Month: April 2025

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By Seth Payne, Lead Product Manager at MongoDB
By Daniel Ernst, Search Engineering Lead at MongoDB
By Arjun Gurumurthy, Staff Engineer at MongoDB
By Archana Srinivasan, Sr. Technical Account Manager at AWS
By Rashmikant Vyas (RK), Sr. Technical Account Manager at AWS
By Sergio Ariel de la Campa, Sr. Technical Account Manager at AWS

MongoDB customers were seeking a solution to handle Atlas Search index rebuild in case of a problem with underlying hardware or scaling activities. This blog post discusses how MongoDB has enhanced the indexing capabilities of its Atlas Search service by leveraging Amazon S3. The approach allows MongoDB to reduce the time required to rebuild search indexes, achieving a 14x improvement compared to the previous method.

Background

MongoDB Atlas a comprehensive cloud database platform, was launched on Amazon Web Services (AWS) in 2016. Today’s service update includes global expansion, now available in 34 AWS regions worldwide. With AWS, MongoDB Atlas offers a user-friendly, scalable, and secure cloud platform for deploying, managing, and monitoring MongoDB databases.

One of the key features of MongoDB Atlas is its integrated search and vector search capabilities. This eliminates the need to run a separate search system alongside your database. Initially, MongoDB ran search and database on same hardware, causing contention and scaling limitations. In 2023, MongoDB introduced dedicated search nodes on Amazon EC2 Graviton to address this. The current challenge with MongoDB Atlas search deployments is that they require complete index rebuilding for each search process, causing operational overhead and longer deployment times.

Challenges with legacy approach

MongoDB Atlas (Full-Text) Search launched 5 years ago with Atlas Vector Search being added within the last couple of years. They have become popular among developers because of the native experience within MongoDB and the evolving query capabilities and enhancements. This launch helps MongoDB Atlas users to seamlessly leverage full-text and vector search capabilities within their transactional database systems. MongoDB’s Search capabilities eliminate the need for complex ETL to keep search and transaction data in sync. With this MongoDB now supports complex search workloads with high demands for queries per second (QPS), data ingestion, and low latency. Atlas Search and Vector Search are based on Apache Lucene. The initial architecture collocated the database process (mongod) and search process (mongot) on the same hardware, which had the potential to create workload contention and scaling challenges. While this collocated approach can still be used, MongoDB released dedicated search nodes in 2023. The new architecture allows users to isolate and independently scale their search (or vector search) processes from their database processes. With the dedicated search node approach, the search process (mongot) runs on its own hardware on Amazon EC2 Graviton. The search process communicates the search results to the database process (mongod) via the network. This method eliminates resource conflicts and allows efficient scaling for large-scale search applications handling high query loads, data ingestion, and storage. Leveraging dedicated search nodes is the optimal approach for any team running search and/or vector search processes in production.

When deploying or scaling search infrastructure, MongoDB Atlas clusters rebuild indices for each search process from source MongoDB collection. Creating search indexes this way is predictable and reliable, but it means reading the entire MongoDB source collection, which may take a long time. Therefore, implementing new search infrastructure is slow and burdens the MongoDB Atlas cluster’s read operations.

Solution overview

This blog post details new Atlas Search indexing features that leverage Amazon Simple Storage Service (Amazon S3). Atlas Search in this context refers to both the full-text and vector search capabilities. MongoDB has improved the indexing experience for users with large datasets, using Amazon S3 to enhance index rebuilding during deployments, scaling, and recovery. The system periodically backs up its Amazon S3 search indexes instead of constantly rebuilding them from the MongoDB source. Rebuilding an index on a fresh instance uses the recent Atlas Search file snapshot, rebuilding only since the last capture. This speeds up deployment and scaling while improving search availability, especially for large datasets. These benefits come with no changes to current configuration for the end users.

MongoDB Atlas Search, launched in 2019, and its recent Atlas Vector Search feature provide developers with native search capabilities within MongoDB. This eliminates the need for complex ETL processes to synchronize search and transactional data. The search solutions initially ran on the same hardware as the database process, causing resource contention and scaling limitations. MongoDB addressed these challenges in 2023 by introducing dedicated search nodes on Amazon EC2 Graviton hardware. However, a significant challenge remains: when deploying or scaling search infrastructure, MongoDB Atlas must rebuild indices for each search process by performing a complete read of the source collection. This creates operational burden and extends deployment times.

Architecture overview

The MongoDB Atlas Control Plane comprises services whose functionality includes handling search index management command, node configuration updates, retrieving index definitions, and provisioning mongot and mongod nodes. Atlas Search indexes are created by specifying a mongod collection as the data source, along with an index configuration. Whenever a customer creates or updates a search index, mongots retrieves its definition from MongoDB Atlas. It then builds the index and dynamically updates it to reflect changes in the collection’s documents.

These search indexes are built on Lucene, and comprise a set of immutable files on a disk. MongoDB Atlas Search takes an index snapshot which is essentially a set of the files corresponding to a valid queryable Lucene index. Figure 1 architecture allows for periodic and efficient uploading of search indexes to Amazon S3, and retrieval of this data when an index rebuild is required. If index files remain unchanged between snapshots, they don’t need to be re-uploaded, optimizing storage and transfer processes.

Figure 1: Architecture of Atlas Search using Amazon S3

The design ensures that index uploads and downloads are atomic and resilient to mongot restarts and transient errors. If a download fails, the system gracefully falls back to building the search index from scratch.

The upload index process involves retrieving Amazon S3 credentials, capturing the latest index snapshot, uploading the index in parallel, and storing index metadata in Amazon S3. For downloads, mongots retrieve credentials, query Amazon S3 for index metadata, and download files in parallel if a snapshot is available.

MongoDB uses GetFederationTokens to vend credentials scoped per customer, ensuring that a customer’s data remains inaccessible to others. The testing strategy is rigorous, involving unit, integration, and end-to-end tests. MongoDB even employed LocalStack to create fault-injectable Amazon S3 clients, simulating various exceptions and errors to verify mongot behavior under different conditions.

Amazon S3 plays a crucial role in enhancing the speed and efficiency of Atlas Search’s index rebuilding process. Amazon S3 provides a reliable and scalable storage solution for periodic snapshots of search index states. This enables quick retrieval of the latest index data when rebuilding is necessary. By using this approach, we get the latest index snapshot from Amazon S3, which speeds up rebuilding compared to using the MongoDB source.

Efficient storage management involves the asynchronous deletion of older snapshots once new ones are uploaded. MongoDB leverages Amazon S3 Lifecycle policies to automatically delete orphaned files, ensuring optimal use of storage resources and maintaining a clean, up-to-date snapshot repository.

The design also incorporates other critical safeguards: limiting resource utilization during S3 access preserves core search functionality, and distributed uploads across multiple search hosts prevent system overload.

Benefits

Traditional MongoDB index recreation during scaling or node rebuilds takes hours, posing scalability challenges due to its time-consuming nature. With its new Amazon S3 powered feature, MongoDB Atlas Search now offers customers faster search index rebuild times. MongoDB’s internal tests have shown this time is reduced by 14X with Amazon S3. This new feature is transparent to MongoDB users, requiring no action from customers.

Amazon S3 based decoupling of the indexing layer allows for a scalable and evolving design. Previously, preventing concurrent index uploads required implementing concurrency control mechanisms. However, with the recently announced conditional writes support in S3, this process is simplified – the native S3 feature can automatically prevent concurrent uploads.

Conclusion

MongoDB Atlas has improved its Search capabilities by implementing an innovative Amazon S3-based approach to index rebuilding. The new method delivers performance and efficiency gains. This new method lets us periodically save copies of search indexes to Amazon S3, which speeds up index rebuilding when deploying, scaling, or recovering. MongoDB’s new method of rebuilding indexes, using Amazon S3’s latest Atlas Search file-level snapshot and starting from the last capture point, is 14 times faster than the old method.

MongoDB customers using Atlas Search dedicated nodes experience faster scaling and node rebuilding, decreasing index reconstruction time from hours to minutes. The Amazon S3 based approach serves as a foundation for future features.

MongoDB – AWS Partner Spotlight

MongoDB is an AWS Competency Partner. Their modern, general purpose database platform, is designed to unleash the power of software and data for developers and the applications they build.

To learn more, refer to these getting started guides for Atlas Search or Atlas Vector Search for step-by-step instructions.

Contact MongoDB | Partner Overview | AWS Marketplace

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

Posted on mongodb google news. Visit mongodb google news

Shares of MongoDB Inc (MDB, Financial) surged 2.87% in mid-day trading on Apr 2. The stock reached an intraday high of $182.00, before settling at $181.68, up from its previous close of $176.61. This places MDB 53.08% below its 52-week high of $387.19 and 6.45% above its 52-week low of $170.66. Trading volume was 1,267,571 shares, 55.0% of the average daily volume of 2,302,646.

Wall Street Analysts Forecast

Based on the one-year price targets offered by 34 analysts, the average target price for MongoDB Inc (MDB, Financial) is $304.56 with a high estimate of $520.00 and a low estimate of $180.00. The average target implies an upside of 67.64% from the current price of $181.68. More detailed estimate data can be found on the MongoDB Inc (MDB) Forecast page.

Based on the consensus recommendation from 38 brokerage firms, MongoDB Inc’s (MDB, Financial) average brokerage recommendation is currently 2.0, indicating “Outperform” status. The rating scale ranges from 1 to 5, where 1 signifies Strong Buy, and 5 denotes Sell.

Based on GuruFocus estimates, the estimated GF Value for MongoDB Inc (MDB, Financial) in one year is $432.97, suggesting a upside of 138.32% from the current price of $181.675. GF Value is GuruFocus’ estimate of the fair value that the stock should be traded at. It is calculated based on the historical multiples the stock has traded at previously, as well as past business growth and the future estimates of the business’ performance. More detailed data can be found on the MongoDB Inc (MDB) Summary page.

This article, generated by GuruFocus, is designed to provide general insights and is not tailored financial advice. Our commentary is rooted in historical data and analyst projections, utilizing an impartial methodology, and is not intended to serve as specific investment guidance. It does not formulate a recommendation to purchase or divest any stock and does not consider individual investment objectives or financial circumstances. Our objective is to deliver long-term, fundamental data-driven analysis. Be aware that our analysis might not incorporate the most recent, price-sensitive company announcements or qualitative information. GuruFocus holds no position in the stocks mentioned herein.

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

Posted on mongodb google news. Visit mongodb google news

MongoDB (NASDAQ:MDB – Get Free Report) had its price objective dropped by analysts at Citigroup from $430.00 to $330.00 in a research report issued to clients and investors on Tuesday,Briefing.com Automated Import reports. The firm presently has a “buy” rating on the stock. Citigroup’s price objective would suggest a potential upside of 88.14% from the stock’s previous close.

A number of other brokerages also recently commented on MDB. Royal Bank of Canada lowered their target price on shares of MongoDB from $400.00 to $320.00 and set an “outperform” rating for the company in a research note on Thursday, March 6th. JMP Securities reiterated a “market outperform” rating and issued a $380.00 price objective on shares of MongoDB in a report on Wednesday, December 11th. Mizuho boosted their target price on MongoDB from $275.00 to $320.00 and gave the stock a “neutral” rating in a research note on Tuesday, December 10th. Bank of America dropped their price target on shares of MongoDB from $420.00 to $286.00 and set a “buy” rating on the stock in a research note on Thursday, March 6th. Finally, Cantor Fitzgerald began coverage on shares of MongoDB in a research report on Wednesday, March 5th. They set an “overweight” rating and a $344.00 price objective for the company. Seven research analysts have rated the stock with a hold rating and twenty-three have issued a buy rating to the stock. Based on data from MarketBeat.com, MongoDB presently has an average rating of “Moderate Buy” and a consensus target price of $316.53.

Get Our Latest Analysis on MongoDB

MongoDB Price Performance

MDB stock opened at $175.40 on Tuesday. The business’s 50-day moving average is $244.01 and its two-hundred day moving average is $265.11. The firm has a market capitalization of $14.24 billion, a P/E ratio of -64.01 and a beta of 1.30. MongoDB has a 1 year low of $170.85 and a 1 year high of $387.19.

MongoDB (NASDAQ:MDB – Get Free Report) last issued its earnings results on Wednesday, March 5th. The company reported $0.19 earnings per share for the quarter, missing the consensus estimate 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 firm had revenue of $548.40 million during the quarter, compared to analyst estimates of $519.65 million. During the same period last year, the business earned $0.86 EPS. On average, analysts expect that MongoDB will post -1.78 EPS for the current year.

Insider Buying and Selling at MongoDB

In other news, Director Dwight A. Merriman sold 3,000 shares of the business’s stock in a transaction that occurred on Monday, March 3rd. The stock was sold at an average price of $270.63, for a total transaction of $811,890.00. Following the sale, the director now owns 1,109,006 shares of the company’s stock, valued at $300,130,293.78. The trade was a 0.27 % decrease in their position. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is available through this hyperlink. Also, CEO Dev Ittycheria sold 8,335 shares of the company’s stock in a transaction that occurred on Tuesday, January 28th. The stock was sold at an average price of $279.99, for a total value of $2,333,716.65. Following the completion of the sale, the chief executive officer now owns 217,294 shares in the company, valued at approximately $60,840,147.06. This represents a 3.69 % decrease in their position. The disclosure for this sale can be found here. Over the last quarter, insiders sold 43,139 shares of company stock valued at $11,328,869. 3.60% of the stock is owned by company insiders.

Institutional Investors Weigh In On MongoDB

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

Headlands Technologies LLC purchased a new position in MongoDB, Inc. (NASDAQ:MDB – Free Report) during the 4th quarter, according to its most recent filing with the Securities and Exchange Commission (SEC). The firm purchased 9,785 shares of the company’s stock, valued at approximately $2,278,000.

Other hedge funds and other institutional investors have also recently modified their holdings of the company. Norges Bank purchased a new stake in MongoDB in the 4th quarter worth approximately $189,584,000. Raymond James Financial Inc. bought a new position in shares of MongoDB in the fourth quarter worth approximately $90,478,000. Amundi increased its stake in shares of MongoDB by 86.2% in the fourth quarter. Amundi now owns 693,740 shares of the company’s stock worth $172,519,000 after purchasing an additional 321,186 shares in the last quarter. Assenagon Asset Management S.A. raised its position in shares of MongoDB by 11,057.0% during the 4th quarter. Assenagon Asset Management S.A. now owns 296,889 shares of the company’s stock valued at $69,119,000 after purchasing an additional 294,228 shares during the period. Finally, Franklin Resources Inc. lifted its stake in shares of MongoDB by 9.7% during the 4th quarter. Franklin Resources Inc. now owns 2,054,888 shares of the company’s stock valued at $478,398,000 after buying an additional 181,962 shares in the last quarter. 89.29% of the stock is currently owned by hedge funds and other institutional investors.

Analyst Upgrades and Downgrades

MDB has been the topic of a number of recent analyst reports. Cantor Fitzgerald started coverage on MongoDB in a report on Wednesday, March 5th. They set an “overweight” rating and a $344.00 price target on the stock. Citigroup reissued a “buy” rating on shares of MongoDB in a research note on Thursday, March 6th. DA Davidson upped their price target on MongoDB from $340.00 to $405.00 and gave the stock a “buy” rating in a research note on Tuesday, December 10th. The Goldman Sachs Group reduced their price objective on shares of MongoDB from $390.00 to $335.00 and set a “buy” rating on the stock in a research report on Thursday, March 6th. Finally, China Renaissance began coverage on shares of MongoDB in a research report on Tuesday, January 21st. They set a “buy” rating and a $351.00 target price for the company. Seven investment analysts have rated the stock with a hold rating and twenty-three have given a buy rating to the company. According to data from MarketBeat, the company presently has a consensus rating of “Moderate Buy” and an average target price of $319.87.

Get Our Latest Stock Report on MongoDB

MongoDB Stock Performance

NASDAQ MDB opened at $175.40 on Tuesday. MongoDB, Inc. has a one year low of $170.85 and a one year high of $387.19. The company has a market capitalization of $14.24 billion, a price-to-earnings ratio of -64.01 and a beta of 1.30. The company’s 50-day simple moving average is $244.01 and its two-hundred day simple moving average is $265.11.

MongoDB (NASDAQ:MDB – Get Free Report) last issued its quarterly earnings data on Wednesday, March 5th. The company reported $0.19 EPS for the quarter, missing analysts’ consensus estimates of $0.64 by ($0.45). The business had revenue of $548.40 million during the quarter, compared to the consensus estimate 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 last year, the firm earned $0.86 EPS. Equities analysts expect that MongoDB, Inc. will post -1.78 earnings per share for the current fiscal year.

Insider Activity at MongoDB

In other MongoDB news, CAO Thomas Bull sold 169 shares of the firm’s stock in a transaction that occurred on Thursday, January 2nd. The shares were sold at an average price of $234.09, for a total value of $39,561.21. Following the completion of the sale, the chief accounting officer now directly owns 14,899 shares in the company, valued at $3,487,706.91. This trade represents a 1.12 % decrease in their ownership of the stock. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is available at the SEC website. Also, insider Cedric Pech sold 287 shares of the company’s stock in a transaction on Thursday, January 2nd. The stock was sold at an average price of $234.09, for a total value of $67,183.83. Following the transaction, the insider now directly owns 24,390 shares in the company, valued at approximately $5,709,455.10. The trade was a 1.16 % decrease in their ownership of the stock. The disclosure for this sale can be found here. Insiders have sold a total of 43,139 shares of company stock valued at $11,328,869 over the last quarter. 3.60% of the stock is owned by corporate insiders.

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.

See Also

This instant news alert was generated by narrative science technology and financial data from MarketBeat in order to provide readers with the fastest and most accurate reporting. This story was reviewed by MarketBeat’s editorial team prior to publication. Please send any questions or comments about this story to contact@marketbeat.com.

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.

View The Five Stocks Here

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Transcript

Di Cesare: I’m going to talk about what we did at DKB about introducing a product mindset in the platform team. I will explain as well what we actually mean by platform team, and what’s the point of introducing a product mindset, and our path to there, what we did, what we still have left, and what we reached.

DKB is a German online bank, which was founded in 1990, in Berlin. We are the special bank in Germany. We are not in Frankfurt. I’m a senior platform engineer. I’m part of the experience team of the platform, that is the team who is doing basically customer relations, so talking with customers, ensuring that user needs are taken into account in the platform, helping them onboard. I’m a member of the CNCF Platform Working Group, which is a good place to have some good information and work on the platform topic. I’m interested in linguistics and languages, so I talk often about communication and understanding each other, which is an important topic for platforms as well.

What is a Platform?

Then, when I talk about platform, so platform is quite a very broad term. The meaning I use here is the meaning that was used in “Team Topologies”, which is a very important book to read, which was released five years ago about that topic. This book describes different kinds of teams, and defines a platform team as the team that provides a compelling product to accelerate delivery by stream-aligned teams. Stream-aligned teams, from that book, means teams who are delivering value directly. For us, for example, people working with internet banking, working with private customers, and so on. Basically, a platform team is about putting know-how together instead of spreading it out through different product teams.

Where We Started

This is a bit of also the scope of the platform. Depending on who you talk to, you will have very different scopes about the platform. What we do, we actually started from teams which were here at the low-level infrastructure. A big part of our team, they are people who are system administrators, who are working with networking, and so on. We built something a bit larger, which includes platform orchestration, which includes what is called here the DevOps platform. It’s basically CI/CD, the deployment part. We don’t include high-level things like API gateway. There is another team which has a data platform, which builds on ours, but they are on the outside in the organization. We are going into internal development platforms, so mostly APIs, CLIs at the moment. Maybe we will go into providing integrated development environments at some point. For the moment, the users are quite free on that side.

The core of our platform started from the container platform, running Kubernetes on EKS, on AWS, and using Crossplane, which is an open-source infrastructure as code product. It’s a bit comparable to Terraform, but it keeps track of the state, live, so that if you modify something in the infrastructure, it will correct it more or less straight away or within minutes. The state we are, it was an engineering-based team. It focused a lot on the engineering topic. It means on the technical side, it works quite well, but we had some hurdles with the communication. We still communicate a lot as infrastructure engineers. It’s sometimes a bit difficult to get into it for product teams who don’t have an infrastructure background.

Challenges, and Goals

First of all, the challenges we encountered. A lot of the challenges for engineers are these people here. It’s not everything to have a platform that works. We had people who didn’t know at all about the platform, and who were actually replicating things that we can already provide. We have people who didn’t understand the scope of the platform, so they were using it for something different than what it was planned for. Who think that the platform, for example, is the operations of the applications, rather than a team that just provides a product, but doesn’t operate everything that the application is doing. Of course, so then the problem was, how can we ensure that we are not only solving a technical problem, but also solving a problem that our users actually have?

The goals we had were, how can we show the value of the platform, so what it brings, and important to show. It’s not only about providing the value, but it’s also about making it visible, which is a typical difficulty for everything that’s operations related, that you realize the value of an operations team at the time you remove it and nothing works anymore. It was important for us to try to show that value before it breaks, so to make it clear for the board and so on, why they are investing in that team and what it brings. We wanted also to show the state of the platform. We would always have people who come and say, when is the platform complete? It’s not that simple, unfortunately. We realized that we didn’t have a very good high-level way of showing where we are.

We could talk about features, about service mesh, is it implemented, and so on, but when you talk to the board, they have no idea what you’re talking about. You need to be more at their level to explain what it brings at a higher level for the company. Also, how can we share information effectively about the platform? To be sure that everybody understands things the same way, understands what the platform is supposed to do, understands the user documentation, and so on.

Roadmap (Platform Journey)

I’m going to start with talking about product mindset, and what’s the point of product mindset? About what we did to try to define the platform and to show the platform maturity. That’s basically the state of the platform. Then I will talk about communication and information. What are our experiences, and what we would say our best practices are.

1. Product Mindset

Regarding product mindset, so the main point of a product mindset, it means that you’re focusing on the user value. For a platform, the user is not always the same as the customer. Customer, you would typically say that’s the person that pays. It would be, in our case, someone like the CIO, the board. The users, in the case of a platform, they are not like, for a consumer product, exactly the same people who pay. You have to take into account both at the same time, to see that the people who pay have value, but it also brings some other value for the users in another way. It’s a bit of a different exercise. What is important as well is that a team that’s working with a product mindset is asked to fix problems and decides on the solution. It’s not a support team which would get a ticket with a full implementation and you have to do it that way.

Part of the work of the platform team is to think broad, not only solve the current problem you see, but also think about how can we solve this in the long term by maybe solving it so that it cannot happen again with other people. Or that there is information about what to do in that kind of situation in the future. A product team is also accountable for the outcome and not for the output. The important thing is not how much you do, the important thing is what is the outcome, what is the user value at the end. It’s not only about occupying people the whole time, but it’s also looking at, what do you want to do in the long run, and are you going in that direction? For background information, there is these sources. This book by Marty Cagan, especially “Transformed” is very useful, it’s the best starting point. It’s more in the concept of company transformation. “Team Topologies”, already mentioned, for the organization. The Melissa Perri book, “Escaping the Build Trap” is more specific in the development product team context.

Then, some tips we noticed. The way we’re doing at the moment is, you need both a clear vision and a clear strategy. Vision is the long-term part, so it’s for something really high level, something like, we are making cloud simpler for developers, so that it’s very clear for everybody what the goal of the team is. Strategy are topics that are a bit more short term or middle term, like one year, something like that, where you would say, for example, what are your exact goals for a specific year that we want to improve that particular part of the platform, the information, we want to introduce that tool that brings that, and so on. This is useful in the team so that people are working in the right direction, and also on the outside so that the users can understand what the platform team wants to do, and can also give some feedback. We actually have a very big problem and you’re not addressing it in your strategy, so, can we talk about it? I found that as an engineer and for many of the engineers, that part is difficult about discovering problems.

Engineers have a tendency to go into the solution straight away. Part of the work is looking around, discovering issues, talking with developers, working with developers so that you can actually see which problems they really have. In our engineering mindset, in a way, it doesn’t feel like real work. For many engineers, it’s difficult to do this because they would see real work is working on tickets, is implementing something, is doing architecture, designing, and so on, but not looking at people’s problems. This is actually quite an important part of the work. You need to strike a balance between easy wins and long-term value. You need some easy wins, especially in the beginning of the platform because you need to show value. If you can fix a problem and show that when we were involved with this issue, we were able to bring that and people worked faster, or we have better visibility and things like that, it’s good for you in the long term. You cannot only focus on that, you need to look as well, in the long term, how can we improve things? You need a balance of that.

In the past, when I was working as a consultant, I’ve seen issues for example of teams who didn’t look for easy wins and then they built a really good background in a year or two. Then, at some point, the board said, nothing happens here, and they canceled everything. You always have to keep in mind that you should stay visible and show something. About the discovery, I think shadowing is really a very good way to find things. Shadowing means that you work with one of the users, developers, people from finance, whatever, for an hour or two, and they show you how they work without you necessarily commenting.

Every time I do this, I always find, this is something we can improve. Or they are using it in a completely different way than what we thought, so maybe we need to improve the documentation. Or maybe the way they’re using is actually even better than what we had in mind, so we can take this over. Then, when doing shadowing, you realize that what helps the users is not always what they ask. Very often, as a platform specialist, you can bring a way of working, say, “If you work that way, if we introduce that way, then it will be a big help for the developers”.

Some challenges we run into. There is a lot of scrum in the company. I found that in scrum, the product owner role is normally doing both the product part and backlog management, basically, so the delivery, ensuring that things get done. Very often, the second part takes the priority at some point, because you have a big backlog, and the product owner ends up ensuring that things get complete, and starts not always considering new topics, because we have such a big backlog, and so on. I think it’s a bit in the way scrum works, that all these roles are in the same person, so maybe in some cases, it’s good to delegate a bit, to have someone who can help the product owner with product topics, for example. Who isn’t stuck in planning meetings the whole time, and so on. There is something I would call team encapsulation, as well, in scrum.

Scrum, I think, was designed to prevent engineers from getting disturbed by other teams the whole time, and need to work on fires the whole time. Sometimes it goes into the other extreme, that the team is completely insulated from the outside world. This is also not good. It must be clear. Communication with the outside is good, as long as you’re not only doing communication. It’s a good thing for engineers to be involved with customers once in a while, so that they can see what is the outcome they are bringing. Very often, we have users try to force the implementation of a requirement. It’s from product teams, but it’s very often from support teams, like security, for example, you must use this tool.

Then, very often, it’s important to go back, to understand, what are we actually trying to fix? Because with the example of security, typically, when we introduce a security tool, it will have effects on the processes. You might get more approvals. Things might become more complex, and you have to find a balance. This is also part of the work of the platform team, of finding the right balance between everybody.

2. Defining the Platform

This is what we use to define the platform. Typically, the part at the bottom would be what many people focus on, for this kind of platform, so the infrastructure, the hard stuff. There is more than this. There is, how do you talk to the users? It’s very important to define these kinds of processes. How do you interface with the users, both technically, so APIs and so on. Also, communication, you talk to the teams, what do you do when you have incidents, when you have requests? Which kind of work you’re doing.

For example, a platform team can do technical consulting. You can have database specialists who could go and work for a team for a few days to improve their processes. This is not something that’s in the infrastructure. It’s not automated. It’s very useful as well, because then you don’t have to have database specialists in every team. In what we call the common layer, this is more guidelines and policies. How do you work with the platform? For us, we’re a bank, so compliance is important. We try to summarize, where do we get information about compliance? What are the different sources? How are you supposed to work? Which topics do you have to look at when you look at security?

Then you can describe, these parts, they’re automated because we have a policy engine, for example, and these topics, if your workload runs in the platform and doesn’t get kicked out, then you’re good. There will be other topics which are more where the application will have to be involved, which cannot be solved by the platform team. We find that very often there’s a lot of value in just the information that the product teams can know, “So this is the scope of security, and we’re covering this, this, but not that, and we need help there”.

What we are trying to do in practice, so in product management, they usually make a distinction between problem space and solution space. Problem space is which problems you have, and solution space is how you fix them. I found that very often when you use tools like Jira, you are always focusing a lot on the solution space. There are objects like user stories, but in practice in Jira, they’re not really used that way. What is useful is to have a repository of what are the kind of use cases that are covered by the platform or could be covered by the platform, because it’s also good information to know, this is something we can work on. Maybe we don’t have time right now, but at least users know that this is something that the platform is not doing for the moment.

If it’s important for me and I need it critically, then I can do it on my own, and then maybe we can integrate it back later. It’s not only about showing what the platform covers right now, but also, what are different possible problems and how can they be solved. There might be different solutions as well. The only solution is not only about automating everything, but it can be about first giving pointers. Here are good pointers about that topic, in the internet. Here is how you manage persistence, or something like that. Then when you see it’s a bigger problem, people need more support, you can continue setting up best practices, and then also the technical size, so automating things. It’s not only about automation. Status and the responsibility as well, who to talk to when you have something about that topic.

3. Clarifying Platform Maturity

This is the platform maturity model from the CNCF, from the Cloud Native Computing Foundation. This describes the state of the platform, according to different topics. We found this is a very good tool to manage up, to talk with people at a higher level because then you can be much more generic without going into the details. It has five categories, so basically how you do the funding, how the team is funded.

How do you work with adoption? What are the interfaces between the team and the outside world, how the team operates? How do you measure things? It’s much easier to talk with people who have budgets, for example, at that kind of level to discuss that, “We want to go in the interfaces from provisional to operational, and it means we would introduce this and that. It will give that kind of value, and we think it would cost that much”. It’s much easier to discuss that way than having the board coming and say, when is your platform finished in the end? This is a bit, what we did, what is our situation, and what we want to improve in the future. It’s not always that you have to be. In the end, it’s a cost issue as well. You have to look at, is the cost in one category being the very best, is it really worth it for us or not?

4. Information – Communication and Documentation

Then, information. I really like this. Matthew Skelton is one of the guys who wrote “Team Topologies”. I think there is a lot of truth in this. We think a lot about implementation, but there is a lot in platform engineering which is about know-how, which is about keeping the know-how on what you do. As a consultant, I’ve seen projects in the past, especially some which were very successful technically. Something was automated and it works flawlessly. The team gets dissolved. It works perfect for 10 years. Then when you need to change something, you realize that nobody actually knows how it works and what it really does. This is not a good situation. Regarding information, so it’s important to consider it as a feature of the platform the same way as technical features. To make it as effective as possible, but don’t constrain too much. You have to look at what are the real use cases as well.

There are use cases for asking a quick question in chat, but you don’t necessarily need the best authoritative answer. It could be something like, is this supported in the platform? No, it’s not supported. Good, we know it. Communication is not only about ITSM tickets and incidents and things that get tracked perfectly. There are also topics about technical consulting. We find this is important in some topics as well. About this, I found this book very interesting, “The Async-First Playbook”. It’s a book about how to communicate in remote environments. I find it very useful even when you’re not remote because it focuses a lot on how to make information visible. I think especially when you work with Scrum, which is quite meeting heavy, the information tends to be in the meetings. If you’re not in the meetings, then you don’t have the information. This is not good. It’s important to think about, what is decided, how do we know that it’s decided, how can you look at it in the long term as well.

Then, documentation is an important topic as well. In our situation, we had it a bit more difficult because we basically are a merger of different teams doing different things. Of course, they all had their different documentation. It’s very difficult to put everything together. Also, because people who write documentation don’t all write documentation in the same way. Developer likes having things like merge requests for review, for example. In general, for us, people like architects, they would prefer to use a tool like Confluence where you have direct feedback. When you do a merge request, if you use something like GitDocs and you do merge request, you only see the real result when you deploy. That’s the difficulty of working that way with documentation. You have to find a way that works for everybody. We gave up in the short term to try to standardize everything, to say, there is this documentation platform, you have to use it, because some people don’t use it.

Then you have shadow documentation. This is not good because then you might have people describing how to use the platform in their own way. Then when the platform changes, if you don’t know that this documentation exists, it doesn’t get updated and it’s wrong. It’s bad for you even if it’s not the official documentation. It’s a good thing to always try to keep the overview, to be as inclusive as possible so that you know also if other product teams have their own documentation, what they are talking about, and so on. This is not only about user documentation. We try to make a map of what it looks like. We have basically documentation that’s more thought for the outside world, which will describe what is the architecture of the platform, what does it do, some quick introductions, and so on, for new users, for people like product managers in other teams so that they understand what it does. We have some more conceptual architecture documentation, which explains how things are built, what are the capabilities, how does it work.

Some which are more low level, so we have internal documentation for things like operations, and user documentation, how to use it or how to troubleshoot things. This is quite broad. Technical documentation needs to be in sync with the user documentation, which is very often a problem. For example, during onboarding, we encourage the people who are onboarding to tell straight away if something doesn’t work. It actually corresponds to this, the psychological safety. Tell when something doesn’t work. It’s not because you did something wrong or you’re incompetent or something like that, but it’s very probably because something has not been updated, so we need to know about it.

Especially for communication with the outside, some people, especially the most competent ones technically, have a tendency to think that everything is simple and obvious. You shouldn’t communicate too much to the outside because, basically, when you do that, you’re telling users you are not the right customer for us. It’s not a good situation because when you don’t have customers anymore, then you have a problem. It’s important, even if sometimes you have the impression that some people don’t understand very fast, that not everybody has the same background, and not to stop people from commenting because this is the way you learn how you can help them.

The Future – Next Steps

What does the future look like for us? We want to continue improving how all stakeholders participate, so even people who are not direct users. We had good experience working with people like finance, for example, which is not always the first kind of user you would think about in a platform, but for them, there’s a lot of value in being better able to track costs, to map costs to a specific product team. We actually had a lot of good collaboration that way. It’s always good when the finance team is happy. To work more, so to encourage especially the engineers to talk a bit more with the users and to consider things from a user point of view. We’re working a lot with Kubernetes. I think with Kubernetes, it’s very interesting technically. I find the same as well. You can spend the whole time looking at new technologies, and at some point, you have to look as well at, what is exactly the value behind for the users?

Questions and Answers

Participant 1: What’s your take, and you can pick whatever is easier in terms of amount of teams or budgeting costs for the engineering teams or whatever, on the balance between the platform and the stream-aligned teams or any other of the complicated subsystems. Or, how does it work in your case? How much capacity do you have working on this platform team? In terms of FTE, or how many people you have working on the platform team, what’s the balance between the platform team and the stream-aligned teams?

Di Cesare: All together, we have about 60 people. This includes some things which are not really working as a product at the moment, like part of the network team, identity and access management. As you saw in the first diagram, it’s quite broad. All together, it’s about 60 people, including administrative people as well, so scrum masters, product owners, and so on.

Participant 1: That’s 60 for the platform?

Di Cesare: Developers, we are in the hundreds. I wouldn’t be sure about the exact number, but it should be in 300, 400 in that level, roughly, yes.

Participant 2: How do these practices that you’ve showed here differ for the enterprise size? Imagine you have a 20-people company versus 2,000 or 20,000. Do you think they’re consistently applicable across all those three sizes?

Di Cesare: I think the principles, yes, but some parts will become more important depending on the size. All the parts with communication, they’re always easier when you’re small, because you can see the people next door. We see, for example, the communication inside the platform. Things like vision and strategy, they are important to be sure that the different sub-teams go in the same direction, and don’t start to work with different priorities, for example. I think on the enterprise side, it’s really important to focus on that as well. Probably to have decisions and communication made in a way that it can be tracked better when you’re on the enterprise level.

Participant 3: In your opinion, what are the most critical roles that have helped you during this journey to adopting a product mindset to the platform team?

Di Cesare: I think the key role in the end, is you have to have the product management mindset at the top, on the leadership side. If you don’t have this, I think you need to reach that at some point because I think people who decide must work in that way. If only the engineers work in that way, it’s not going to work. If you are in a situation where only engineers work with a product mindset, people on the leadership need to be convinced. We fortunately are in the situation that people in the leadership are convinced of this because we have also many end user products and they work more like this.

Participant 4: You mentioned about making small investments and finding small wins against making big investments. Do you have any advice for people who are already making big investments but are having challenges on how to dial back on that and make some progress?

Di Cesare: The main thing is you have to consider both because some teams who are more short-term focused will look only at the easy wins. Then you will get a lot of buy-in in the beginning but then you’re going to build a lot of technical debt as well, and things won’t be effective in the long term. I’ve seen also in other positions, the opposite, where you have companies which are architecture heavy, they have a very clean architecture, but they are not focusing very much on having something that works and that people can use even if it’s not perfect. The main focus is on the long term but you have to be sure that you always look at short-term things on the side as well, so that there is always some visible progress.

Participant 5: Do you have any tips on how to avoid shallow documentation?

Di Cesare: You need to talk with the users, and look at how users work. Very often when you look at how users work, you notice that they’re using this as information, and this is something that was written three years ago and it’s not relevant anymore. I think shadowing is a good exercise to do regularly to discover things like that.

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CIBC Private Wealth Group LLC increased its holdings in MongoDB, Inc. (NASDAQ:MDB – Free Report) by 10.4% in the fourth quarter, according to its most recent disclosure with the Securities & Exchange Commission. The institutional investor owned 85,519 shares of the company’s stock after buying an additional 8,068 shares during the period. CIBC Private Wealth Group LLC owned about 0.11% of MongoDB worth $21,267,000 as of its most recent filing with the Securities & Exchange Commission.

A number of other hedge funds also recently bought and sold shares of MDB. Hilltop National Bank lifted its stake in shares of MongoDB by 47.2% during the 4th quarter. Hilltop National Bank now owns 131 shares of the company’s stock worth $30,000 after purchasing an additional 42 shares during the last quarter. NCP Inc. purchased a new position in MongoDB in the fourth quarter valued at about $35,000. Continuum Advisory LLC raised its holdings in MongoDB by 621.1% in the third quarter. Continuum Advisory LLC now owns 137 shares of the company’s stock worth $40,000 after purchasing an additional 118 shares in the last quarter. Versant Capital Management Inc lifted its position in shares of MongoDB by 1,100.0% during the fourth quarter. Versant Capital Management Inc now owns 180 shares of the company’s stock valued at $42,000 after buying an additional 165 shares during the last quarter. Finally, Wilmington Savings Fund Society FSB bought a new stake in shares of MongoDB in the 3rd quarter valued at about $44,000. Hedge funds and other institutional investors own 89.29% of the company’s stock.

MongoDB Trading Down 1.5 %

Shares of MDB opened at $175.40 on Tuesday. The firm has a market cap of $14.24 billion, a P/E ratio of -64.01 and a beta of 1.30. MongoDB, Inc. has a 12 month low of $170.85 and a 12 month high of $387.19. The stock’s 50 day moving average is $244.01 and its two-hundred day moving average is $265.11.

MongoDB (NASDAQ:MDB – Get Free Report) last announced its 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). The business had revenue of $548.40 million for the quarter, compared to analysts’ expectations 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 last year, the business earned $0.86 EPS. Equities research analysts forecast that MongoDB, Inc. will post -1.78 EPS for the current year.

Insider Buying and Selling

In related news, Director Dwight A. Merriman sold 3,000 shares of the stock in a transaction on Monday, March 3rd. The stock was sold at an average price of $270.63, for a total value of $811,890.00. Following the completion of the transaction, the director now directly owns 1,109,006 shares of the company’s stock, valued at approximately $300,130,293.78. This represents a 0.27 % decrease in their position. The transaction was disclosed in a document filed with the Securities & Exchange Commission, which can be accessed through the SEC website. Also, CAO Thomas Bull sold 169 shares of MongoDB stock in a transaction on Thursday, January 2nd. The stock was sold at an average price of $234.09, for a total transaction of $39,561.21. Following the sale, the chief accounting officer now directly owns 14,899 shares in the company, valued at approximately $3,487,706.91. This trade represents a 1.12 % decrease in their position. The disclosure for this sale can be found here. Insiders sold a total of 43,139 shares of company stock worth $11,328,869 in the last 90 days. Insiders own 3.60% of the company’s stock.

Analyst Ratings Changes

A number of research firms recently commented on MDB. Cantor Fitzgerald assumed coverage on shares of MongoDB in a research report on Wednesday, March 5th. They set an “overweight” rating and a $344.00 target price for the company. China Renaissance began coverage on MongoDB in a research report on Tuesday, January 21st. They issued a “buy” rating and a $351.00 price objective for the company. UBS Group set a $350.00 price target on MongoDB in a report on Tuesday, March 4th. Needham & Company LLC dropped their price target on MongoDB from $415.00 to $270.00 and set a “buy” rating on the stock in a research note on Thursday, March 6th. Finally, Mizuho increased their price objective on shares of MongoDB from $275.00 to $320.00 and gave the company a “neutral” rating in a research report on Tuesday, December 10th. Seven analysts have rated the stock with a hold rating and twenty-three have issued a buy rating to the company. According to data from MarketBeat, the company currently has a consensus rating of “Moderate Buy” and a consensus price target of $319.87.

Read Our Latest Stock Report on MongoDB

About MongoDB

(Free Report)

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

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This instant news alert was generated by narrative science technology and financial data from MarketBeat in order to provide readers with the fastest and most accurate reporting. This story was reviewed by MarketBeat’s editorial team prior to publication. Please send any questions or comments about this story to contact@marketbeat.com.

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MongoDB (NASDAQ:MDB – Free Report) had its price objective trimmed by Citigroup from $430.00 to $330.00 in a research note published on Tuesday morning, MarketBeat reports. The brokerage currently has a buy rating on the stock.

Several other equities analysts have also weighed in on the stock. DA Davidson lifted their target price on shares of MongoDB from $340.00 to $405.00 and gave the company a “buy” rating in a report on Tuesday, December 10th. Wells Fargo & Company cut shares of MongoDB from an “overweight” rating to an “equal weight” rating and cut their price objective for the company from $365.00 to $225.00 in a report on Thursday, March 6th. Tigress Financial lifted their price target on MongoDB from $400.00 to $430.00 and gave the company a “buy” rating in a research note on Wednesday, December 18th. JMP Securities reiterated a “market outperform” rating and set a $380.00 price target on shares of MongoDB in a research report on Wednesday, December 11th. Finally, Scotiabank reissued a “sector perform” rating and issued a $240.00 price objective (down from $275.00) on shares of MongoDB in a report on Wednesday, March 5th. Seven investment analysts have rated the stock with a hold rating and twenty-four have issued a buy rating to the stock. According to MarketBeat.com, the company currently has a consensus rating of “Moderate Buy” and a consensus target price of $312.84.

Get Our Latest Stock Report on MDB

MongoDB Price Performance

NASDAQ MDB opened at $176.61 on Tuesday. The company has a fifty day moving average of $242.38 and a 200 day moving average of $264.31. The company has a market capitalization of $14.34 billion, a PE ratio of -64.46 and a beta of 1.30. MongoDB has a 1 year low of $170.66 and a 1 year high of $387.19.

MongoDB (NASDAQ:MDB – Get Free Report) last issued its quarterly earnings data 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 company had revenue of $548.40 million during the quarter, compared to analyst estimates 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 period last year, the firm earned $0.86 EPS. Research analysts expect that MongoDB will post -1.78 earnings per share for the current year.

Insider Activity

In related news, CFO Michael Lawrence Gordon sold 1,245 shares of the company’s stock in a transaction that occurred on Thursday, January 2nd. The stock was sold at an average price of $234.09, for a total value of $291,442.05. Following the transaction, the chief financial officer now directly owns 79,062 shares in the company, valued at approximately $18,507,623.58. This represents a 1.55 % decrease in their ownership of the stock. The transaction was disclosed in a legal filing with the SEC, which is accessible through this link. Also, CAO Thomas Bull sold 169 shares of the firm’s stock in a transaction on Thursday, January 2nd. The stock was sold at an average price of $234.09, for a total value of $39,561.21. Following the sale, the chief accounting officer now directly owns 14,899 shares of the company’s stock, valued at approximately $3,487,706.91. This trade represents a 1.12 % decrease in their position. The disclosure for this sale can be found here. In the last quarter, insiders have sold 43,139 shares of company stock valued at $11,328,869. 3.60% of the stock is currently owned by insiders.

Institutional Trading of MongoDB

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

About MongoDB

(Get Free Report)

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

See Also

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

Posted on mongodb google news. Visit mongodb google news

Daiwa Capital Markets began coverage on shares of MongoDB (NASDAQ:MDB – Free Report) in a research report released on Tuesday, MarketBeat Ratings reports. The brokerage issued an outperform rating and a $202.00 price target on the stock.

Several other research analysts have also issued reports on the company. Oppenheimer lowered their target price on MongoDB from $400.00 to $330.00 and set an “outperform” rating for the company in a research report on Thursday, March 6th. Robert W. Baird reduced their target price on MongoDB from $390.00 to $300.00 and set an “outperform” rating on the stock in a report on Thursday, March 6th. UBS Group set a $350.00 target price on shares of MongoDB in a research report on Tuesday, March 4th. Royal Bank of Canada dropped their price target on shares of MongoDB from $400.00 to $320.00 and set an “outperform” rating on the stock in a research report on Thursday, March 6th. Finally, Stifel Nicolaus decreased their price objective on shares of MongoDB from $425.00 to $340.00 and set a “buy” rating for the company in a research report on Thursday, March 6th. Seven analysts have rated the stock with a hold rating and twenty-four have issued a buy rating to the stock. According to data from MarketBeat.com, the stock presently has an average rating of “Moderate Buy” and an average price target of $312.84.

Get Our Latest Analysis on MDB

MongoDB Trading Up 0.7 %

MongoDB stock opened at $176.61 on Tuesday. The company has a market capitalization of $14.34 billion, a PE ratio of -64.46 and a beta of 1.30. The stock has a 50-day moving average price of $242.38 and a two-hundred day moving average price of $264.31. MongoDB has a 12 month low of $170.66 and a 12 month high of $387.19.

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

Insider Activity

In related news, CAO Thomas Bull sold 169 shares of the company’s stock in a transaction on Thursday, January 2nd. The stock was sold at an average price of $234.09, for a total transaction of $39,561.21. Following the completion of the sale, the chief accounting officer now owns 14,899 shares in the company, valued at approximately $3,487,706.91. This represents a 1.12 % decrease in their ownership of the stock. The sale was disclosed in a filing with the SEC, which can be accessed through this link. Also, CFO Michael Lawrence Gordon sold 1,245 shares of the firm’s stock in a transaction on Thursday, January 2nd. The shares were sold at an average price of $234.09, for a total value of $291,442.05. Following the transaction, the chief financial officer now owns 79,062 shares in the company, valued at approximately $18,507,623.58. This trade represents a 1.55 % decrease in their ownership of the stock. The disclosure for this sale can be found here. Insiders sold 43,139 shares of company stock worth $11,328,869 over the last three months. 3.60% of the stock is currently owned by insiders.

Institutional Trading of MongoDB

A number of large investors have recently added to or reduced their stakes in the stock. OneDigital Investment Advisors LLC grew its position in shares of MongoDB by 3.9% during the 4th quarter. OneDigital Investment Advisors LLC now owns 1,044 shares of the company’s stock valued at $243,000 after acquiring an additional 39 shares during the period. Hilltop National Bank grew its holdings in MongoDB by 47.2% during the fourth quarter. Hilltop National Bank now owns 131 shares of the company’s stock valued at $30,000 after purchasing an additional 42 shares during the period. Avestar Capital LLC grew its holdings in MongoDB by 2.0% during 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 increased its position in MongoDB by 1.4% in the fourth quarter. Aigen Investment Management LP now owns 3,921 shares of the company’s stock worth $913,000 after buying an additional 55 shares during the last quarter. Finally, Perigon Wealth Management LLC raised its stake in shares of MongoDB by 2.7% in the fourth quarter. Perigon Wealth Management LLC now owns 2,528 shares of the company’s stock valued at $627,000 after buying an additional 66 shares during the period. Hedge funds and other institutional investors own 89.29% of the company’s stock.

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

Posted on mongodb google news. Visit mongodb google news

In our exclusive GenAI segment, The Fast Mode spoke to MongoDB’s Benjamin Lorenz on how AI and Generative AI is transforming telecom and enterprise networks, from managing network performance to mitigating threats and improving efficiency.

The telco industry faces ongoing challenges, such as managing ever-growing data volumes, optimizing networks in real time, and mitigating security risks. Legacy infrastructure and siloed data create additional obstacles to achieving seamless connectivity and operational efficiency. Gen AI offers a bright future by enabling intelligent automation, predictive maintenance, and dynamic resource allocation. By analyzing vast volumes of network data in real time, gen AI can predict potential failures, refine routing, and automate troubleshooting, reducing manual intervention and improving overall performance. However, as with any transformative technology, gen AI introduces challenges that need to be carefully managed to unlock its full potential.

• Data integrity: Gen AI models are only as good as the data on which they are trained. Inaccurate, incomplete, or biased datasets can lead to flawed predictions, affecting network performance and decision-making.
• Infrastructure modernization: Many telecom providers still rely on legacy systems not designed to support AI-driven automation and real-time analytics. Upgrading these systems to handle gen AI’s computational demands requires substantial investment and operational changes.
• Security governance: With AI-driven networks processing vast volumes of sensitive data, ensuring robust security measures and compliance with regulatory requirements is crucial. Lack of proper governance may lead to vulnerabilities, exposing networks to potential cyber threats and data breaches.

To overcome these challenges, companies must adopt a holistic approach integrating robust data strategies, infrastructure modernization, and stringent security governance. First, addressing data integrity involves ensuring the accuracy and completeness of data and building systems capable of processing network data in real time. Leveraging high-performance distributed databases and adopting cloud-native architectures play a crucial role in this. These solutions provide the scalability needed to handle AI-driven automation and real-time analytics, enabling telecom providers to modernize legacy systems and transition to agile, AI-ready infrastructures.

As telecom networks process increasingly sensitive data, ensuring robust security becomes paramount. A unified strategy should incorporate zero-trust architectures and AI-driven anomaly detection, which can enhance data protection, mitigate security risks, and ensure compliance with regulatory standards. This approach ensures that telecom providers optimize network performance and decision-making and create a resilient and secure infrastructure capable of supporting the transformative potential of Gen AI. By integrating these solutions, telecom companies can future-proof their networks, drive efficiency, and provide a seamless and secure customer experience.

This article is a part of The Fast Mode’s 2025 Special Edition: GenAI segment. To learn more about the segment, visit the dedicated page here. To view all articles published under the segment, click here. A research report based on the findings of the segment survey will be published in February 2025. To access the survey, click here.

The views expressed in this article belong solely to the author and do not represent The Fast Mode. While information provided in this post is obtained from sources believed by The Fast Mode to be reliable, The Fast Mode is not liable for any losses or damages arising from any information limitations, changes, inaccuracies, misrepresentations, omissions or errors contained therein. The heading is for ease of reference and shall not be deemed to influence the information presented.

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Article originally posted on InfoQ. Visit InfoQ

The Bun team recently released Bun v1.2, with major progress regarding compatibility with Node.js, a key component of Bun’s value proposition. Bun 1.2 also now has a built-in S3 object storage API and a built-in Postgres client that comes in addition to the existing built-in SQLite client.

Bun, designed as a drop-in replacement for Node.js, has Node.js compatibility as a core target feature. With Bun 1.2, the team now measures that compatibility by running the Node.js test suite on every change to the code. Core Node modules such as path, os, string_decoder, puny_code, query_string, url, events, stream, fs, and more now pass 90% of the Node.js test suite.

The release reports however that the Node.js test suite could not be run as is, as it is specific to Node.js idiosyncrasies. For instance, the test suite checks the exact Node.js string of error messages, which may change between releases. Some tests also relied on internal Node.js details. As a result, the Bun team ported the Node test suite to Bun, for instance replacing error strings with error codes so Bun has the freedom to add helpful details to the error messages.

Bun v1.2 also adds support for the node:http2 module to create HTTP/2 servers, with a purported 2x speed improvement vs. the same module as part of the Node.js distribution. Bun v1.2 additionally adds support for node:dgram (UDP sockets management), node:cluster (spawning new Bun instances), and node:zlib.

Importantly, Bun 1.2 adds built-in support for S3. Bun developers can now read, write, and delete files from an S3 bucket using APIs that are compatible with Web standards like Blob. The release note explained:

Decoupling storage from compute prevents an entire class of reliability issues: low disk space, high p95 response times from busy I/O, and security issues with shared file storage.

S3 is the defacto-standard for object storage in the cloud. The S3 APIs are implemented by a variety of cloud services, including Amazon S3, Google Cloud Storage, Cloudflare R2, and dozens more.

Bun S3’s native client touts 5x speed improvement when downloading files vs. packages like @aws-sdk/client-s3 with Node.js.

Bun 1.2 additionally expands support for SQL databases and contributes Bun.sql, a built-in SQL client with Postgres support. The new client comes to complement the existing built-in SQ_Lite_ client.

SQLite fits a wide variety of use cases. As Wesley Aptekar-Cassels argued on his blog:

On the whole, I think using SQLite is a good tradeoff for a lot of projects, including web apps that expect to have a potentially large number of users. As long as you don’t expect to need tens of thousands of small writes per second, thousands of large writes, or long-lived write transactions, it’s highly likely that SQLite will support your use case. It significantly reduces complexity and operational burden and eases testing, with the primary downside that it’s somewhat harder to get levels of availability and uptime that almost no one needs in the first place.

Postgres support means Bun developers also have a built-in option for those heavy use cases when SQLite is no longer a good fit. The client is written in native code with optimizations including automatic prepared statements, query pipelining, connection pooling, and structure caching. The release note claims up to 50% speed improvement when reading rows vs, using the most popular Postgres clients with Node.js.

By introducing built-in support for popular data stores like S3 and Postgres, Bun strives to accommodate further the demands of production applications for scalable, cloud-native solutions with fewer external dependencies.

Bun v1.2 is a large release with plenty of additional and important features. Developers are invited to review the full release note.

Bun is written in Zig and uses WebKit’s JavaScriptCore for its JavaScript engine. Bun 1.0 was released in September 2023