Month: April 2025

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Polymer Capital Management HK LTD acquired a new position in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) in the fourth quarter, according to its most recent Form 13F filing with the SEC. The firm acquired 3,937 shares of the company’s stock, valued at approximately $917,000.

Several other large investors have also recently made changes to their positions in the stock. Hilltop National Bank lifted its stake in MongoDB by 47.2% in the fourth quarter. Hilltop National Bank now owns 131 shares of the company’s stock worth $30,000 after acquiring an additional 42 shares during the last quarter. Avestar Capital LLC increased its stake in MongoDB by 2.0% in the fourth quarter. Avestar Capital LLC now owns 2,165 shares of the company’s stock valued at $504,000 after purchasing an additional 42 shares during the period. Aigen Investment Management LP raised its holdings in shares of MongoDB by 1.4% in the fourth quarter. Aigen Investment Management LP now owns 3,921 shares of the company’s stock valued at $913,000 after buying an additional 55 shares during the last quarter. Perigon Wealth Management LLC boosted its stake in shares of MongoDB by 2.7% during the fourth quarter. Perigon Wealth Management LLC now owns 2,528 shares of the company’s stock worth $627,000 after buying an additional 66 shares during the period. Finally, O Shaughnessy Asset Management LLC increased its position in MongoDB by 4.8% in the 4th quarter. O Shaughnessy Asset Management LLC now owns 1,647 shares of the company’s stock valued at $383,000 after acquiring an additional 75 shares during the period. 89.29% of the stock is currently owned by institutional investors and hedge funds.

Insiders Place Their Bets

In other news, Director Dwight A. Merriman sold 885 shares of the firm’s stock in a transaction on Tuesday, February 18th. The shares were sold at an average price of $292.05, for a total value of $258,464.25. Following the sale, the director now owns 83,845 shares in the company, valued at approximately $24,486,932.25. This trade represents a 1.04 % decrease in their ownership of the stock. The sale was disclosed in a legal filing with the SEC, which is accessible through this hyperlink. Also, insider Cedric Pech sold 1,690 shares of the company’s stock in a transaction on Wednesday, April 2nd. The shares were sold at an average price of $173.26, for a total transaction of $292,809.40. Following the transaction, the insider now owns 57,634 shares of the company’s stock, valued at approximately $9,985,666.84. The trade was a 2.85 % decrease in their ownership of the stock. The disclosure for this sale can be found here. Insiders sold a total of 58,060 shares of company stock valued at $13,461,875 in the last quarter. Insiders own 3.60% of the company’s stock.

MongoDB Trading Down 5.5 %

MongoDB stock opened at $154.39 on Monday. The company has a market capitalization of $12.53 billion, a P/E ratio of -56.35 and a beta of 1.49. MongoDB, Inc. has a 1-year low of $146.50 and a 1-year high of $387.19. The firm’s fifty day simple moving average is $236.68 and its 200-day simple moving average is $261.78.

MongoDB (NASDAQ:MDB – Get Free Report) last announced its quarterly earnings results on Wednesday, March 5th. The company reported $0.19 earnings per share for the quarter, missing analysts’ consensus estimates of $0.64 by ($0.45). The company had revenue of $548.40 million during the quarter, compared to analyst estimates of $519.65 million. MongoDB had a negative return on equity of 12.22% and a negative net margin of 10.46%. During the same period in the prior year, the firm earned $0.86 EPS. On average, equities research analysts predict that MongoDB, Inc. will post -1.78 earnings per share for the current year.

Analyst Ratings Changes

Several equities research analysts recently weighed in on MDB shares. KeyCorp downgraded MongoDB from a “strong-buy” rating to a “hold” rating in a report on Wednesday, March 5th. Monness Crespi & Hardt upgraded MongoDB from a “sell” rating to a “neutral” rating in a research report on Monday, March 3rd. Morgan Stanley lowered their price target on MongoDB from $350.00 to $315.00 and set an “overweight” rating on the stock in a report on Thursday, March 6th. Daiwa Capital Markets began coverage on shares of MongoDB in a report on Tuesday, April 1st. They issued an “outperform” rating and a $202.00 price target on the stock. Finally, Wedbush dropped their price target on MongoDB from $360.00 to $300.00 and set an “outperform” rating for the company in a research report on Thursday, March 6th. Seven research analysts have rated the stock with a hold rating, twenty-four have assigned a buy rating and one has given a strong buy rating to the company’s stock. Based on data from MarketBeat.com, MongoDB currently has an average rating of “Moderate Buy” and a consensus price target of $312.84.

Get Our Latest Research Report on MDB

MongoDB Company Profile

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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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Transcript

Brodskyi: My name is Mykhailo Brodskyi. As Principal Software Architect, I focus on platform security and cloud migration. I’m going to walk you through top four security risk categories in software supply chain and show you how to mitigate them effectively. I will share real case studies from our projects, highlighting strategies that protect systems from vulnerabilities, and ensure security and resilience of your platform.

Here is how I’m going to do it. First, we’ll start with the challenges that we have in FinTech industries. Then, we will deep dive in the risk categories. I will show practical examples of how to mitigate them. I prepared some case studies from our real projects. Then, I will show a real hands-on demo.

Looking Into the Future and Reflecting on the Past

Do any of you know what significant event related to security takes place here in Munich every year? It’s not Oktoberfest. Any ideas, every year in winter? Munich Security Conference. It has been a global stage for discussing international security issues. Here, we are not talking about geopolitical issues. We are talking about something equally similar, software security. As Munich Security Conference, that shapes global security policies, our goal is how we shape our software security chain.

Uncover FinTech Landscape Challenges

Let’s dive into the FinTech landscape. The FinTech ecosystem is driven by key serious business domains, such as customer onboarding, payment processing. Each of these domains ensure smooth operations of financial services. Each domain houses numerous business applications inside. For example, for instance, customer onboarding. There is an application for know your customers. For payment processing, we have an application that is responsible for APMs processing, alternative payment processing, credit card transaction processing, and more. Each of these domains work under some framework that operates with standards, laws, and principles.

For example, payment processing is subject to PSD2, while fraud detection is mitigated and operated with AMLD. Why do we have all these principles, laws, and standards, all these regulations that we have in FinTech, and in other areas as well? The answer is simple, that’s risks. All these regulations are designed to mitigate some risk: financial risk, reputation risk. This law exists right now, that helps organizations to mitigate such risk. As you can see that the FinTech landscape is very complex due to these regulations that we have, and also integrations with other applications. It’s clear that we need to have a really robust approach, how we can secure all our applications inside our landscape.

Explore Software Supply Chain

Let’s dive into software supply chain. I would like to begin with something that we are all familiar with, that’s our physical goods supply chain. The journey begins with upstream supplier, that delivers raw materials to manufacturer, and then customer is going to receive the final product. Similar to software development, we rely on suppliers such as third-party libraries, dependencies, and tools. Everything goes into development flow. In case any of this component is compromised, our final product is at risk. Development organization in software supply chain security, it’s similar to manufacturer. Inside we have the different stages of the process.

The process starts with development, goes to integration, and then end with deployment. Each of this stage relies again on third-party libraries, tools, and dependencies. It’s clear that we need to have an approach to secure all these dependencies. That’s why compliance and security, it’s not the static flow, it’s a static layer in our software supply chain. It’s dynamic and it’s going to be integrated in each step of the process. Based on this overview and understanding of software supply chain, we can create different categories. The main category that’s related to our third-party libraries and tools. The second category that’s related to our internal development. Then we have process and all this risk that’s related to our delivery and deployment, and governance, and security testing.

Address Mitigation Strategies for Third-Party Risks

Now I’m going to talk in detail about all these categories that we have. Let’s start with the first one. Let’s start with our software development chain, when we have all these components. The first approach and first what we need to understand and ask when we work with third-party libraries, they need to be certified. In this case, we can make sure that our final product that is going to be developed based on these libraries is also going to be protected. Then we can integrate software composition analysis. This approach will help us to mitigate these issues and risk that’s related to third-party libraries and tools. Software composition analysis, there are multiple steps there.

First component, that’s dependency analysis, they analyze all our dependencies. Then vulnerability detection, because this tool already has integration with internal database, which is possible to monitor and understand if there are any issues in our pipeline. Then, also module that’s responsible for license compliance. In our organization, usually we have private artifact repository. Then we have version control system. Our journey starts with fetching these dependencies and trying to build our project. This tool, software composition analysis, will help us to analyze all these dependencies that we have there. The next step is going to be build our pipeline. We can integrate some job in this pipeline that is going to monitor all these dependencies. Then, also to provide some notification to us in case we have any issues.

Now we can go even further and try to mitigate and build even more layers of security while we are talking about third-party libraries. Let’s imagine that we would like to start working on some new feature, and we need to use some new third-party libraries that are not available in our repository. First, we have the development team, then we have cyber team, and we have our supplier. In this case, that supplier who is going to deliver third-party libraries and tools. A developer is going to select this component that is needed to be integrated in our private artifact repository, and select in public artifact repository. Then it’s going to be added first in intermediate repository, where we’re going to trigger this vulnerability scanning, what I mentioned earlier, and license scanning.

Only after we perform vulnerability scan, license compliance check, and we will be ready for the further check, we can include this library into the next repository to secure the repository. This repository is going to be integrated and continuously execute some monitoring tool. We will try to identify new vulnerabilities there. Try to also check licenses, what we have. Once we receive a good sign from this validation, we can include this library to our development repository. This zero-trust dependency management really will help us to minimize all these risks that’s related to dependencies and tool. Finally, at the end, we can execute verification. We can execute all security verification, SAST and DAST. Then perform penetration test.

Let’s try to summarize what we need to do for mitigating third-party dependencies. We need to ask about licenses. They need to be compliant with that. Then we need to integrate. Of course, use only private artifact repository. To build several layers of repository in case it’s needed, depends on your business domain. Then, to integrate continuous verification in your pipeline.

Secure Internal Development

Let’s go to the internal development. Here there is a best practice in case you would like to improve your security development. Try to integrate some existing principles and standards. For example, in our FinTech industry, there is a common set of rules and standards, PCI DSS. All payment processing domains should follow these standards. Let’s talk about these standards. The definition. First of all, it’s a set of standards that explain in which way we need to implement, and how we need to build our network. Also, there are other standards as well. This standard is super important for FinTech. There are six groups of requirements. One group is focused on network segmentation. Then it’s related to how you build access control to your system and your environment. Also related to how you’re going to monitor your environments and your applications. There are stages of process inside this flow, in case you would like to apply these standards for your organization.

First process that we need to discover, we need to scope and we need to analyze your infrastructure and your landscape, what you have. What does it mean for this? You identify all components in this chain. You also analyze which type of data you store there. Based on this information, then you can apply different segmentation strategies. That’s number one, scoping, organization analysis. Then, categorization. PCI DSS explains different categories for systems that you need to apply. It depends on which data you store there. First, that’s CD system, cardholder data environment. That’s the environment, where do you process transactional data or you store transactional data? Everything that’s related to simple transactions, everything that’s related to cardholder data.

Then, connected-to: you have a separated system that doesn’t store any cardholder related data or customer data. This system is just only connected to cardholder data environment that process or store related data. Then you have security impacts in your system. A good example, some configuration management, when you store configuration for a particular microservice or particular customer. Out-of-the scope system. Out-of-the scope means that the system is not going to be under PCI DSS. Its system doesn’t contain any credit cardholder data. It can be completely isolated from our main environment. The next step, we need to implement all these segmentation and controls. Then, we need to implement validation. We need to maintain this segmentation. It means, for example, in our industry, two times per year, we need to complete PCI DSS. Every time we need to update this documentation, we need to show that we have a monitoring system in place. That’s why it’s very important.

Examine Real-World Case Study

I would like to show a real example. It’s a very interesting story of what we already started. Our company, the main goal is process transactions. All our systems that we have currently, they were hosted in a private data center. We initiated a really complex project to migrate all our 100-plus application modules from a physical data center to the cloud. During this migration journey, we had to review all our current segmentation approaches that we have, all our communication strategies. I’m trying to show some small set of architecture where we try to apply all these principles. Then, somehow, to bring architectural improvements during this cloud migration. Holistic architecture. In payment processing, there are different layers of architecture. Here we have, first, input channel, where we need to obtain this transaction. Then, to send to our payment processing gateway. There are different input channels. We can use mobile devices. We can integrate with external websites. Or it can be integration with external systems, with airlines, for example.

In this case, we have environment, when we need to consume these transactions. Right now, there is a component input channel. We are going to receive this transaction from physical terminal. Then, if you use different currency, and you would like, for example, to travel somewhere from Europe to U.S., or in other countries, you can ask which currency you would like to pay. For this currency conversion exchange, there is a separate component, or even a separate service is responsible and integrated to payment industry. That’s currency conversion service. This component is responsible to decide, which option is better and how we are going to exchange it. Then, we are going to process this transaction.

In this case, payment processing service is going to be connected to one of these card schemes that we have. Once we started to analyze the current architecture, what we had previously in our data center, the landscape is super complex. Sometimes there is shared database approach, and 10 applications connected to one database. Of course, in cloud, it’s difficult to somehow troubleshoot this issue, and try to implement some new features. That’s why we started thinking, let’s try to separate these components. Let’s remember which categories we have. That’s CD system that’s responsible for cardholder data environment, connected-to system, security-impacting systems, and out-of-the scope system.

Obviously, the transaction is going to be received, first of all, by input channel. Then, processed by payment service. Then, sent to card schemes. It means that these two services, it goes to CD bucket. Then we can separate and we can move currency conversion service independently to another zone. In this case, let’s assume that we can include the service in non-card data environment. What else do we need to follow in order to build this separation, and, first of all, to move this service to the out-of-the scope category? We need to implement access control. We need to have authentication and encryption. It means that it’s not just possible payment service is going to talk directly to currency conversion service, we need to authenticate this service. We need to implement some mechanism of authorization, how we are going to do it. Also, we need to put it in a separate zone.

I understand that there are so many people from different industries. I try to think, how can you use this information and apply this information and deploy already, let’s say, next week? Try to think from this categorization point of view, and these separate categories that we have in FinTech industry, in PCI DSS, and build the same categorization and segmentation level on your system. Let’s say that we are talking about healthcare. We can create and build a separate environment where we are going to put applications that are related to storing and processing some personal information. Then, you can store this information that’s related to health insurance, health state, for example, of this person, in this separate environment and even in separate application. Then, construction. I remember back in the past, in my experience, construction domain, we had microservice architecture. All these services were just deployed in one single zone.

Of course, from communication point of view and then security point of view, it’s a really bad approach. In case we are talking about construction domain, it’s better also, again, all related customer information put in one service, in one database, and then to separate in a completely different environment. Then, real estate. The same goes to this domain. Customer related information, we put in one database, even in another environment. All information about objects and real estate properties, you can put in separate environment, because also you need to protect this information. Somehow, for their competitors, it’s going to be super important. Energy sector, all information, for example, telemetry information, information about some plants manufacturers, you can also separate in completely different environment and zone. That’s cross-industry applications, and how we can build this inspiration and apply for other industries.

Approach number two, that also goes to secure development practices. This approach is successfully applied in the current company and also in the previous one that was related to network security protection, so threat modeling. What does it mean? There are three questions that we need to answer. First, we need to understand what we are going to build, which potential issues we can have, and how we are going to mitigate them. Idea that, in case you have any design process in your organization or you have architectural process, you can integrate threat modeling on an earlier stage of your development. That’s exactly what we did in the current company. It means that on this earlier stage, you can, together with your development team, think with all these vulnerabilities, potential vulnerabilities that we have right now, and try to mitigate already on the earlier stage of your design, architectural draft version.

It helped us multiple times, because it’s reputation risk, and it’s development risk, and even some additional costs then which we need to fix later on. Key components, so, first of all, we identify the same, there is some similarity that’s related to PCI DSS, that we have scoping, here we have asset identification. We are going to analyze all our components, what we have in our system. Then we are trying to also review current threats that we have, and try to build mitigation for this threat. There are some benefits. First of all, we can increase time to market. We are not going to spend some additional time for testing or verification, and then fixing these issues. We can improve our application security. Then, it’s also to use some best practices, some frameworks that we have already in this industry. There are so many approaches of what we have. We applied several times a straight approach for threat modeling.

I’m going to show you right now a DFD diagram. That’s a diagram that is going to be compiled and created during this threat modeling process. With this diagram, you can identify external boundaries of what you have, internal systems that we have right now, and then the processes and storage. Then we will try to map all these issues that we can have, and identify what is the communication flow from one service to another service, and then try to build some additional security layers. For example, what is going to be authentication and authorization? Do we have any encryption there? Which type of information do we store in this database? Then it’s going to be everything documented. It’s going to be reviewed together with our cyber experts, with our architects. Then, to make sure that we are not going to introduce any additional risk there. This approach is possible to automate with different tools.

Even from Microsoft, there is automation. It’s even possible to use some AI approach to analyze and build some list of the risks potentially that you can have. Once we applied this approach, we were able to identify some potential vulnerabilities which were not identified during penetration test, and that was really a red sign for us, and we spent immediately to resolve these issues that were related to service-to-service communication, and which data do we send there. These two big issues were identified, especially because of this process that we applied.

Let’s summarize how we can mitigate internal development. First of all, that’s one more time, apply existing security standards, what we have right now. In case you’re in healthcare, you can try to apply these industry standards, what I just explained recently. Also, security review, really good code review, and threat modeling.

Mitigate Software Delivery and Governance Risks

Let’s move to software delivery deployment, and governance and security testing categories. I would like to show you how we are going to mitigate these delivery risks, what we have during our deployment. Let’s, one more time, go back to our process that we have, our development organization, with different stages during this flow. First issue, what we can have, that’s version control system stage. We can, by accident, expose some credential secret. There were so many examples in GitLab, in GitHub, that were found in public repositories, all secrets. It can be a really big issue to all systems that we have.

This issue, we can mitigate with secret management. Let’s say that we are together right now, building some software, building a new feature. Of course, there are so many available secrets management tools for our platform. There are platform agnostic, that we don’t care which cloud provider we are going to use. There are some cloud providers that are container native. Then we have some tools that’s DevOps focused. Also, in a separate category, I added identity management system. That’s not related to all of them, but it’s somehow in the first layer, how we are going to protect our access. Let’s say, because during our cloud migration, we are going to deploy everything that we have in a data center to Azure.

In this case, let’s select Key Vault Secret Manager in Azure. Then we can go and we can move to the build stage. Here there is a risk that our build infrastructure can be compromised. In this case, we can use additional security controls. That’s what we have in all version control systems, in Git or GitLab. Then we can also include and implement SAST and DAST, static and dynamic security test and analysis. For static testing, we have SonarQube. For dynamic, we have Acunetix and Qualys. Let’s say that for security controls, we will select SonarQube and Acunetix. That’s what we use in our current company. Then, package stage, insecure artifact. Insecure artifact, I explained previously, that’s really zero-trust approach and CCI approach as well. It can also be integrated. Another approach is source code signing. There are different tools for this: Cosign, Notary, pipeline code signing in Azure. We are going to select Cosign.

Then, let’s move to the testing stage and deployment stage. Insufficient security testing. I have seen multiple times that we do not pay really big attention. There are no multiple security test cases available to mitigate and complete final verification of your software. That’s why it’s a really good approach first to integrate SAST, DAST. Depending on your domain industry, integrate also penetration test for your organization. This approach even was applied earlier in previous companies, related to construction or network and security verification testing. All these issues we can mitigate with security controls and secret management. Also, there is a point here. Have you already integrated a secret management tool in your pipeline? Also, there is verification. It’s very important that these keys that you have in this tool, that they have expiration date. Otherwise, it’s not going to be compliant, in case you use any tool that’s integrated with your environment, and then can monitor it.

Hands-On Demo

Now, I would like to go to the demo that I prepared. Specifically, I’m going to focus on the third-party libraries’ mitigation, and show you how this artifact, we can generate a software bill of material. We can use in our verification and analysis. Here, I have a simple project in GitHub. There is a microservice with some dependencies. It’s a really simple microservice. In the pipeline, we have two different stages. It’s build and generate software bill of material. Then there is stage when we build integration with Snyk. There are two stages. First, we generate this software bill of material. Then we use this artifact for further scanning and verification. That’s related to software composition analysis. Also, there is a dashboard of this tool. Right now, I don’t have any critical or high critical vulnerabilities. Also, I integrated this Nexus Repository. Right now, it’s running on my EC2 instance. Here, we have different types of repositories that I created. First is Maven Central Repository.

There is GitHub repository here, integrated pipeline there. There are multiple stages. First, we have Snyk scan integration. I’m going to trigger right now the tool of this build. Then, I have integration with this dashboard. Also, there are no high critical vulnerabilities. There are multiple repositories. This repository, it’s related to my dependencies, what I have in the current project. Then, I have a separate repository when I’m going to publish my artifact, which I’m going to build. Here, you can see the separation of these two repositories. That’s EC2 configuration of security groups. Then, I’m going to change this form configuration. Right now, everything is green. Here, I’m going to introduce non-dependency, Log4j dependency, and see what is going to be the behavior of this tool and how it’s going to be integrated in this dashboard. I’m going to comment out this dependency, and trigger a build. Build started. It was completed. Now, you can see that new issues were introduced.

Based on this artifact that was created, this tool is integrated, and continuously analyzing my software bill of material. Then, I’m going to remove this dependency, and generate this file one more time. At the end, it’s a big artifact. It’s a big XML or JSON file, with all these dependencies that you have in the application. Then, this file, now you can see that’s integrated already in the pipeline. Here, you can even build some business logic on the current pipeline on top. You can establish continuous monitoring. Then, you can use this file in order to share, and then trigger a compliance check. Then, you can use this outcome for your regulation and compliance process. I remember back in the past, in one of the projects, the compliance team asked the development team, can you please create an Excel file and put all the dependencies in this file? We were really surprised. It’s really manual work. It’s better to implement and integrate software bill of materials. Then, to have some stage in the pipeline that the security and risk team can analyze and can approve. At the end, this issue is mitigated, resolved, and dashboard is green.

Questions and Answers

Nikolai: You have a step, a Snyk scan, but what if a dependency was found after the build finished and it already was deployed? Do you continuously rescan all your dependencies, and then notify and rebuild all the services which depend on this dependency?

Brodskyi: A question about the integration, about how we automate, and how we’re going to notify and protect our next deployment step.

Nikolai: Not next, but if it’s already in production, and next day we found some zero-day vulnerability in the dependency which we already deployed.

Brodskyi: In this case, you need to establish patch management, and make sure that your organization is able to provide this process where you can mitigate and deliver this simple fix as soon as possible. That’s only related to, what is your patch process. In case it’s happened, then in our organization there is an SLA. We need to react in this period of time. In case it’s not happened, then it’s going to be a problem, a reputation risk for our company. That’s patch processing what we have. We have SLA, how fast we’re going to react, and what is going to be the mitigation.

Nikolai: To know that you have this vulnerability, how you go about it.

Brodskyi: To know it, because of the PCI DSS, we need to have a really strong monitoring system. We have a monitoring system that is going to notify each team immediately, all development teams. This monitoring tool is integrated with all other notification channels that we have: Teams, for example, emails, and so on. First of all, the operational support team is going to receive this alarm. Then, development team is going to receive all this notification.

Nikolai: More practical, like, for example, I have a container inside my private registry. I know that, for example, AWS Inspector continuously does this scanning of the containers if you keep it in their registry. As soon as they found that in your container you have some vulnerability, you can configure this notification pipeline that will send you a message. Then you can rebuild your artifact with a fresh dependency, and then deploy it again. How do you do that? What tools do you use?

Brodskyi: For container scanning, we use Azure tool. We integrated this tool there. Then, for this type of application that’s not in the cloud right now, we use Acunetix, Qualys, SonarQube, and, of course, penetration test in case we are going to release a very business critical update.

Shashi: These DORA regulations are coming from next year, which have to be adapted by, I think, all the European companies regardless of the industry. These pipelines which you showed us, will these have to be adapted and become more faster because the SLAs might be much smaller because of this regulation? If yes, then, is there already something going on on these architectures which you have just shown in this talk?

For example, in our company, we use Black Duck for software composition analysis. Because in our company we have C++ based libraries and some of them take really long to build, we build them locally on our infrastructure. Let’s say we have a CVE found, like the guy asked, zero-day CVE found, how would we use this thing which you showed us just now to be compliant with the DORA regulations that we have immediately a new batch created and delivered to the end customer?

Brodskyi: There is a new regulation coming to the FinTech industry, DORA. Also, regarding the pipeline, how is it going to be adapted?

Regarding DORA regulation, that’s particularly related to resilience and how your system platform is going to be resilient. How do you deploy? Also, it’s about platform security. Regarding the deployment, for sure, right now we are working to improve our deployment. Because of the cloud migration, we integrated all these DevOps principles in order to speed up. Latest example is that in order to complete some verification of our big APM processing, alternative payment method processing application, we spent several hours in this cloud migration, optimizing the processing strategy and optimizing the feedback loop in this pipeline. In GitLab plus Argo CD, we are able to speed up our deployment. This DORA regulation in our company is running in parallel, because we are doing these improvements not because of this regulation, because of our big cloud migration journey and to improve speed to market.

How do we react to some vulnerabilities that we have in production?

Regarding the vulnerabilities, in case we have them in production, we have a very complex monitoring tool. We have our support team that is looking every time on this monitoring tool. Also, we have notifications. Once they receive, we immediately react. All scrum teams, depending on the application or microservice, focus on this particular vulnerability. Then it’s going to be delivered. We will use all these tools in our pipeline in order to verify. Then it’s going to be deployment using this patch processing and hotfix deployment.

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Google Cloud has announced a suite of new tools and features designed to help organizations reduce costs and improve efficiency of AI workloads across their cloud infrastructure. The announcement comes as enterprises increasingly seek ways to optimize spending on AI initiatives while maintaining performance and scalability.

The new features focus on three key areas: compute resource optimization, specialized hardware acceleration, and intelligent workload scheduling. These improvements aim to address one of the primary challenges enterprises face when deploying AI at scale—balancing innovation with cost management.

In the announcement, Google Cloud’s VP of AI Products said:

Organizations are increasingly looking for ways to optimize their AI costs without sacrificing performance or capability, these new features directly address that need by providing more efficient ways to run machine learning training and inference.

Google Cloud’s approach begins with strategic platform selection. Organizations now have multiple options ranging from fully-managed services to highly customizable solutions. Vertex AI offers a unified, fully managed AI development platform that eliminates infrastructure management concerns, while Cloud Run with GPU support provides a scalable inference service option. For long-running tasks, Cloud Batch combined with Spot Instances can significantly reduce costs. Organizations with existing Kubernetes expertise may benefit from Google Kubernetes Engine (GKE), while those requiring maximum control can utilize Google Compute Engine.

A key recommendation focuses on optimizing container performance. When working with inference containers in environments like GKE or Cloud Run, Google advises keeping containers lightweight by externally storing models using Cloud Storage with FUSE, Filestore, or shared read-only persistent disks. This approach dramatically reduces container startup times and improves scaling efficiency—critical factors in managing both performance and costs.

Storage selection emerges as another critical factor in optimization. Google Cloud recommends Filestore for smaller AI workloads, Cloud Storage for object storage at any scale, and Cloud Storage FUSE for mounting storage buckets as a file system. For workloads requiring lower latency, Parallelstore provides sub-millisecond access times, while Hyperdisk ML delivers high-performance storage specifically engineered for serving tasks.

To prevent costly delays in resource acquisition, Google Cloud emphasizes the importance of Dynamic Workload Scheduler and Future Reservations. These tools secure needed cloud resources in advance, guaranteeing availability when required while optimizing the procurement process for popular hardware components.

The final strategy addresses deployment efficiency through custom disk images. Rather than repeatedly configuring operating systems, GPU drivers, and AI frameworks from scratch, organizations can create and maintain custom disk images that allow new, fully-configured workers to deploy in seconds rather than hours.

AI cost management has become increasingly critical across industries, in response to the growing demand for more efficient and cost-effective AI infrastructure, both AWS and Microsoft Azure have also ramped up their efforts to support enterprise AI workloads. AWS has introduced new cost-aware tools within its SageMaker platform, including Managed Spot Training and model monitoring capabilities to help users optimize both performance and budget. Similarly, Azure is enhancing its AI offering through Azure Machine Learning with features like intelligent autoscaling, reserved capacity pricing, and seamless integration with Azure Kubernetes Service (AKS) for better workload orchestration.

Like Google Cloud, both AWS and Azure are emphasizing hybrid flexibility, storage optimization, and GPU acceleration to give enterprises more control over how they scale and spend. This convergence signals a competitive push across cloud providers to address the pressing challenge of AI cost management while still empowering innovation at scale.

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Alliancebernstein L.P. cut its position in MongoDB, Inc. (NASDAQ:MDB – Free Report) by 5.9% in the fourth quarter, according to its most recent Form 13F filing with the SEC. The institutional investor owned 87,001 shares of the company’s stock after selling 5,503 shares during the quarter. Alliancebernstein L.P. owned approximately 0.12% of MongoDB worth $20,255,000 at the end of the most recent reporting period.

Other hedge funds have also modified their holdings of the company. Hilltop National Bank raised its holdings in shares of MongoDB by 47.2% in the fourth quarter. Hilltop National Bank now owns 131 shares of the company’s stock worth $30,000 after buying an additional 42 shares during the last quarter. NCP Inc. acquired a new position in MongoDB in the 4th quarter worth approximately $35,000. Continuum Advisory LLC increased its holdings in MongoDB by 621.1% in the 3rd quarter. Continuum Advisory LLC now owns 137 shares of the company’s stock worth $40,000 after buying an additional 118 shares during the period. Versant Capital Management Inc raised its position in MongoDB by 1,100.0% during the 4th quarter. Versant Capital Management Inc now owns 180 shares of the company’s stock worth $42,000 after buying an additional 165 shares during the last quarter. Finally, Wilmington Savings Fund Society FSB acquired a new stake in MongoDB during the 3rd quarter valued at approximately $44,000. 89.29% of the stock is owned by hedge funds and other institutional investors.

MongoDB Stock Performance

NASDAQ:MDB opened at $147.38 on Tuesday. MongoDB, Inc. has a 1 year low of $140.78 and a 1 year high of $387.19. The stock has a market cap of $11.97 billion, a P/E ratio of -53.79 and a beta of 1.49. The stock has a 50-day simple moving average of $234.34 and a 200-day simple moving average of $260.82.

MongoDB (NASDAQ:MDB – Get Free Report) last announced its quarterly earnings results on Wednesday, March 5th. The company reported $0.19 earnings per share (EPS) for the quarter, missing analysts’ consensus estimates of $0.64 by ($0.45). MongoDB had a negative net margin of 10.46% and a negative return on equity of 12.22%. The business had revenue of $548.40 million during the quarter, compared to analyst estimates of $519.65 million. During the same period last year, the company earned $0.86 earnings per share. On average, equities research analysts predict that MongoDB, Inc. will post -1.78 EPS for the current year.

Wall Street Analysts Forecast Growth

A number of analysts recently issued reports on the stock. Canaccord Genuity Group lowered their price target on shares of MongoDB from $385.00 to $320.00 and set a “buy” rating on the stock in a report on Thursday, March 6th. UBS Group set a $350.00 price target on MongoDB in a report on Tuesday, March 4th. Oppenheimer dropped their price objective on MongoDB from $400.00 to $330.00 and set an “outperform” rating on the stock in a report on Thursday, March 6th. Stifel Nicolaus reduced their target price on MongoDB from $425.00 to $340.00 and set a “buy” rating for the company in a research note on Thursday, March 6th. Finally, Needham & Company LLC dropped their price target on shares of MongoDB from $415.00 to $270.00 and set a “buy” rating on the stock in a research note on Thursday, March 6th. Seven research analysts have rated the stock with a hold rating, twenty-four have issued a buy rating and one has issued a strong buy rating to the stock. According to MarketBeat.com, the company currently has an average rating of “Moderate Buy” and a consensus price target of $312.84.

Get Our Latest Report on MDB

Insider Activity at MongoDB

In other MongoDB news, insider Cedric Pech sold 1,690 shares of MongoDB stock in a transaction that occurred on Wednesday, April 2nd. The stock was sold at an average price of $173.26, for a total transaction of $292,809.40. Following the transaction, the insider now owns 57,634 shares of the company’s stock, valued at $9,985,666.84. The trade was a 2.85 % decrease in their ownership of the stock. The transaction was disclosed in a filing with the SEC, which is available at this hyperlink. Also, Director Dwight A. Merriman sold 1,045 shares of the stock in a transaction that occurred on Monday, January 13th. The shares were sold at an average price of $242.67, for a total value of $253,590.15. Following the completion of the sale, the director now directly owns 85,652 shares of the company’s stock, valued at $20,785,170.84. The trade was a 1.21 % decrease in their ownership of the stock. The disclosure for this sale can be found here. Insiders sold a total of 58,060 shares of company stock valued at $13,461,875 over the last ninety days. Insiders own 3.60% of the company’s stock.

About MongoDB

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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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Redis, the company behind the eponymous in-memory key-value database, mostly made news in recent months because of its license change, which resulted in the launch of the Valkey project. Now, Redis is hoping to change the conversation a bit with the launch of two new AI-centric products ahead of the launch of Redis 8 on May 1. The first of these is a new caching tool, LangCache, which allows developers to bring large language model (LLM) response caching to its applications. The second is the launch of a new data type, vector sets, for…

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Tessell has announced the launch of its fully managed database service on Google Cloud, making it accessible through the Google Cloud Marketplace.

The Tessell Database-as-a-Service (DBaaS) now accommodates Oracle, PostgreSQL, SQL Server, MySQL, MongoDB, and Milvus, thereby supporting all four major cloud platforms: Azure, AWS, Google Cloud, and Oracle Cloud Infrastructure. This service allows organizations to modernise their transactional applications, database estate, and data architectures within Google Cloud’s infrastructure.

“Tessell’s support for Oracle, PostgreSQL, SQL Server, MySQL, MongoDB, and Milvus on Google Cloud empowers enterprises to capitalize on the newly available opportunity to bring application workloads to Google Cloud GCP,” said Bala Kuchibhotla, Co-Founder and CEO at Tessell. “Tessell has already seen rapid adoption of its fully managed database service on Google Cloud, with customers successfully running mission-critical workloads. Organizations are leveraging the platform to simplify operations, improve scalability, and accelerate cloud adoption without the complexities traditionally associated with database management. As more enterprises recognize the benefits of this streamlined approach, Tessell looks forward to expanding its footprint and supporting even more businesses in their cloud transformation journey.”

The partnership between Oracle and Google Cloud to support Oracle databases on the Google Cloud infrastructure has enhanced opportunities for cloud-based data management innovation. Tessell leverages this collaboration to offer enterprises a managed solution that alleviates the challenges of managing multiple data ecosystems, allowing them to concentrate on innovation.

“Bringing Tessell DBaaS to Google Cloud Marketplace will help customers quickly deploy, manage, and grow the managed database service on Google Cloud’s trusted, global infrastructure,” said Dai Vu, Managing Director, Marketplace & ISV GTM Programs at Google Cloud. “Tessell can now securely scale and support customers on their digital transformation journeys.”

Martti Kontula, Head of OT and Data at Landis+Gyr, expressed satisfaction with Tessell’s approach, saying, “Tessell’s deep database expertise, customer-first approach, and solution-focused mindset made our cloud migration seamless. Their ability to optimize and manage database workloads on Google Cloud ensured a smooth transition. The Tessell platform delivers a powerful, intuitive experience, providing full visibility into database health and performance at a glance. For any enterprise seeking to run databases efficiently in the cloud, Tessell is the ideal choice.”

The service includes several features designed to ease the database management process. Automated maintenance functions such as patching, backup, and recovery reduce downtime and let IT teams focus on strategic projects. High availability and disaster recovery are facilitated through multi-zone availability and cross-region recovery, ensuring business continuity.

Security is a significant emphasis, with flexible backup options, encryption, access controls, and robust recovery mechanisms to meet compliance needs. Tessell also supports enterprise-grade flexibility, integrating the automation benefits of PaaS with the customisation of IaaS.

Tessell provides a unified security and compliance posture, enabling customers to extend their security and compliance services to Google Cloud. By allowing customers to bring their own keys, Tessell ensures continuity in management, security, and compliance operations. A centralised dashboard offers a streamlined view for managing compliance across multi-cloud setups.

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Shares of MongoDB (MDB -0.14%) took a dive last month as the maker of NoSQL database software gave disappointing guidance for 2025 and got caught up in the broad market sell-off on fears around waning consumer confidence and the trade war.

According to data from S&P Global Market Intelligence, the stock finished the month down 34%. As you can see from the chart, the stock tumbled on the earnings report and stayed down from there, slumping toward the end of the month.

MDB data by YCharts

MongoDB sees a slowdown ahead

MongoDB actually beat estimates in its fourth-quarter earnings report, but guidance was the real problem, as the stock tumbled 27% on March 6 after the report came out.

Revenue in the quarter rose 20% to $548.4 million, which easily beat the consensus at $519.8 million. Adjusted earnings per share rose from $0.86 to $1.28, which was well ahead of the consensus at $0.67.

Its cloud-based product MongoDB Atlas continued to deliver strong revenue growth, up 24%, and made up 71% of revenue in the quarter, as management noted better-than-expected consumption of Atlas.

However, investors were disappointed with its guidance for 2025, as the company called for revenue of $2.24 billion-$2.28 billion, up just 12.4% at the midpoint, a notable slowdown from 19% growth in 2024. That was also below the analyst consensus at $2.32 billion. On the bottom line, the company sees adjusted earnings per share of $2.44-$2.62, which was down from $3.33 in 2024 and worse than the consensus at $3.39.

MongoDB also announced a $200 million stock buyback initiative to offset the dilutive impact of its acquisition of Voyage AI, a creator of embedding and reranking models for AI applications.

Following the report, several Wall Street analysts downgraded the stock or lowered their price targets, noting the disappointing forecast for the current year.

Image source: Getty Images.

What’s next for MongoDB?

Management said the slowdown in growth in fiscal 2026 — this year — is related to a more limited cohort eligible for multiyear deals than in the last two years. It also sees margins headwinds due to the loss of $50 million in multiyear revenue and due to investments in R&D.

Those headwinds could lift in fiscal 2027, but the sell-off on the news is understandable. MongoDB is still unprofitable on a generally accepted accounting principles (GAAP) basis, and its forward P/E is roughly 60, based on guidance for adjusted earnings.

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MongoDB, Inc. (NASDAQ:MDB – Get Free Report)’s stock price reached a new 52-week low during trading on Monday after an insider sold shares in the company. The company traded as low as $140.78 and last traded at $141.13, with a volume of 442325 shares changing hands. The stock had previously closed at $154.39.

Specifically, CFO Srdjan Tanjga sold 525 shares of the firm’s stock in a transaction dated Wednesday, April 2nd. The stock was sold at an average price of $173.26, for a total transaction of $90,961.50. Following the transaction, the chief financial officer now directly owns 6,406 shares in the company, valued at $1,109,903.56. This trade represents a 7.57 % decrease in their ownership of the stock. The transaction was disclosed in a filing with the Securities & Exchange Commission, which is available at the SEC website. Also, insider Cedric Pech sold 1,690 shares of the company’s stock in a transaction dated Wednesday, April 2nd. The stock was sold at an average price of $173.26, for a total value of $292,809.40. Following the sale, the insider now owns 57,634 shares of the company’s stock, valued at approximately $9,985,666.84. This trade represents a 2.85 % decrease in their position. The disclosure for this sale can be found here. In related news, CAO Thomas Bull sold 301 shares of the firm’s stock in a transaction that occurred on Wednesday, April 2nd. The stock was sold at an average price of $173.25, for a total value of $52,148.25. Following the transaction, the chief accounting officer now directly owns 14,598 shares of the company’s stock, valued at approximately $2,529,103.50. This trade represents a 2.02 % decrease in their position. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is accessible through this link.

Analyst Upgrades and Downgrades

A number of equities analysts have recently commented on MDB shares. China Renaissance assumed coverage on shares of MongoDB in a research report on Tuesday, January 21st. They issued a “buy” rating and a $351.00 price target on the stock. Wells Fargo & Company lowered shares of MongoDB from an “overweight” rating to an “equal weight” rating and cut their target price for the stock from $365.00 to $225.00 in a research report on Thursday, March 6th. The Goldman Sachs Group lowered their price target on MongoDB from $390.00 to $335.00 and set a “buy” rating on the stock in a research report on Thursday, March 6th. Bank of America decreased their target price on MongoDB from $420.00 to $286.00 and set a “buy” rating for the company in a research note on Thursday, March 6th. Finally, Daiwa Capital Markets started coverage on MongoDB in a research report on Tuesday, April 1st. They issued an “outperform” rating and a $202.00 price target on the stock. Seven investment analysts have rated the stock with a hold rating, twenty-four have given a buy rating and one has issued a strong buy rating to the company. According to MarketBeat, the stock presently has an average rating of “Moderate Buy” and a consensus target price of $312.84.

Read Our Latest Stock Analysis on MDB

MongoDB Stock Down 4.5 %

The business’s fifty day moving average is $234.34 and its two-hundred day moving average is $260.82. The firm has a market cap of $11.97 billion, a price-to-earnings ratio of -53.79 and a beta of 1.49.

MongoDB (NASDAQ:MDB – Get Free Report) last posted its quarterly earnings results on Wednesday, March 5th. The company reported $0.19 earnings per share (EPS) for the quarter, missing the consensus estimate of $0.64 by ($0.45). The firm had revenue of $548.40 million during the quarter, compared to the consensus estimate of $519.65 million. MongoDB had a negative net margin of 10.46% and a negative return on equity of 12.22%. During the same quarter in the prior year, the business earned $0.86 EPS. Research analysts forecast that MongoDB, Inc. will post -1.78 EPS for the current year.

Hedge Funds Weigh In On MongoDB

Hedge funds and other institutional investors have recently bought and sold shares of the business. Vanguard Group Inc. increased its holdings in MongoDB by 0.3% in the fourth quarter. Vanguard Group Inc. now owns 7,328,745 shares of the company’s stock valued at $1,706,205,000 after purchasing an additional 23,942 shares during the last quarter. Franklin Resources Inc. increased its stake in shares of MongoDB by 9.7% in the 4th quarter. Franklin Resources Inc. now owns 2,054,888 shares of the company’s stock valued at $478,398,000 after acquiring an additional 181,962 shares during the last quarter. Geode Capital Management LLC raised its position in shares of MongoDB by 1.8% in the 4th quarter. Geode Capital Management LLC now owns 1,252,142 shares of the company’s stock valued at $290,987,000 after acquiring an additional 22,106 shares during the period. First Trust Advisors LP boosted its position in shares of MongoDB by 12.6% during the fourth quarter. First Trust Advisors LP now owns 854,906 shares of the company’s stock worth $199,031,000 after purchasing an additional 95,893 shares in the last quarter. Finally, Norges Bank bought a new stake in shares of MongoDB in the 4th quarter valued at $189,584,000. 89.29% of the stock is owned by institutional investors.

MongoDB Company Profile

(Get Free Report)

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

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MongoDB, the leading database for modern applications, today announced that Sachin Chawla will be taking on a new leadership role to drive growth and customer value across both the Indian and ASEAN markets.

With more than 20 years of experience in sales, including leadership roles at Amazon Web Services and BMC Software, Sachin joined MongoDB in February 2022 to lead the India business. Since then he has helped fuel exceptional growth in the region including with customers such as Tata Digital, Canara HSBC, Zepto, Zomato and Intellect AI. As part of his new role, Sachin will relocate to Singapore.

MongoDB India experienced 70% headcount growth over the past three years, and the company launched a number of initiatives including a drive to deliver much needed data and AI skills to more than 500,000 students in India.

The world’s leading modern document database provider, MongoDB already has a strong presence across ASEAN. In addition to its large developer community in the region, MongoDB’s impressive list of ASEAN customers includes Grab, Dkatalis, M-DAQ, and Aktivo Labs.

Now, as organisations across APAC increasingly seek to integrate AI into their businesses, MongoDB stands out for its ability to support diverse workloads, simplify data management, and to accelerate application modernisation. MongoDB has evolved into a fully integrated, AI-ready data platform, ensuring organisations can adapt to what’s next.

“ASEAN and India each boast massive developer populations and dynamic business environments, and they’re perfectly poised for growth in the AI era. Following the massive impact he had in India, I know Sachin Chawla is the ideal leader to build on MongoDB’s strong foundation in ASEAN, and to deliver transformative results for our customers, employees and partners,” said Simon Eid, Senior Vice President of APAC, MongoDB.

MongoDB recently announced that it had expanded the availability of MongoDB Atlas in Southeast Asia. The multi-cloud data platform is now available on AWS regions in Malaysia and Thailand. This expansion comes on the heels of significant growth in the Southeast Asia region, with headcount growing more than 200% over the last two years.

“I’m incredibly proud of the team we’ve built in India, and I’ve seen firsthand the immense impact MongoDB has made in helping local customers build transformative applications. Now as the AI era heats up, I couldn’t be more energised and excited about the opportunity ahead in ASEAN. These two markets, at this time, with a technology as impactful as MongoDB – it really is an incredible opportunity,” said Sachin Chawla, Area Vice President of ASEAN and India, MongoDB.

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