. MarketBeat has identified the five stocks that top analysts are quietly whispering to their clients to buy now before the broader market catches on… and MongoDB wasn’t on the list.

While MongoDB currently has a “Moderate Buy” rating among analysts, top-rated analysts believe these five stocks are better buys.

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.NET MAUI 9 Launched with Better Performance, New Controls

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Article originally posted on InfoQ. Visit InfoQ

On November 12th, Microsoft presented the .NET MAUI 9 in its final form. This version brings two new controls (HybridWebView and TitleBar), a slew of improvements throughout the framework, free SyncFusion controls and a Xcode sync tool for Apple-specific files. The performance and stability of the entire framework has been enhanced.

MAUI is an acronym that stands for Multiplatform Application UI. According to Microsoft, it’s an evolution of Xamarin and Xamarin Forms frameworks, unifying separate target libraries and projects into a single project for multiple devices. Currently, MAUI supports writing applications that run on Android 5+, iOS 12.2+, macOS 12+ (as Mac Catalyst), Samsung Tizen, Windows 10 version 1809+, or Windows 11. The new versions bumps the minimum Apple devices support from iOS 11 and macOS 10.15 in .NET MAUI 8.

The .NET MAUI 9 journey to the GA (general availability) version started with the Preview 1 in February 2024. A new preview was launched roughly every month, plus two RC (release candidate) versions were made available in September and October. These frequent releases caught bugs and performance fixes that were included in the final version.

The first of the newly added controls, HybridWebView, allows developers to host HTML, JavaScript and CSS content within a WebView control, but with a communication bridge between the web view and the MAUI application code in .NET. On the JavaScript side there is a HybridWebViewMessageReceived event and SendRawMessage method. On the .NET side of the application there is a RawMessageReceived event and a SendRawMessage method on the control.

The second of the new controls, TitleBar, allows developers to create custom title bars in their application. For the moment, this control is only supported on the Windows platform, with Mac Catalyst support coming ‘in a future release’. The title bar control is then set to the parent Window object using the Window.TitleBar property.

While there are only two new first-party controls in .NET MAUI 9, the recent partnership with SyncFusion has added 14 new free controls from the vendor to MAUI as a package. The new MAUI version adds a sample application to the MAUI App template, which showcases how to use several of the contributed controls, together with recommended practices for common app patterns.

As for performance and stability improvements, one of the significant changes was the complete re-implementation of CollectionView and CarouselView controls on Apple devices. The new implementation requires a code change in the root MauiProgram class.

The new version also brings several deprecated features. The most important one is the Frame control, which is marked as obsolete and should be replaced with the Border control. In addition, the Application.MainPage property is replaced by setting the Window.Page property to the first page of the app.

It is worth noting that just two days after the launch, a Service Release (SR) patch was released with the version 9.0.10 of the framework. The SR version adds small fixes to the GA code. It could be in response to the comments of users on social networks, complaining that upgrading to .NET MAUI 9 breaks Visual Studio or the build process. On the other hand, developers like Claudio Bernasconi are stating that “MAUI is heading in the right direction”.

Readers can refer to GitHub official MAUI repository for complete release notes.

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Atria Investments Inc Acquires 135 Shares of MongoDB, Inc. (NASDAQ:MDB)

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Atria Investments Inc boosted its position in MongoDB, Inc. (NASDAQ:MDBFree Report) by 6.6% during the 3rd quarter, according to its most recent 13F filing with the Securities and Exchange Commission (SEC). The institutional investor owned 2,175 shares of the company’s stock after purchasing an additional 135 shares during the quarter. Atria Investments Inc’s holdings in MongoDB were worth $588,000 as of its most recent filing with the Securities and Exchange Commission (SEC).

Other institutional investors and hedge funds have also bought and sold shares of the company. Principal Financial Group Inc. raised its position in shares of MongoDB by 2.7% during the 3rd quarter. Principal Financial Group Inc. now owns 6,095 shares of the company’s stock valued at $1,648,000 after buying an additional 160 shares in the last quarter. Janney Montgomery Scott LLC bought a new stake in shares of MongoDB during the 3rd quarter valued at about $861,000. Stephens Investment Management Group LLC boosted its stake in shares of MongoDB by 22.8% during the 3rd quarter. Stephens Investment Management Group LLC now owns 30,664 shares of the company’s stock valued at $8,290,000 after purchasing an additional 5,688 shares in the last quarter. US Bancorp DE boosted its stake in shares of MongoDB by 9.1% during the 3rd quarter. US Bancorp DE now owns 3,869 shares of the company’s stock valued at $1,046,000 after purchasing an additional 324 shares in the last quarter. Finally, First Trust Direct Indexing L.P. boosted its stake in shares of MongoDB by 16.0% during the 3rd quarter. First Trust Direct Indexing L.P. now owns 1,888 shares of the company’s stock valued at $510,000 after purchasing an additional 261 shares in the last quarter. 89.29% of the stock is currently owned by hedge funds and other institutional investors.

MongoDB Stock Up 1.7 %

Shares of MongoDB stock opened at $289.15 on Wednesday. The firm’s 50 day moving average price is $278.06 and its 200-day moving average price is $273.04. The company has a quick ratio of 5.03, a current ratio of 5.03 and a debt-to-equity ratio of 0.84. MongoDB, Inc. has a 52-week low of $212.74 and a 52-week high of $509.62. The company has a market cap of $21.36 billion, a P/E ratio of -94.26 and a beta of 1.15.

MongoDB (NASDAQ:MDBGet Free Report) last released its earnings results on Thursday, August 29th. The company reported $0.70 earnings per share for the quarter, topping the consensus estimate of $0.49 by $0.21. The company had revenue of $478.11 million for the quarter, compared to the consensus estimate of $465.03 million. MongoDB had a negative net margin of 12.08% and a negative return on equity of 15.06%. MongoDB’s revenue was up 12.8% compared to the same quarter last year. During the same quarter in the previous year, the firm earned ($0.63) EPS. On average, sell-side analysts predict that MongoDB, Inc. will post -2.39 EPS for the current fiscal year.

Insider Buying and Selling at MongoDB

In related news, CAO Thomas Bull sold 154 shares of the business’s stock in a transaction on Wednesday, October 2nd. The shares were sold at an average price of $256.25, for a total value of $39,462.50. Following the completion of the transaction, the chief accounting officer now directly owns 16,068 shares in the company, valued at $4,117,425. The trade was a 0.95 % decrease in their position. The transaction was disclosed in a document filed with the SEC, which is available at the SEC website. Also, Director Dwight A. Merriman sold 3,000 shares of the business’s stock in a transaction on Wednesday, October 2nd. The shares were sold at an average price of $256.25, for a total transaction of $768,750.00. Following the completion of the transaction, the director now owns 1,131,006 shares of the company’s stock, valued at approximately $289,820,287.50. This represents a 0.26 % decrease in their ownership of the stock. The disclosure for this sale can be found here. In the last ninety days, insiders sold 25,600 shares of company stock worth $7,034,249. Company insiders own 3.60% of the company’s stock.

Analyst Upgrades and Downgrades

Several brokerages have issued reports on MDB. Needham & Company LLC lifted their target price on shares of MongoDB from $290.00 to $335.00 and gave the stock a “buy” rating in a report on Friday, August 30th. Oppenheimer upped their target price on shares of MongoDB from $300.00 to $350.00 and gave the company an “outperform” rating in a report on Friday, August 30th. Wedbush raised shares of MongoDB to a “strong-buy” rating in a research note on Thursday, October 17th. Scotiabank increased their price objective on shares of MongoDB from $250.00 to $295.00 and gave the stock a “sector perform” rating in a research note on Friday, August 30th. Finally, Stifel Nicolaus raised their target price on shares of MongoDB from $300.00 to $325.00 and gave the company a “buy” rating in a research note on Friday, August 30th. One investment analyst has rated the stock with a sell rating, five have issued a hold rating, nineteen have assigned a buy rating and one has given a strong buy rating to the stock. Based on data from MarketBeat.com, the stock presently has a consensus rating of “Moderate Buy” and a consensus price target of $336.54.

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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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MongoDB and Microsoft expand partnership to advance AI applications and data analytics

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Database company MongoDB Inc. today announced an expanded partnership with Microsoft Corp. that includes new integrations aimed at enhancing artificial intelligence application development, real-time data analytics and deployment flexibility.

The first integration sees MongoDB Atlas, MongoDB’s fully managed cloud database service, integrated into Microsoft Azure AI Foundry. The goal is to allow customers to build retrieval-augmented generation or RAG applications by combining MongoDB’s data capabilities with Azure OpenAI Service.

With the integration, developers can enhance large language models with proprietary data stored in MongoDB Atlas without additional coding or pipeline building, streamlining the process of creating chatbots, copilots and enterprise AI applications. Azure AI Foundry’s “Chat Playground” feature further simplifies development by enabling real-time testing of LLMs with enterprise data before deployment.

The integration offers users a way to augment generative AI models with their own data to ensure their applications are grounded in up-to-date context. The combination of MongoDB Atlas and Azure AI Foundry offers flexibility and efficiency in leveraging enterprise data for advanced AI use cases.

In the second announcement, real-time data analytics with Microsoft Fabric, MongoDB Atlas now supports Open Mirroring in Microsoft Fabric for a near real-time connection with OneLake. The capability synchronizes data between the two platforms, allowing businesses to generate timely analytics, AI predictions, and business intelligence reports.

Through enabling real-time insights, businesses can leverage MongoDB’s operational data and Microsoft Fabric’s analytics tools to drive strategic decisions and optimize performance across diverse use cases, from AI-powered predictions to reporting.

The final announcement allows users to “deploy MongoDB their way” with MongoDB Enterprise Advanced on Azure Marketplace, introducing greater flexibility for organizations deploying applications in Kubernetes environments. With Azure Arc-enabled Kubernetes, customers can deploy and self-manage MongoDB instances across on-premises, multicloud and edge environments.

“By integrating MongoDB Atlas with Microsoft Azure’s powerful AI and data analytics tools, we empower our customers to build modern AI applications with unparalleled flexibility and efficiency,”Sandy Gupta, vice president of partner development ISV at Microsoft, said in a statement.

Sahir Azam, chief product officer of MongoDB, spoke with theCUBE, SiliconANGLE Media’s live streaming studio, in May, when he discussed how the company is strengthening its database ecosystem and advancing artificial intelligence capabilities with key partners:

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MongoDB Announces Expanded Collab with Microsoft – Datanami

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CHICAGONov. 19, 2024 — Today at Microsoft Ignite, MongoDB, Inc. announced an expanded collaboration with Microsoft that introduces three new capabilities for joint customers. First, customers building applications powered by retrieval-augmented generation (RAG) can now select MongoDB Atlas as a vector store in Microsoft Azure AI Foundry, combining MongoDB Atlas’s vector capabilities with generative AI tools and services from Microsoft Azure and Azure Open AI. Meanwhile, users looking to maximize insights from operational data can now do so in near real-time with Open Mirroring in Microsoft Fabric for MongoDB Atlas. And the launch of MongoDB Enterprise Advanced (EA) on Azure Marketplace for Azure Arc-enabled Kubernetes applications enables organizations that operate across on-premises, multi-cloud, and edge Kubernetes environments to choose MongoDB. With these capabilities, MongoDB is meeting customers where they are on their innovation journeys, and making it easier for them to unleash the power of data.

Through the strengthened MongoDB-Microsoft relationship, customers will be able to:

  • Enhance LLMs with proprietary data stored in MongoDB Atlas: Accessible through Azure AI Foundry, the Azure OpenAI Service allows businesses to develop RAG applications with their proprietary data in combination with the power of advanced LLMs. This new integration with Azure OpenAI Service enables users to take enterprise data stored in MongoDB Atlas and augment LLMs with proprietary context. This collaboration makes it easy to build unique chatbots, copilots, internal applications, or customer-facing portals that are grounded in up-to-date enterprise data and context. Developers are now able to add MongoDB Atlas as a vector data store for advanced LLMs, all without the need for additional coding or pipeline building. And through Azure AI Foundry’s “Chat Playground” feature, developers can quickly test how their enterprise data and selected LLM function together before taking it to production.
  • Generate key business insights faster: Microsoft Fabric empowers businesses to gather actionable insights from their data on an AI-powered Analytics platform. Now Open Mirroring in Microsoft Fabric with MongoDB Atlas will allow for a near real-time connection, to keep data in sync between MongoDB Atlas and OneLake in Microsoft Fabric. This enables the generation of near real-time analytics, AI-based predictions, and business intelligence reports. Customers will be able to seamlessly take advantage of each data platform without having to choose between one or the other, or without worrying about maintaining and replicating data from MongoDB Atlas to OneLake.
  • Deploy MongoDB Their Way: The launch of MongoDB EA on Azure Marketplace for Azure Arc-enabled Kubernetes applications gives customers greater flexibility when building applications across multiple environments. With MongoDB EA, customers are able to deploy and self-manage MongoDB database instances in the environment of their choosing, including on-premises, hybrid, and multi-cloud. The MongoDB Enterprise Kubernetes Operator, part of the MongoDB Enterprise Advanced offering, enhances the availability, resilience, and scalability of critical workloads by deploying MongoDB replica sets, sharded MongoDB clusters, and the Ops Manager tool across multiple Kubernetes clusters. Azure Arc further complements this by centrally managing these Kubernetes clusters running anywhere—in Azure, on premises, or even in other clouds. Together, these capabilities ensure that customers can build robust, distributed applications by leveraging the resilience of a strong data layer along with the central management capabilities that Azure Arc offers for its Arc-enabled Kubernetes applications.

“We frequently hear from MongoDB’s customers and partners that they’re looking for the best way to build AI applications, using the latest models and tools.” said Alan Chhabra, Executive Vice President of Partners at MongoDB. “And to address varying business needs, they also want to be able to use multiple tools for data analytics and business insights. Now, with the MongoDB Atlas integration with Azure AI Foundry, customers can power gen AI applications with their own data stored in MongoDB. And with Open Mirroring in Microsoft Fabric, customers can seamlessly sync data between MongoDB Atlas and OneLake for efficient data analysis. Combining the best from Microsoft with the best from MongoDB will help developers push applications even further.”

Joint Microsoft and MongoDB customers and partners welcome the expanded collaboration for greater data development flexibility.

Trimble, a leading provider of construction technology, delivers a connected ecosystem of solutions to improve coordination and collaboration between construction teams, phases and processes.

“As an early tester of the new integrations, Trimble views MongoDB Atlas as a premier choice for our data and vector storage. Building RAG architectures for our customers require powerful tools and these workflows need to enable the storage and querying of large collections of data and AI models in near real-time,” said Dan Farner, Vice President of Product Development at Trimble. “We’re excited to continue to build on MongoDB and look forward to taking advantage of its integrations with Microsoft to accelerate our ML offerings across the construction space.”

Eliassen Group, a strategic consulting company that provides business, clinical, and IT services, will use the new Microsoft integrations to drive innovation and provide greater flexibility to their clients.

“We’ve witnessed the incredible impact MongoDB Atlas has had on our customers’ businesses, and we’ve been equally impressed by Microsoft Azure AI Foundry’s capabilities. Now that these powerful platforms are integrated, we’re excited to combine the best of both worlds to build AI solutions that our customers will love just as much as we do,” said Kolby Kappes, Vice President – Emerging Technology, Eliassen Group.

Available in 48 Azure regions globally, MongoDB Atlas provides joint customers with the powerful capabilities of the document data model. With versatile support for structured and unstructured data, including Atlas Vector Search for RAG-powered applications, MongoDB Atlas accelerates and simplifies how developers build with data.

“By integrating MongoDB Atlas with Microsoft Azure’s powerful AI and data analytics tools, we empower our customers to build modern AI applications with unparalleled flexibility and efficiency,” said Sandy Gupta, VP, Partner Development ISV, Microsoft. “This collaboration ensures seamless data synchronization, real-time analytics, and robust application development across multi-cloud and hybrid environments.”

To read more about MongoDB Atlas on Azure go to https://www.mongodb.com/products/platform/atlas-cloud-providers/azure.

About MongoDB

Headquartered in New York, MongoDB’s mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. Built by developers, for developers, MongoDB’s developer data platform is a database with an integrated set of related services that allow development teams to address the growing requirements for a wide variety of applications, all in a unified and consistent user experience. MongoDB has more than 50,000 customers in over 100 countries. The MongoDB database platform has been downloaded hundreds of millions of times since 2007, and there have been millions of builders trained through MongoDB University courses. To learn more, visit mongodb.com.


Source: MongoDB

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