Month: June 2023

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MongoDB said today it’s beefing up its cloud database MongoDB Atlas with a series of new products and features that it says will make it easier for developers to build modern applications.

They include new generative artificial intelligence capabilities for MongoDB’s Vector Search feature, enabling the retrieval and personalization of highly relevant information, Search Nodes for dedicated resources with search workloads at scale and Stream Processing high-velocity streams of data.

The new capabilities in MongoDB Atlas were announced at the company’s annual developer conference MongoDB.local NYC, alongside a bunch of other updates. Also new today is MongoDB Relational Migrator, a tool that simplifies application migration, and a new partnership with Google Cloud that’s intended to accelerate the use of generative AI to build new types of applications.

MongoDB is the creator of the document-oriented MongoDB database, which is used for a wide range of data-intensive applications and prized for its ability to store information in multiple different formats. MongoDB Atlas is the cloud-hosted version of that database.

The company explained that the choice of database is critical for enterprises that want to take advantage of generative AI, which is the technology that sits at the heart of next-generation chatbots such as OpenAI LP’s ChatGPT. It said a database that’s unified, fully managed, flexible and scalable makes life much easier for developer teams.

Today’s updates mean that MongoDB Atlas is positioned to become that database, the company said. Its updated Vector Search feature is key to building generative AI apps, as they require data to be stored in “vectors,” or geometric representations.

Generative AI models work by measuring the similarity between vectors to construct sentences or images probabilistically from prompts or return accurate search results with more context than traditional search engines. With MongoDB Atlas Vector Search, companies can support new workloads such as semantic search, text-to-image search and personalized recommendation systems.

MongoDB Atlas Vector Search also makes it possible to augment the capabilities of existing generative AI models with additional data to create more accurate results in specific use cases and domains, the company said.

Constellation Research Inc. analyst Doug Henschen told SiliconANGLE that vector search is a key enabler of generative AI workloads. “Vectors are geometric/numerical representations of semantically similar text, images, audio and other content, so the possibilities for helping to train custom large language models with your own data are limitless,” he pointed out. “Vectors could help to improve natural language query, NL text/image/code generation and more.”

Meanwhile, MongoDB Atlas Stream processing is a key update that makes it easier to process streaming data and support real-time applications. In Henschen’s view, this is the most important update for customers, as low-latency workloads are becoming more and more prevalent for enterprises.

“MongoDB really had to step up on this front to live up to its billing as the developer data platform,” the analyst said. “Rival data platforms associated with analytics, such as Snowflake and Databricks, have already addressed real-time needs, so MongoDB is filling a competitive gap.

There’s also greater scalability with Time Series Collections, which make it easier to handle time-series workloads with the ability to modify such data after it has been ingested. That’s important because time series databases typically do not allow for data to be modified after it has been created, even when errors have occurred.

A final update pertains to the ability to tier and query data on Microsoft Azure with MongoDB Atlas Online Archive and Atlas Data Federation. With this, customers can tie their MongoDB databases to the most cost-effective cloud storage tier, while retaining the ability to query with higher performance.

MongoDB Chief Executive Dev Ittycheria (pictured) said the new features were prioritized based on customer feedback. “We’re further supporting customers running the largest, most demanding and mission-critical workloads that require continually increasing scalability and flexibility,” he said.

Migrations and integrations

The new MongoDB Relational Migrator tool is designed to help developers that want to migrate their existing applications from a legacy database to MongoDB, and is promised to do this in a cost-effective way with zero risk.

Meanwhile, the new partnership with Google Cloud pertains to MongoDB’s integration with Vertex AI, which is a suite of tools for data scientists to build, automate, standardize and manage machine learning projects, including generative AI models.

Ittycheria said the shift to generative AI begins first and foremost with developers, who are the ones tasked with integrating the technology into a new class of business applications. “We want to democratize access to game-changing technology so all developers can build the next big thing,” the CEO said. “With our strategic partnership with Google Cloud, it’s now easier for organizations of all shapes and sizes to incorporate AI into their applications.”

Henschen said the integration with Google Cloud plays to MongoDB’s new vector search capabilities. “MongoDB can now provide the data expressed as vectors to drive large language model training, and Vertex AI is one of the many tools available for developing custom LLMs,” he explained.

New verticals

The flurry of updates continued with the announcement of MongoDB Atlas for Industries, which was billed as a new program for organizations to accelerate cloud adoption and modernization through industry-specific expertise and integrated solutions. The idea is to provide customers with access to expert-led architectural design reviews, knowledge accelerators to provide more relevant training for developer teams, and partnerships to build solutions to industry-specific challenges, MongoDB said.

MongoDB Atlas for Industries sees the company launch its first-ever vertical offerings for financial services customers, and there are more to come in manufacturing, automotive, insurance, healthcare and retail later this year.

Like with the stream processing updates, MongoDB’s new support for industry verticals follows in the footsteps of its competitors, namely Amazon Web Services Inc., Google Cloud, Snowflake and Databricks, Henschen pointed out. “MongoDB is starting with the one industry that’s on everyone’s shortlist, financial services, because that’s where the most money promises to be,” he added. “The typical payoff for vertical industry clouds is prebuilt solutions and short cuts for common use cases. So far it’s early days, so we’re yet to see what it will offer in terms of speeding time to market and accelerating time to value.”

Finally, the company announced a number of more general updates for its databases, including expanded programming language support for MongoDB Atlas, a move that will simplify the deployment of resources on Amazon Web Services using infrastructure-as-code. There’s also a new Kotlin Driver for MongoDB, which makes it possible to build Kotlin-based applications, and MongoDB Atlas Kubernetes Operator, which simplifies the task of working with containerized applications.

“Everything MongoDB is announcing is designed to make it a more comprehensive and complete ‘developer data platform,’” Henschen said. “The more that MongoDB can provide to enable developers with all the tools that they need, the stickier the platform becomes for those developers and the organizations they work for.”

Photo: SiliconANGLE

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Organizations use data. Although this might sound like the most obvious statement ever made in the modern age of digital business, there is a deeper contextual meaning to draw from this home truth.

Organizations do use data, but across different industry verticals, firms use data in different ways, typically quite uniquely aligned to the needs of the functions carried out in the business use cases at hand. This combined difference and commonality means that enterprise technology vendors often go to market with an industry-specific set of offerings designed to provide pre-architected, custom-aligned, specifically-aligned and particularly-integrated technology services to customers.

MongoDB, Inc. reflected this precise move this week at its developer conference, MongoDB.local NYC. The company announced MongoDB Atlas for Industries, a new program to help organizations accelerate cloud adoption and modernization by leveraging industry-specific expertise, programs, partnerships and integrated solutions. 

With MongoDB Atlas for Industries, customers have access to expert-led architectural design reviews, technology partnerships that deliver enhanced solutions for industry-specific challenges and industry-specific knowledge accelerators to provide relevant technology training paths for development teams.

MongoDB Atlas for Industries is launching its first set of vertical solutions for financial services, an industry that is changing rapidly with shifts in automation and technology advancements. 

“Increasingly organizations in different industries are moving away from one-size-fits-all technology solutions to improve their competitive advantage and better serve customers. At MongoDB, we’ve built a team of experts with deep industry knowledge, many of whom used to be customers, so we are not only building better products with the right third party integrations, but our team can help customers get up and running faster,” said Boris Bialek, managing director of industry solutions, MongoDB. “Six of the top 10 global banks already use MongoDB in their infrastructure to solve some of their most critical problems. Launching Atlas for Industries is our commitment to continuing this level of dedication across financial services and other key industries.”

With MongoDB Atlas, financial institutions are promised the ability to improve customer experiences by modernizing legacy functionality on existing in-house banking systems and building composable architectures – architectures that make it easier to integrate best of breed third-party solutions – to get ideas to market faster with the performance and scale they need. MongoDB Atlas for Industries programs for manufacturing and automotive, insurance, healthcare, retail and other industries will follow over the course of the year.

The company says that tens of thousands of customers rely on MongoDB to power their applications every day, but each industry has its own unique set of challenges and needs. Many industries are also at a crossroads between being restricted by the capabilities of their legacy environments and the opportunity that is being presented by next-generation applications, data analytics and generative AI. 

However, additional pressures like cost optimization can make it seem like transformation efforts are being stifled. Despite all of this, many organizations still feel a sense of urgency to modernize workloads for their most mission-critical applications and innovate quickly to remain competitive and meet customer demands. When providers can offer experts who understand the nuances and have deep experience working in a specific industry along with their technology solutions, it delivers the extra support organizations need to get started.

MongoDB Atlas for Industries provides:

• Industry Innovation Workshops – dedicated executive engagement with industry experts from MongoDB and our partners to discuss client-specific solutions using best practices developed through proven industry experience.
• Industry Access Pass – access to MongoDB’s industry-specific partner integrations and toolchains to evaluate how to accelerate innovation and modernization projects
  
• Industry Jumpstart Assessment – building on the innovation workshops, organizations can engage with the MongoDB professional services team and industry experts to build a minimum viable product to help shape or refine a project.
  
• Industry Knowledge Accelerators – tailored MongoDB University courses and learning materials, including unlimited access to curated webinars and solutions sessions, to enable developer success

With changes in customer demands, regulatory compliance and new players challenging incumbents, the financial services industry, which has traditionally been conservative in its adoption of new technology, is under increasing pressure to modernize applications and innovate consumer experiences. MongoDB Atlas is built to help with this modernization, as well as enable organizations to build next-generation applications that take advantage of new technologies like generative AI. MongoDB Atlas also provides the resilience, scale and highest levels of data privacy and regulatory compliance that MongoDB financial customers require.

As part of the MongoDB Atlas for Financial Services launch, MongoDB has achieved Amazon Web Services (AWS) Financial Services Competency. To obtain this competency, MongoDB was tested against strict security, operational and reliability requirements, validating that MongoDB and AWS can help financial institutions get ideas to market faster, while reducing cost and enhancing business agility.

“MongoDB has been a trusted AWS Partner for eight years, giving customers running MongoDB Atlas on AWS a more powerful experience for building modern applications,” Mona Chadha, director of infrastructure partnerships, ISVs, AWS. “Achieving the AWS Financial Services Competency reaffirms MongoDB’s commitment to AWS and financial services customers, while demonstrating their breadth and depth of financial services expertise for use cases like fraud detection and real-time payments. With MongoDB Atlas on AWS, financial institutions can optimize to reduce costs in the short term while simultaneously transforming their infrastructure for long-term growth to embrace emerging technologies such as machine learning and AI.”

This AWS work builds on MongoDB’s long-standing relationship with AWS, including MongoDB Atlas’ availability in the AWS Marketplace. Additionally, MongoDB has become a critical foundation of the Temenos Banking Cloud, with a recent benchmark proving that together, Temenos and MongoDB can support the needs of even the largest global banks with exceptionally high performance while providing transparent data access through the JSON document model.

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MongoDB on Thursday unveiled a host of new features for its database, highlighted by new data migration and generative AI capabilities.

The new features — some of which are generally available while others are in preview — were revealed during MongoDB.local NYC, an in-person user event for application developers in New York City, where the vendor is based. In addition, MongoDB launched updated versions of numerous existing tools.

As a group, the new and updated capabilities represent mostly incremental progress for MongoDB, according to Stephen Catanzano, an analyst at TechTarget’s Enterprise Strategy Group.

However, the addition of generative AI capabilities through a partnership with Google Cloud and a tool called MongoDB Atlas Vector Search stands apart, he noted.

“The generative AI announcement would be considered innovative,” Catanzano said. “MongoDB’s audience is very developer-centric, and with Google Cloud and the tools they are enabling [with generative AI] to attract developers, this should get the attention of developers trying to quickly integrate generative AI. The rest is mostly moving the ball forward.”

MongoDB is a database vendor whose NoSQL (Not only SQL) database was built as an alternative to relational databases, which now sometimes fail to meet the modern needs of data-driven organizations.

Relational databases struggle to discover relationship between data points, which is a necessity as organizations ingest data from an ever increasing number of sources. In addition, because they were first invented in 1970, the databases were not built for the scale many organizations now require as their data volume continues to grow.

As a result, alternatives such as graph databases that specialize in discovering relationships between data points are becoming more popular. MongoDB, meanwhile, is a document-based database, and is designed to work with large sets of distributed data.

Last year, MongoDB launched version 6.0 of its database, which included a new queryable encryption feature that enables users to search encrypted data and an updated version of its time series capabilities to increase the speed with which it can read data.

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Improving the feedback time of a continuous integration (CI) system and optimizing the test methods and classes resulted in more effective feedback for development teams. According to Tobias Geyer, CI systems are an important part of the development process and should be treated as such.

Tobias Geyer spoke about improving a continuous integration system as a tester at the Romanian Testing Conference 2023.

When Geyer started at the company, the continuous integration system was an old developer PC that had been put under a desk, and nobody was responsible for it, with no time to do maintenance. Being slow it resulted in very long feedback cycles and developers ignoring the feedback from the CI system altogether, he said.

A quick win mentioned to improve the feedback time was to skip build steps during business hours that weren’t needed all the time and limit them to a nightly build, Geyer said.

A problem that took more effort to fix was the disk I/O bottleneck. The build did read and write so much data that the hard disk couldn’t keep up. Geyer mentioned that he got in touch with their IT department and moved the CI system from the developer PC to virtual machines in the datacenter. That allowed them to fix the disk I/O issue and scale the system to two machines, enabling them to run more builds in parallel.

After figuring out which tests were fast and which were slow, they split them into categories and ran only a subset of the tests as part of their fast feedback CI builds. The slower tests were moved to a dedicated build that was run less frequently. That way they still got the full feedback but they got the majority of it way faster than before, Geyer explained.

Geyer described how they optimized the test methods and classes:

Once we identified which tests were slow, we treated them like technical debt. We created “test debt budgets” for developers to improve the tests, which they approached in different ways:

1. Test data got trimmed down so that only the relevant data was kept, which shortened the setup time for tests.

2. Mocking was introduced in the tests, making it obsolete to load any test data in the first place.

3. The product code was made more testable so that the same checks could be done as unit tests instead of integration tests.

Geyer concluded that it’s not necessary to have a deep understanding of the technical side of things to have an impact:

I can use my testing skills of measuring, experimenting and collaborating to make changes for the better happen, even if it means that someone else has to do the actual implementation work.

InfoQ interviewed Tobias Geyer about improving the CI system.

InfoQ: Can you give an example of a build step that was skipped?

Tobias Geyer: The most prominent example was our product obfuscation. Each product artifact gets obfuscated before it’s delivered to the customer, and obviously the obfuscated product needs to be tested. Unfortunately, the obfuscation takes at least 30 minutes. We’re skipping it during the day, at night the products get obfuscated and tested that way.

InfoQ: What major changes did you do to the building process and platform?

Geyer: We migrated our build system from Ant and Windows batch files to Gradle. That was done mostly by the developers in my team with me taking care of all the parts that had to deal with executing tests.

We made an obvious but important change by introducing a test CI system. This allowed us to prepare and test changes to the CI system (like plugin updates, new build nodes,…) without interrupting the normal development flow.

InfoQ: How did you encourage knowledge sharing about continuous integration among different teams?

Geyer: I looked for people in other parts of the company who worked with or on CI systems and had a similar tech stack. We had regular meetings where we discussed the latest changes and the problems we were facing. Quite often some team had already solved a problem and their solution could be re-used.

It was great to see what the others were doing and the results they had. There were cases where teams wanted to introduce a change but met pushback in their teams. Being able to say “This other team made good experiences with it” helped to convince people.

InfoQ: What’s your advice to teams that are unhappy with their CI solution?

Geyer: Approach it like any other software development project. Make a list of the issues that bother you and treat them like bugs. Which means: Prioritize them, analyze them and then collaborate on fixing them. I’d like to stress the “collaboration” point – talking to our IT department was crucial to get some of the issues fixed.

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Fulton Bank N.A. bought a new stake in MongoDB, Inc. (NASDAQ:MDB – Get Rating) during the first quarter, according to its most recent disclosure with the Securities and Exchange Commission (SEC). The firm bought 1,097 shares of the company’s stock, valued at approximately $256,000.

A number of other institutional investors and hedge funds have also bought and sold shares of MDB. Cherry Creek Investment Advisors Inc. raised its stake in shares of MongoDB by 1.5% during the fourth quarter. Cherry Creek Investment Advisors Inc. now owns 3,283 shares of the company’s stock worth $646,000 after acquiring an additional 50 shares in the last quarter. Allworth Financial LP raised its stake in shares of MongoDB by 12.9% during the fourth quarter. Allworth Financial LP now owns 508 shares of the company’s stock worth $100,000 after acquiring an additional 58 shares in the last quarter. Cetera Advisor Networks LLC raised its stake in shares of MongoDB by 7.4% during the second quarter. Cetera Advisor Networks LLC now owns 860 shares of the company’s stock worth $223,000 after acquiring an additional 59 shares in the last quarter. First Republic Investment Management Inc. raised its stake in shares of MongoDB by 1.0% during the fourth quarter. First Republic Investment Management Inc. now owns 6,406 shares of the company’s stock worth $1,261,000 after acquiring an additional 61 shares in the last quarter. Finally, Janney Montgomery Scott LLC raised its stake in shares of MongoDB by 4.5% during the fourth quarter. Janney Montgomery Scott LLC now owns 1,512 shares of the company’s stock worth $298,000 after acquiring an additional 65 shares in the last quarter. 89.22% of the stock is owned by hedge funds and other institutional investors.

Insider Transactions at MongoDB

In other MongoDB news, Director Hope F. Cochran sold 2,174 shares of the business’s stock in a transaction dated Thursday, June 15th. The stock was sold at an average price of $373.19, for a total value of $811,315.06. Following the completion of the transaction, the director now owns 8,200 shares of the company’s stock, valued at approximately $3,060,158. The sale was disclosed in a legal filing with the SEC, which is available through this link. In other MongoDB news, CRO Cedric Pech sold 720 shares of the business’s stock in a transaction dated Monday, April 3rd. The stock was sold at an average price of $228.33, for a total value of $164,397.60. Following the completion of the transaction, the executive now owns 53,050 shares of the company’s stock, valued at approximately $12,112,906.50. The sale was disclosed in a legal filing with the SEC, which is available through this link. Also, Director Hope F. Cochran sold 2,174 shares of the business’s stock in a transaction dated Thursday, June 15th. The stock was sold at an average price of $373.19, for a total value of $811,315.06. Following the completion of the transaction, the director now directly owns 8,200 shares of the company’s stock, valued at approximately $3,060,158. The disclosure for this sale can be found here. Insiders sold a total of 108,856 shares of company stock valued at $27,327,511 in the last quarter. 4.80% of the stock is owned by insiders.

Analysts Set New Price Targets

MDB has been the topic of several research reports. Stifel Nicolaus lifted their price target on shares of MongoDB from $240.00 to $375.00 in a research note on Friday, June 2nd. The Goldman Sachs Group lifted their target price on shares of MongoDB from $280.00 to $420.00 in a research note on Friday, June 2nd. Needham & Company LLC lifted their target price on shares of MongoDB from $250.00 to $430.00 in a research note on Friday, June 2nd. Royal Bank of Canada lifted their target price on shares of MongoDB from $235.00 to $400.00 in a research note on Friday, June 2nd. Finally, Wedbush decreased their target price on shares of MongoDB from $240.00 to $230.00 in a research note on Thursday, March 9th. One research analyst has rated the stock with a sell rating, two have assigned a hold rating and twenty-one have assigned a buy rating to the company. Based on data from MarketBeat, MongoDB currently has a consensus rating of “Moderate Buy” and an average price target of $328.35.

MongoDB Price Performance

MDB opened at $372.96 on Thursday. The stock has a 50-day moving average price of $289.02 and a 200-day moving average price of $235.41. The company has a debt-to-equity ratio of 1.44, a current ratio of 4.19 and a quick ratio of 4.19. The firm has a market cap of $26.12 billion, a PE ratio of -79.86 and a beta of 1.04. MongoDB, Inc. has a 1 year low of $135.15 and a 1 year high of $398.89.

MongoDB (NASDAQ:MDB – Get Rating) last posted its quarterly earnings results on Thursday, June 1st. The company reported $0.56 earnings per share (EPS) for the quarter, topping the consensus estimate of $0.18 by $0.38. The firm had revenue of $368.28 million during the quarter, compared to analyst estimates of $347.77 million. MongoDB had a negative return on equity of 43.25% and a negative net margin of 23.58%. MongoDB’s quarterly revenue was up 29.0% compared to the same quarter last year. During the same quarter in the previous year, the firm earned ($1.15) EPS. Sell-side analysts predict that MongoDB, Inc. will post -2.85 earnings per share for the current fiscal year.

MongoDB Profile

(Get Rating)

MongoDB, Inc provides general purpose database platform worldwide. The company offers 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-premise, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

Further Reading

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Aerospike this week rolled out new graph database offering that leverages open source components, including the TinkerPop graph engine and the Gremlin graph query language. The NoSQL company foresees the new property graph being used by customers initially for OLTP workloads, such as fraud detection and identity authentication, with the possibility of OLAP functionality in the future.

Aerospike initially emerged as a distributed key-value store designed to store and query data at high speeds with low latencies. Over time, it became a multi-modal database by supporting SQL queries, via the Presto support it unveiled in 2021, as well as the capability to store and query JSON documents, added last year.

When Aerospike executives heard that some of its financial services customers were spending their own time and money developing bespoke graph databases to handle specific compute-intensive tasks–such as detecting fraud in financial transactions–they decided it was a good time to add graph to the mix.

“We had this payment company that had done this at scale,” says Lenley Hensarling, Aerospike’s chief product officer. “And we looked around at other of our customers who are throwing bespoke graph code, hand-coding graphs in order to get the throughput and the scale of data for a real production application of graphs.”

The product developers at Aerospike realized they could take Apache TinkerPop, an open source graph query engine that also forms the heart of the AWS Neptune and the Microsoft Azure Cosmos DB graph database offerings, and integrate it into the Aerospike storage engine.  JanusGraph’s Gremlin was selected as the initial graph language, although the company is aiming to support openCypher, which is the open source version of Neo’s graph query language.

The combination of TinkerPop query engine, Gremlin query language, and Aerospike’s data management capabilities is a general-purpose property graph database that’s suitable for the types of transactional and operational use cases its customers require, Hensarling says.

“There’s just white space for graph solutions at scale,” he tells Datanami. “We believe there’s an unmet need. We can provide tens of thousands to hundreds of thousands to millions of transactions per second. It’s not going to be as fast as the key-value lookup, for sure. But it’s going to be over and over again, for many different applications.”

Fraud detection and identity authentication are the two main use cases that Aerospike sees customers using the graph database to build. Fraud detection, where connections to known fraudulent entities (people, businesses, devices, etc.) can be quickly discovered in real time, is a classic property graph workload.

But modern identity authentication methods today–in which multiple pieces of data are brought to bear to determine that yes, this person is really who they claim to be–are beginning to closely resemble that fraud detection workload, too.

Aerospike has optimized its database to deliver two to five “hops,” which is the number of traversals a query makes as it travels along vertices to find other connected nodes, within a short amount of time. Completing the graph lookup within about 20 milliseconds is the goal, Hensarling says.

“It’s part of a longer transaction,” he says of the graph lookups. “They may use graph for part of it. They may use AI and ML stuff in another part. But they have seconds to do the whole chain of things and typically it’s like 20 milliseconds” for the graph component.

Aerospike worked with Marko Rodriguez, the creator of TinkerPop, to develop a connection to the Aerospike database, Hensarling says. That layer, which Aerospike developers called Firefly, enables OLTP workloads, but a similar layer could be adapted that leverages TinkerPop for OLAP and graph analytics workloads, he says.

The company has done a lot of development work in the past 18 months that prepared it for the move into the graph database realm, Hensarling says. That includes work on secondary indexes, as well as the support for predicate pushdowns, where data processing work is pushed into the database engine. “That has allowed us to do this at a much faster, scalable route than we could have previously,” he says.

For small deployments, all of the storage and query engines could sit in the same namespace, Hensarling says. But large Aerospike graph deployments will likely resemble large Aerospike Trino (or Presto) deployments, where the data is persisted on an Aerospike cluster while the TinkerPop query engine sits on a separate cluster. The TinkerPop cluster will run the queries against the Aerospike data, and will scale horizontally if necessary to handle bigger workloads.

“If you need more throughput, you can just stand up more nodes of TinkerPop,” Hensarling says. “And you can also take them down as you have bursts of transactions, because the data is held in Aerospike and it’s persisted, so you just connect it again and scale out. That’s something people have really responded to as well.”

The graph database has been in beta with Aerospike customers for several months. The largest  deployment so far involved a financial transaction processing company that had a graph with billions of vertices and thousands of edges, with responses coming back in 15 milliseconds, Hensarling says.

Aerospike is confident that its new graph offering will resonate with customers, particularly among those that need to combine graph capabilities with other database capabilities.

“There’s an unmet need in the marketplace,” Hensarling says. “People don’t want yet another database all the time. If they can use the skills for operations and leverage them across more types of workloads, that’s good, as long as the performance and the semantic coverage is there.”

Related Items:

Aerospike Adds JSON Support, Preps for Fast, Multi-Modal Future

Aerospike’s Presto Connector Goes Live

Aerospike Turbocharges Spark ML Training with Pushdown Processing

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Wednesday’s IBD 50 Stocks To Watch pick, AI stock leader MongoDB (MDB), is one of the best stocks to buy and watch right now, with company earnings surging 180% in the latest quarter. MDB stock traded 2% lower midday Wednesday, near its recent lows.

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New York-based MongoDB provides an open-source database platform for businesses. It has a subscription-based software-as-a-service business model.

On June 2, MongoDB soared 28% after the company reported a 180% surge in quarterly profit to 56 cents a share vs. the year-ago period. Revenue increased 29% to $368.3 million.

“We believe the recent breakthroughs in AI represent the next frontier of software development,” said CEO Dev Ittycheria in the earnings report press release.

Ittycheria continued, “The move to embed AI in applications requires a broad and sophisticated set of capabilities while enabling developers to move even faster to create a competitive advantage. We are confident MongoDB’s developer data platform is well positioned to benefit from the next wave of AI applications in the years to come.”

MongoDB Earnings Surge

With huge earnings growth in the latest quarter, but no long-term track record of profitability, MDB stock shows an 83 out of a perfect 99 Earnings Per Share Rating, according to IBD Stock Checkup. This rating takes into account EPS growth and stability of earnings over the past three years. The two most recent quarters are weighted more heavily.

MDB stock boasts a perfect 99 IBD Composite Rating due to strong fundamentals in the latest quarter, along with powerful price action in recent months. The IBD Composite Rating is designed to help investors easily gauge the quality of a stock’s fundamental and technical metrics.

MDB ranks No. 1 in IBD’s Computer Software-Database industry group.

AI Stock Leader Is One Of Best Stocks To Buy And Watch

Following the AI stock leader’s June 2 earnings-fueled gap up, MongoDB is trading tightly, offering a new entry, per IBD MarketSmith chart analysis. The three-weeks-tight formation shows a buy point at 398.89, while aggressive investors could use a trendline-entry around 386.

MBD stock boasts a powerful relative strength line, reiterating that it is a stock market leader to watch. Remember, the RS line measures a stock’s price performance vs. the S&P 500 and is the blue line plotted on every IBD chart.

This AI stock has more. Strong fundamentals and technicals in recent quarters make MDB one of the best stocks to buy and watch in today’s stock market.

With the stock market in a confirmed uptrend, investors should be on the lookout for top stocks breaking out of basing patterns. Amid Wednesday’s stock market drop, IBD recommends exposure in stocks at 60% to 80% of your portfolio, according to IBD’s The Big Picture. 

Best Stocks To Buy And Watch

Three recent IBD 50 Stocks To Watch picks, which are among the best stocks to buy and watch.

Source: IBD Data As Of June 21

Follow Scott Lehtonen on Twitter at @IBD_SLehtonen for more on the best stocks to buy and watch and the stock market.

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Wednesday, June 21, 2023 12:29 PM | InvestorsObserver Analysts

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Mongodb Inc (MDB) is around the top of the Software – Infrastructure industry according to InvestorsObserver. MDB received an overall rating of 62, which means that it scores higher than 62 percent of all stocks. Mongodb Inc also achieved a score of 79 in the Software – Infrastructure industry, putting it above 79 percent of Software – Infrastructure stocks. Software – Infrastructure is ranked 57 out of the 148 industries.

MDB has an Overall Score of 62. Find out what this means to you and get the rest of the rankings on MDB!

What do These Ratings Mean?

Analyzing stocks can be hard. There are tons of numbers and ratios, and it can be hard to remember what they all mean and what counts as “good” for a given value. InvestorsObserver ranks stocks on eight different metrics. We percentile rank most of our scores to make it easy for investors to understand. A score of 62 means the stock is more attractive than 62 percent of stocks.

Our proprietary scoring system captures technical factors, fundamental analysis and the opinions of analysts on Wall Street. This makes InvestorsObserver’s overall rating a great way to get started, regardless of your investing style. Percentile-ranked scores are also easy to understand. A score of 100 is the top and a 0 is the bottom. There’s no need to try to remember what is “good” for a bunch of complicated ratios, just pay attention to which numbers are the highest.

What’s Happening With Mongodb Inc Stock Today?

Mongodb Inc (MDB) stock is trading at $371.25 as of 12:25 PM on Wednesday, Jun 21, a drop of -$8.53, or -2.25% from the previous closing price of $379.78. The stock has traded between $367.60 and $381.95 so far today. Volume today is low. So far 987,041 shares have traded compared to average volume of 2,185,091 shares.

Click Here to get the full Stock Report for Mongodb Inc stock.

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Posted on mongodb google news. Visit mongodb google news

Following a stellar first-quarter earnings performance, MongoDB Inc., the forerunner in the database platform domain, prepares for its global conference tour with MongoDB.local NYC this week.

I had a chance to chat with MongoDB Chief Executive Dev Ittycheria (pictured) and Andrew Davidson, its senior vice president of products, in advance of this week’s conference to get a glimpse of what the event will showcase. The conference is expected to offer a glimpse into the company’s latest advancements and future plans, centered on providing tools for developers to modernize, innovate and deliver the business outcomes that drive success — all on one developer data platform.

MongoDB’s continued success has been attributed to customer workload wins and broader product adoption, as well as the support it provides for developers. Ittycheria points to MongoDB’s groundbreaking approach in empowering developers to ride the wave of generative artificial intelligence, which has become a game-changer for startups and established enterprises alike. After all, at the core of AI is data, and simplifying working with data to build applications has been what MongoDB does well.

Born in the cloud, continuing to evolve with the next-gen cloud

MongoDB’s growth narrative transcends its technological advances. The company has proven deft at understanding the needs of its users, cultivating a developer-first ethos that emphasizes productivity and simplicity.

The database landscape has been a dynamic one, witnessing substantial shifts as industry heavyweights such as Oracle Corp. adapt their services to meet emerging demands. But MongoDB’s roots in document space and its emphasis on availability at scale provide a unique competitive edge, even as others rush to announce MongoDB application programming interfaces.

MongoDB’s growth is a testament to its commitment to innovation and its vision to cater to a diverse set of developers, workloads and regions. As the company gears up to address more use cases, its ascent in the tech world is poised to continue, potentially establishing MongoDB as a benchmark for innovation in the database industry.

Harnessing the power of constant data motion

The theme for this year’s MongoDB.local, which kicks off in NYC on Thursday, is “Behind the Build,” where the company will showcase the power of modern applications built on data which is in constant, ever-accelerating, and unpredictable motion. It underscores the company’s commitment to fostering a dynamic environment for developers amidst the AI revolution. As generative AI continues to reshape the landscape of software and data, MongoDB is at the forefront, helping its clientele leverage these groundbreaking opportunities.

Yet even amidst this technological transformation, Ittycheria insists on the centrality of human elements that drive digital change. “Developers are at the forefront of this digital revolution,” says Ittycheria. “Enterprises need to support them by providing the necessary tools to quickly and successfully innovate.” MongoDB is promising to do just that. 

Data developers on the rise: A developer-led data platform emerges

In the current era of cloud-native AI-driven digital advancements, executives and information technology experts have shifted their focus toward performance, reliability, openness and multicloud operability, valuing these factors over market share or footprint. The database market remains highly competitive, with nearly a thousand distinct systems vying for attention. However, it is crucial to recognize that the game’s dynamics are evolving, with applications taking center stage.

Over the years, ecosystems and their communities have made a significant shift from solely prioritizing the “database to run the company” to placing greater emphasis on developer-centric experiences, often referred to as “data developers.” This transition is particularly evident in cloud-native applications and cloud infrastructure-as-code, where agile applications increasingly influence database decisions.

For teams engaged in database development and data-focused developers, the primary value lies in the process of constructing a product and establishing the necessary infrastructure to support it. Once established, this framework can be leveraged by any application or AI-enabled system, solidifying its significance.

The ascent of MongoDB to the summit of the database market comes as no surprise, given its comprehensive range of capabilities, leadership in cloud-native services tailored to developers, seamless cloud integration, scalability and unwavering commitment to innovation. Prominent players in both the banking and auto manufacturing industries have expressed their admiration for MongoDB’s accomplishments.

As the database market undergoes rapid transformations in the next 48 months, we anticipate the emergence of new contenders and shifts in positions, with some companies rising while others fall. MongoDB’s success serves as a model for effectively adapting and flourishing in this evolving landscape.

Enabling the AI-driven future

As AI and GenAI promise to redefine the very concept of being a developer, Ittycheria underscores the need for the underlying database foundation to evolve in tandem.

“Companies of all sizes and across verticals, like GE Healthcare and Hugging Face, are leveraging MongoDB to experiment and adapt with generative AI,” said Ittycheria. “MongoDB’s document model enables these companies to quickly innovate to achieve their goals, which will only become more critical as the development landscape changes with AI’s influence.”

Addressing the rapid advancement of GenAI, MongoDB is creating AI-aided services to assist with crucial stages in the application modernization process. Its Relational Migrator service, for instance, streamlines the transformation from legacy relational to contemporary document-based data models, thereby enhancing operational efficiency.

The road ahead: MongoDB’s vision

With software playing an increasingly competitive role in business, MongoDB identifies AI as the catalyst that will drive development productivity to new heights. As Ittycheria points out, this transition will favor contemporary platforms offering a wide array of sophisticated capabilities, delivered with high performance and scalability.

As MongoDB embarks on its global tour with MongoDB.local, the company remains steadfast in its commitment to aiding developers and enterprises to navigate the fast-paced realm of modern data infrastructure. By doing so, it stands to enable these groups to keep pace with the rapid strides in software development.

Photo: MongoDB

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