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MongoDB Atlas now available on Azure OpenAI Service

New Microsoft Fabric Mirroring integration with MongoDB Atlas allows for near real-time data syncs

MongoDB Enterprise Advanced now available on Azure Marketplace for Azure Arc-enabled Kubernetes applications

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

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

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

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

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

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

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

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

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

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

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

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

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

Forward-looking Statements
This press release includes certain “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended, including statements concerning MongoDB’s deepened partnership with Microsoft. These forward-looking statements include, but are not limited to, plans, objectives, expectations and intentions and other statements contained in this press release that are not historical facts and statements identified by words such as “anticipate,” “believe,” “continue,” “could,” “estimate,” “expect,” “intend,” “may,” “plan,” “project,” “will,” “would” or the negative or plural of these words or similar expressions or variations. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations, strategies and prospects as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations or strategies will be attained or achieved. Furthermore, actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control including, without limitation: the effects of the ongoing military conflicts between Russia and Ukraine and Israel and Hamas on our business and future operating results; economic downturns and/or the effects of rising interest rates, inflation and volatility in the global economy and financial markets on our business and future operating results; our potential failure to meet publicly announced guidance or other expectations about our business and future operating results; our limited operating history; our history of losses; failure of our platform to satisfy customer demands; the effects of increased competition; our investments in new products and our ability to introduce new features, services or enhancements; our ability to effectively expand our sales and marketing organization; our ability to continue to build and maintain credibility with the developer community; our ability to add new customers or increase sales to our existing customers; our ability to maintain, protect, enforce and enhance our intellectual property; the effects of social, ethical and regulatory issues relating to the use of new and evolving technologies, such as artificial intelligence, in our offerings or partnerships; the growth and expansion of the market for database products and our ability to penetrate that market; our ability to integrate acquired businesses and technologies successfully or achieve the expected benefits of such acquisitions; our ability to maintain the security of our software and adequately address privacy concerns; our ability to manage our growth effectively and successfully recruit and retain additional highly-qualified personnel; and the price volatility of our common stock. These and other risks and uncertainties are more fully described in our filings with the Securities and Exchange Commission (“SEC”), including under the caption “Risk Factors” in our Annual Report on Form 10-Q for the quarter ended July 31, 2024, filed with the SEC on August 30, 2024, and other filings and reports that we may file from time to time with the SEC. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations or otherwise.

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For years, enterprise companies have been plagued by data silos separating transactional systems from analytical tools—a divide that has hampered AI applications, slowed real-time decision-making, and driven up costs with complex integrations. Today at its Ignite conference, Microsoft announced a major step toward breaking this cycle.

The tech giant revealed that Azure SQL, its flagship transactional database, is now integrated into Fabric, Microsoft’s unified data platform. This integration allows enterprises to combine real-time operational and other historical data into a single, AI-ready data later called OneLake. 

This announcement represents a critical evolution of Microsoft Fabric, its end-to-end data platform, which also includes new capabilities like real-time intelligence and the general availability of the OneLake catalog (see our full coverage of the Microsoft Ignite data announcements here). Together, these updates aim to address the growing demand for accessible, high-quality data in enterprise AI workflows.

Until now, companies have struggled to connect disparate data systems, relying on patchwork solutions to support AI applications. The urgency has only increased with the rise of AI agents—software tools capable of performing complex reasoning autonomously. These agents require instantaneous access to live and historical data to function effectively, a demand Microsoft aims to meet with Fabric.

And with AI agents becoming one one of the hottest trends for enterprise companies next year, Microsoft is pushing to lead here. See our separate coverage about how Microsoft is ahead in this race, and no one else is close.

The integration of Azure SQL is just the beginning of this integration of transactional data. Microsoft plans to extend support to other key transactional databases, including Cosmos DB, its NoSQL document database widely used in AI applications, and PostgreSQL, the popular open-source relational database. While timelines for these integrations remain unspecified, this marks a significant milestone in Microsoft’s effort to create a truly unified data platform. 

Microsoft also said it plans to integrate with popular open source transactional databases, including MongoDB, and Cassandra, but it’s unlikely Microsoft will prioritize integration with competing proprietary transactional databases like Couchbase and Google’s Bigtable.

The power of unified data integration

Arun Ulag, corporate vice president of Azure Data, emphasized in an interview that integrating transactional databases like Cosmos DB into Fabric is critical for enabling next-generation AI applications. For example, OpenAI’s ChatGPT—the fastest-growing consumer AI product in history—relies on Cosmos DB to power its conversations, context, and memory, managing billions of transactions daily.

As AI agents evolve to handle complex tasks like e-commerce transactions, the demand for real-time access to transactional databases will only grow. These agents rely on advanced techniques like vector search, which retrieves data based on semantic meaning rather than exact matches, to answer user queries effectively—such as recommending a specific book.

“You don’t have the time to…go run your RAG model somewhere else,” Ulag said, referencing retrieval-augmented generation models that combine real-time and historical data. “It has to be just built into the database itself.”

By unifying operational and analytical capabilities, Fabric allows businesses to build AI applications that seamlessly leverage live transactional data, structured analytics, and unstructured insights.

Key advancements include:

• Real-time intelligence: Built-in vector search and retrieval-augmented generation (RAG) capabilities simplify AI application development, reducing latency and improving accuracy.
• Unified data governance: OneLake provides a centralized, multi-cloud data layer that ensures interoperability, compliance, and easier collaboration.
• Seamless code generation: Copilot in Fabric can automatically translate natural language queries into SQL, allowing developers to get inline code suggestions,  real-time explanations and fixes.

AI Skills: simplifying AI agent app development

One of the most dynamic announcements in Fabric is the introduction of AI Skills, a capability that enables enterprises to interact with any data – wherever it resides –  through natural language. They connect to Copilot Studio, so you can build AI agents that easily query this data across multiple systems, from transactional logs to semantic models.

Ulag said that if he had to pick one announcement that excites him the most, it would be AI Skills. With AI Skills, business users can simply point to any dataset — be it from any cloud, structured, or unstructured – and begin asking questions about that data, whether through natural language, SQL queries, Power BI business definitions, or real-time intelligence engines, he said. 

For example, a user could use AI Skills to identify trends in sales data stored across multiple systems or to generate instant insights from IoT telemetry logs. By bridging the gap between business users and technical systems, AI Skills simplifies the development of AI agents and democratizes data access across organizations.

As of today, AI Skills can connect with lakehouse and data warehouse tables, mirrored DB and shortcut data, and now semantic models and Eventhouse KQL databases. Support for unstructured data is “coming soon,” the company said. 

Differentiation in a crowded market

Microsoft faces fierce competition from players like Databricks and Snowflake on the data platform front, as well as AWS and Google Cloud in the broader cloud ecosystem—all of which are working on integrating transactional and analytical databases. However, Microsoft’s approach with Fabric is beginning to carve out a unique position.

By leveraging a unified SaaS model, seamless Azure ecosystem integration, and a commitment to open data formats, Microsoft eliminates many of the data complexities that have plagued enterprise data systems. Additionally, tools like Copilot Studio for building AI agents and Fabric’s deep integration across multi-cloud environments give it an edge (see my separate analysis [LINK] of Microsoft’s positioning around AI agents, which also appears to be industry-leading).

Microsoft’s ability to embed AI capabilities directly into its unified data environment “could provide a better experience for developers and data scientists,” said Robert Kramer, vice president at research firm Moor Insights, underscoring how Fabric’s design simplifies workflows and accelerates AI-driven innovation.

Key differentiators include:

• Unified SaaS model: Fabric eliminates the need to manage multiple services, offering enterprises a single, cohesive platform that reduces complexity and operational overhead.
• Multi-cloud support: Unlike some competitors, Fabric integrates with AWS, Google Cloud, and on-premises systems, enabling organizations to work seamlessly across diverse data environments.
• AI-optimized workflows: Built-in support for vector similarity search and retrieval-augmented generation (RAG) streamlines the creation of intelligent applications, cutting development time and improving performance.

Microsoft’s strategy to unify and simplify the enterprise data stack not only meets the demands of today’s AI-centric workloads but also sets a high bar for competitors in the rapidly evolving data platform market.

The road ahead: where Fabric fits in the AI ecosystem

The integration of transactional databases into Fabric marks a significant milestone, but it also reflects a broader shift across the enterprise data landscape: the race toward seamless interoperability. With AI agents becoming a cornerstone of enterprise strategy, the ability to unify disparate systems into a cohesive architecture is no longer optional—it’s essential.

However, Arun Ulag, corporate vice president of Azure Data, acknowledged the challenges that come with operating at Microsoft’s scale. While the company has taken major strides with Fabric, the fast-moving nature of the industry demands constant innovation and adaptability.

“A lot of these patterns are new,” Ulag explained, describing the challenges of designing for a diverse set of use cases across industries. “Some of these patterns will work. Some of them will not, and we’ll only know as customers try them at scale…The way it’s used in automotive may be very, very different from the way it’s used in healthcare,” he added, emphasizing the role of external forces like government regulations in shaping future development.

As Microsoft continues to refine Fabric, the company is positioning itself as a leader in the shift to unified, AI-ready data architectures. But with competitors also racing to meet the demands of enterprise AI, the journey ahead will require constant evolution, rapid learning, and a focus on delivering value at scale.

For more insights into the announcements and Arun Ulag’s perspective, watch our full video interview above.

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At QCon SF 2024, David Berg and Romain Cledat gave a talk about how Netflix uses Metaflow, an open-source framework, to support a variety of ML systems. The pair gave an overview of Metaflow’s design principles and illustrated several of Netflix’s use cases, including media processing, content demand modeling, and meta-models for explaining models.

Berg and Cledat, both Senior Software Engineers at Netflix, began with several design principles for Metaflow. The goal is to accelerate ML model development in Python by minimizing the developer’s cognitive load. The Metaflow team identified several effects that they wished to minimize: the House of Cards effect, where the underlying layers of a framework are “shaky” instead of a solid foundation; the Puzzle effect, where the composable modules have unique or unintuitive interfaces; and the Waterbed effect, where the system has a fixed amount of complexity that “pops up” in one spot when pushed down elsewhere.

Cledat gave an overview of the project’s history. Metaflow began in 2017 as an internal project at Netflix; in 2019, it was open-sourced, although Netflix continued to maintain its own internal version. In 2021, a group of Netflix ex-employees created a startup, Outerbounds, to maintain and support the open-source project. The same year, Netflix’s internal version and the open-source version were refactored to create a shared “core.”

The key idea of Metaflow is to express computation as a directed acyclic graph (DAG) of steps. Everything is expressed using Python code that “any Python developer would be comfortable coding” instead of using a DSL. The DAG can be executed locally on a developer’s machine or in a production cluster without modification. Each execution of the low, or “run,” can be tagged and persisted for collaboration.

Berg gave several examples of the different ML tasks that Netflix developers have tackled with Metaflow. Content demand modeling tries to predict user demand for a video “across the entire life cycle of the content.” This actually involves multiple data sources and models, and leverages Metaflow’s ability to orchestrate among multiple flow DAGs; in particular, it uses a feature where flows can signal other flows, for example when a flow completes.

Another use case is meta-modeling, which trains a model to explain the results of other models. This relies on Metaflow’s ability to support reproducible environments. Metaflow packages all the dependencies needed to run a flow so that developers can perform repeatable experiments. When training a meta-model, this may require loading several environments, as the meta-model may have different dependencies from the explained model.  

The presenters concluded their talk by answering questions from the audience. Track host Thomas Betts asked the first question. He noted that the code for a flow DAG can have annotations specifying the size of the compute cluster to execute it, but the hosts said the same DAG could also be executed on a single machine, and he wondered if those were ignored in that case. The hosts confirmed that this was the case.

Another attendee asked about how these cluster specifications were tuned, especially in the case of over-provisioning. Berg said that the framework can surface “hints” about resource use. He also said there was some research being done on auto-tuning the resources, but not everything could be abstracted away.

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This post was co-authored with Nissan Modi, Staff Software Engineer at Coinbase.

In this post, we discuss how Coinbase migrated their user clustering system to Amazon Neptune Database, enabling them to solve complex and interconnected data challenges at scale.

Coinbase’s mission is to expand global economic freedom by building an open financial system that uses cryptocurrency to increase access to the global economy and aims to achieve this by providing a secure online platform for buying, selling, transferring, and storing cryptocurrency. Coinbase handles vast amounts of data related to transactions, user behavior, and market trends. To efficiently manage and analyze this data, the company employs various data science techniques, including clustering. This method of data organization and analysis is particularly useful for a platform like Coinbase, which needs to understand patterns in user behavior, detect anomalies, and optimize its services. Clustering groups similar data points together based on their features by sorting entities into sets, or clusters, where items in the same cluster share key traits. These traits could be things like age, habits, or interests. The main idea is that data points in one cluster are alike, and those in different clusters are not. This method helps find natural patterns in data, making it straightforward to understand and use large datasets.

Challenge

The platform organization within Coinbase has been responsible for managing a clustering system that has been in place since 2015. Since the original datastore for the clustering system was not graph-based, clusters needed to be precomputed and stored in a NoSQL database. At any given time, the system would store approximately 150 million clusters, some of which contain over 50,000 nodes. This made it challenging to keep clusters up-to-date as user attributes changed in real time, as whenever a user attribute was updated, the system would need to re-calculate clusters.

Pre-calculating clusters became even more challenging as Coinbase expanded their product offerings and their customer base grew. Additionally, logic for grouping users became increasingly complex over time. This necessitated a high number of database updates to support each specific use case. As a result, the system began to experience performance degradation, higher storage costs, and difficulties in supporting different read patterns. This growing inefficiency made it clear that the existing approach was no longer sustainable.

The scale of the system was significant, with around 150 million clusters, some of which included over 50,000 nodes. This massive scale added to the complexity and challenges faced by the team, especially as the system’s write-heavy nature became more pronounced over time.

Initially, the system relied on a NoSQL database to store the precomputed clusters. Precomputing results can be advantageous in systems that are primarily read-heavy, because it avoids the need to repeat the same computations during read operations. However, the clustering system at Coinbase was characterized by a write-heavy workload with frequent updates, making precomputing less optimal as the system evolved. This led to performance issues, increased storage costs, and challenges in accommodating the complex and dynamic relationships within the data. Consequently, the team needed to reevaluate the database choice to better scale the system and meet the demands of Coinbase’s growing product ecosystem.

Solution overview

Graph databases are designed to manage complex, interconnected data structures, allowing representation and querying of relationships between entities. Because Coinbase’s use case is write-heavy and the data is highly connected, they needed a solution that can handle frequent updates to both data and relationships. Instead of relying on precomputed joins, a graph database can perform real-time traversals of connections and relationships, leading to improved query performance and reduced storage costs as compared to using a non-graph datastore to solve the same problem. Adopting a graph database for Coinbase’s clustering system represents a strategic shift towards a more flexible and scalable data architecture, which is key as Coinbase grows not only their customer base but the increasing complexity of its customer relationships.

Graph databases are purpose-built for storing and efficiently querying highly connected data. The following are key indicators of whether a graph is well-suited for a particular use case:

• Is the dataset highly connected, full of many-to-many relationships?
• Do your intended queries for the graph require understanding relationships between data points?

Coinbase’s clustering use case aims to group entities according to attributes shared across the entities. Therefore, when clustering is complete, entities within a single cluster will be more closely associated with one another, compared to other entities that are in different clusters.

You can represent the dataset using a series of relational tables, for example, a user_attributes table where each row represents a user, and each column represents a different attribute, as illustrated in the following figure.

You can also model it as a graph, as shown in the following figure.

The benefit of modeling this data in a graph format is that you can efficiently find groups and patterns based on mutual connections. For example, given the following sample graph of entities (ent#) and attributes (attr#), you might want to find the collection of entities that share certain attributes but not others. A shared attribute is defined as an attribute node that is connected to two or more entities.

More specifically, let’s say you want to find a collection of entities that meet the following requirements:

1. All entities in the collection share at least attributes attr1, attr2, attr3
2. All entities in the collection do not share the attributes attr4, attr5
3. All entities in the collection share any attribute with at least one other entity that shares a specific attribute with at least two other entities

And your graph contains the follow entities and relationships:

With this example, only ent1 and ent2 would be returned, since ent1 and ent2 both connect to attr1, attr2, and attr3, meeting the first requirement. ent1 is connected to attr5, and ent2 is connected to attr4, but both attributes are not shared attributes since they don’t connect to more than one node each – thus meeting the second requirement. And both ent1 and ent2 share attr1, which is also shared by ent4, ent5, and ent6 – thus meeting the third requirement.

To answer this question efficiently, you need to know not only how entities are connected to the attributes they are associated with, but a way to traverse those connections across multiple levels. Although this question can be answered with a relational database, for query performance to be efficient, you should know all your query patterns upfront, so table joins can be pre-calculated and stored. But by keeping this data in a graph, it not only lets you recalculate queries in real time as the data changes (with no need to pre-calculate joins), but also gives additional flexibility for different query patterns to be written as needed.

Neptune Database addresses several technical challenges faced by large-scale graph database implementations. Because it is fully managed, Coinbase can eliminate significant operational overhead while providing flexibility in data modeling and querying. Neptune Database doesn’t enforce a schema, so adding new properties, node types, and edge types to answer evolving business use cases doesn’t require the graph to be rebuilt or reloaded. Additionally, Neptune Database is capable of querying billions of relationships with millisecond latency, allowing Coinbase to scale this system with their growing customer base.

In Coinbase’s solution, the data ingestor service writes to Neptune Database transactionally. Multiple events are batched into a single graph query, which is run as a single transaction within Neptune Database. This keeps the graph up to date in near real time with the incoming events. Coinbase micro batches multiple changes into the same transaction, and is therefore able to achieve their desired ingestion rates through 20 writes per second, where each write takes the place of many writes (depending on how many users’ clusters were being updated) in the old NoSQL system.

The following diagram illustrates the architecture for Coinbase’s enhanced clustering solution.

Services communicate with Neptune Database through an API server, where different use cases are mapped to different queries. For example, when invoked, the get-related-users API takes an attribute name and attribute value and runs the following Gremlin query to retrieve information about a given user:

g.V().HasLabel(attributeName).
Has("attribute_value", attribute_value).
In().
HasLabel("user").
ElementMap().
ToList()

One feature that Coinbase was unable to implement with the legacy architecture was a UI for stakeholders that visualized the graph. Even though clusters could be pre-calculated, the results themselves were still stored in a tabular format. Now that the data is in a graph format, visualizations of the entities and relationships can be generated with ease. Providing a visualization allows stakeholders to see a different perspective of the data, and makes it straightforward to visually identify the common attributes used for generating clusters—enabling stakeholders to take the proper actions when linkages between common attributes are found. The following is an example visualization from Coinbase’s enhanced clustering system.

Representing graph data and querying

Neptune Database supports two open frameworks for representing graph data: the Resource Description Framework (RDF) and the Labeled Property Graph framework (LPG). You can represent graph use cases using either framework, but depending on the types of queries that you want to run, it can be more efficient to represent the graph using one framework or the other.

The types of queries that are commonly used for clustering in the Coinbase system require recursive traversals with filtering on edge properties and other traversals. Therefore, representing this use case with the LPG framework was a good fit because it’s simpler to write complex pathfinding queries using the LPG query languages openCypher and/or Gremlin.

For example, one benefit of using LPG and the Gremlin query language is the presence of support for a query results cache. Pathfinding queries that are used to generate clusters can have many results, and with the query results cache, you can natively paginate the results for improved overall performance. Additionally, to generate a visualization of subgraphs, you need to return a path, which is the sequence of nodes and edges that were traversed from your starting points to your ending points. You can use the Gremlin path() step to return this information, making it less complicated to generate paths for recursive traversals with condition-based ending conditions, such as finding the path between a given pair of nodes.

Benefits and results

Coinbase’s solution with Neptune Database yielded the following benefits:

• New use cases – The new solution facilitates the discovery of related users across various product use cases without the need for hard-coded aggregation logic. Additionally, attribute lists can be passed to the get-related-users API to instantly generate a list of related users. This capability aids in debugging and allows for the efficient identification of similar users for administrative purposes.
• Performance efficiency – 99% of the queries that Coinbase runs achieves a latency of less than 80 milliseconds for the platform team while running on a smaller, cost-optimized instance, without a caching layer. This instance can scale to 300 transactions per second (TPS). These transactions are more meaningful than TPS figures on the previous NoSQL system, due to batching the writes and updating all of the users’ attributes across multiple clusters. Because computing multiple joins was required, the NoSQL system thus needed multiple queries to find the same results that a single graph query now finds.
• Reliability – Because updates are now limited to a single node, the number of database operations has been drastically reduced. This optimization has effectively eliminated the race conditions that were prevalent in the previous system. Additionally, Coinbase can take advantage of automatic hourly backups through Neptune Database.
• Cost optimization – Coinbase was able to achieve 30% savings in storage costs by eliminating redundant information in the old system and computing the clusters at runtime using Neptune Database.
• Visualizations – New visualization capabilities provided through a custom-built UI help business owners and teams across the company understand fraud and risk situations and allow new ways to show useful data. This has already significantly reduced analysis time.

Conclusion

Coinbase’s journey with Neptune Database showcases the power of graph databases in solving complex, interconnected data challenges at scale. By migrating their user clustering system to Neptune Database, Coinbase has not only overcome the limitations of their previous NoSQL solution but also unlocked new capabilities and efficiencies. The fully managed nature of Neptune Database has allowed Coinbase to focus on innovation rather than operational overhead. The platform’s ability to handle billions of relationships with millisecond latency enables Coinbase’s future growth and evolving business needs.

Now that the data is in a graph format on Neptune Database, it’s less complicated for Coinbase to add more user attributes without increasing the complexity of managing the relationship. In the future, they plan to ingest more of these attributes and gain richer insights. This will lead to even greater benefits and new use cases.

The graph format also makes it straightforward to extend analyses to experiment with new graph-based techniques. Neptune Analytics is a memory-optimized graph database that helps you find insights faster by analyzing graph datasets with built-in algorithms. Graph algorithms can be used to identify outlier patterns and structures within the dataset, providing insights on new behaviors to investigate. A Neptune Analytics graph can be created directly from a Neptune Database cluster, making it effortless to run graph algorithms without having to manage additional extract, transform, and load (ETL) pipelines and infrastructure.

Get started today with Fraud Graphs on AWS powered by Neptune. You can use sample notebooks such as those in the following GitHub repo to quickly test in your own environment.

About the Authors

Joshua Smithis a Senior Solutions Architect working with FinTech customers at AWS. He is passionate about solving high-scale distributed systems challenges and helping our fastest scaling financial services customers build secure, reliable, and cost-effective solutions. He has a background in security and systems engineering, working with early startups, large enterprises, and federal agencies.

Melissa Kwok is a Senior Neptune Specialist Solutions Architect at AWS, where she helps customers of all sizes and verticals build cloud solutions according to best practices. When she’s not at her desk you can find her in the kitchen experimenting with new recipes or reading a cookbook.

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The most recent trading session ended with MongoDB (MDB) standing at $284.43, reflecting a +1.67% shift from the previouse trading day’s closing. The stock’s performance was ahead of the S&P 500’s daily gain of 0.39%. Meanwhile, the Dow lost 0.13%, and the Nasdaq, a tech-heavy index, added 0.6%.

Heading into today, shares of the database platform had gained 1.54% over the past month, outpacing the Computer and Technology sector’s gain of 0.59% and the S&P 500’s gain of 1.06% in that time.

Investors will be eagerly watching for the performance of MongoDB in its upcoming earnings disclosure. The company is predicted to post an EPS of $0.69, indicating a 28.13% decline compared to the equivalent quarter last year. Meanwhile, our latest consensus estimate is calling for revenue of $495.23 million, up 14.39% from the prior-year quarter.

Regarding the entire year, the Zacks Consensus Estimates forecast earnings of $2.43 per share and revenue of $1.93 billion, indicating changes of -27.03% and +14.48%, respectively, compared to the previous year.

Furthermore, it would be beneficial for investors to monitor any recent shifts in analyst projections for MongoDB. These latest adjustments often mirror the shifting dynamics of short-term business patterns. Hence, positive alterations in estimates signify analyst optimism regarding the company’s business and profitability.

Empirical research indicates that these revisions in estimates have a direct correlation with impending stock price performance. We developed the Zacks Rank to capitalize on this phenomenon. Our system takes these estimate changes into account and delivers a clear, actionable rating model.

The Zacks Rank system ranges from #1 (Strong Buy) to #5 (Strong Sell). It has a remarkable, outside-audited track record of success, with #1 stocks delivering an average annual return of +25% since 1988. The Zacks Consensus EPS estimate has moved 0.76% higher within the past month. Right now, MongoDB possesses a Zacks Rank of #3 (Hold).

With respect to valuation, MongoDB is currently being traded at a Forward P/E ratio of 115.24. This represents a premium compared to its industry’s average Forward P/E of 30.95.

We can additionally observe that MDB currently boasts a PEG ratio of 11.08. Comparable to the widely accepted P/E ratio, the PEG ratio also accounts for the company’s projected earnings growth. MDB’s industry had an average PEG ratio of 2.45 as of yesterday’s close.

The Internet – Software industry is part of the Computer and Technology sector. This industry, currently bearing a Zacks Industry Rank of 33, finds itself in the top 14% echelons of all 250+ industries.

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Amazon DynamoDB is a serverless, NoSQL, fully managed database with single-digit millisecond performance at any scale. Today, we are announcing the general availability of attribute-based access control (ABAC) support for tables and indexes in all AWS Commercial Regions and the AWS GovCloud (US) Regions. ABAC is an authorization strategy that lets you define access permissions based on tags attached to users, roles, and AWS resources. Using ABAC with DynamoDB helps you simplify permission management with your tables and indexes as your applications and organizations scale.

ABAC uses tag-based conditions in your AWS Identity and Access Management (IAM) policies or other policies to allow or deny specific actions on your tables or indexes when IAM principals’ tags match the tags for the tables. Using tag-based conditions, you can also set more granular access permissions based on your organizational structures. ABAC automatically applies your tag-based permissions to new employees and changing resource structures, without rewriting policies as organizations grow.

There is no additional cost to use ABAC. You can get started with ABAC using the AWS Management Console, AWS API, AWS CLI, AWS SDK, or AWS CloudFormation. Learn more at Using attribute-based access control with DynamoDB.

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Gordon Michael Lawrence, serving as COO and CFO of MongoDB, sold 5,000 shares of Class A Common Stock on November 14, 2024. The transactions were executed at weighted average prices ranging from $291.5 to $301.0 per share, resulting in a total sale amount of $1,480,541. Following these transactions, Lawrence directly owns 80,307 shares and indirectly owns 4,000 shares through family members. All transactions were reported as direct ownership and were conducted under a Rule 10b5-1 trading plan.

SEC Filing: MongoDB, Inc. [ MDB ] – Form 4 – Nov. 18, 2024

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NEW YORK, Nov. 18, 2024 /PRNewswire/ — MongoDB, Inc. (NASDAQ: MDB) today announced it will report its third quarter fiscal year 2025 financial results for the three months ended October 31, 2024, after the U.S. financial markets close on Monday, December 9, 2024.

In conjunction with this announcement, MongoDB will host a conference call on Monday, December 9, 2024, at 5:00 p.m. (Eastern Time) to discuss the Company’s financial results and business outlook. A live webcast of the call will be available on the “Investor Relations” page of the Company’s website at http://investors.mongodb.com. To access the call by phone, please go to this link (registration link), and you will be provided with dial in details. To avoid delays, we encourage participants to dial into the conference call fifteen minutes ahead of the scheduled start time. A replay of the webcast will also be available for a limited time at http://investors.mongodb.com.

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

Investor Relations
Brian Denyeau
ICR for MongoDB
646-277-1251
ir@mongodb.com

Media Relations
MongoDB
press@mongodb.com

View original content to download multimedia:https://www.prnewswire.com/news-releases/mongodb-inc-announces-date-of-third-quarter-fiscal-2025-earnings-call-302309005.html

SOURCE MongoDB, Inc.

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Posted on mongodb google news. Visit mongodb google news

Gordon Michael Lawrence, the Chief Operating Officer and Chief Financial Officer of MongoDB , Inc. (NASDAQ:MDB), reported a series of transactions involving the company’s Class A Common Stock. On November 14, Lawrence executed a significant sale of shares amounting to approximately $1.48 million. The shares were sold at prices ranging from $291.50 to $301.00 per share.

In addition to the sales, Lawrence also acquired 5,000 shares through the exercise of stock options at a price of $6.50 per share. This acquisition was executed under a pre-arranged trading plan, as noted in the filing.

Following these transactions, Lawrence holds a total of 80,307 shares directly. The transactions were part of a Rule 10b5-1 trading plan, which allows company insiders to sell a predetermined number of shares at a predetermined time.

In other recent news, MongoDB, Inc. has announced the full redemption of its 0.25% Convertible Senior Notes due in 2026, with a total principal amount of $1,149,972,000. The company also reported a 13% year-over-year revenue increase in the second quarter, amounting to $478 million, mainly due to the success of its Atlas (NYSE:ATCO) and Enterprise Advanced offerings. This has led to the addition of over 1,500 new customers, bringing the total customer base to over 50,700.

Analysts from DA Davidson, Piper Sandler, and KeyBanc Capital Markets have raised their price targets for MongoDB. Additionally, Oppenheimer has increased its price target while maintaining an Outperform rating. These adjustments reflect MongoDB’s strong performance and the belief in its continued growth.

Looking ahead, MongoDB’s management anticipates third-quarter revenue to range between $493 million and $497 million. The full fiscal year 2025 revenue is projected to be between $1.92 billion and $1.93 billion, based on the company’s recent performance and analyst expectations. These recent developments underscore the confidence in MongoDB’s potential and its capacity to maintain a positive growth trajectory.

MongoDB’s recent insider transactions occur against a backdrop of mixed financial indicators. According to InvestingPro data, the company boasts a market capitalization of $21.03 billion, reflecting its significant presence in the database software market. Despite not being profitable over the last twelve months, with an adjusted operating income of -$285.81 million, MongoDB has shown strong revenue growth of 22.37% in the last twelve months, reaching $1.82 billion.

InvestingPro Tips highlight that MongoDB holds more cash than debt on its balance sheet, suggesting a solid financial position. This liquidity strength is further emphasized by the fact that the company’s liquid assets exceed short-term obligations. These factors may provide context for the insider’s decision to exercise options and sell shares, as they indicate a company with a strong financial foundation despite current profitability challenges.

Analysts appear optimistic about MongoDB’s future, with 22 analysts revising their earnings upwards for the upcoming period. An InvestingPro Tip also notes that net income is expected to grow this year, potentially signaling a turn towards profitability. This positive outlook might explain why the stock trades at a high revenue valuation multiple and a high Price / Book ratio of 15.41, as investors may be pricing in future growth expectations.

It’s worth noting that MongoDB does not pay a dividend to shareholders, which is common for high-growth technology companies reinvesting in their operations. The stock’s performance has been mixed, with a 11.44% return over the past three months but a -28.73% return over the past year, reflecting the volatility often associated with tech stocks.

For investors seeking a more comprehensive analysis, InvestingPro offers 11 additional tips for MongoDB, providing a deeper understanding of the company’s financial health and market position.

This article was generated with the support of AI and reviewed by an editor. For more information see our T&C.

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

Posted on mongodb google news. Visit mongodb google news

NEW YORK, Nov. 18, 2024 /PRNewswire/ — MongoDB, Inc. (NASDAQ: MDB) today announced it will report its third quarter fiscal year 2025 financial results for the three months ended October 31, 2024, after the U.S. financial markets close on Monday, December 9, 2024.

In conjunction with this announcement, MongoDB will host a conference call on Monday, December 9, 2024, at 5:00 p.m. (Eastern Time) to discuss the Company’s financial results and business outlook. A live webcast of the call will be available on the “Investor Relations” page of the Company’s website at http://investors.mongodb.com. To access the call by phone, please go to this link (registration link), and you will be provided with dial in details. To avoid delays, we encourage participants to dial into the conference call fifteen minutes ahead of the scheduled start time. A replay of the webcast will also be available for a limited time at http://investors.mongodb.com.

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

Investor Relations
Brian Denyeau
ICR for MongoDB
646-277-1251
ir@mongodb.com

Media Relations
MongoDB
press@mongodb.com

View original content to download multimedia:https://www.prnewswire.com/news-releases/mongodb-inc-announces-date-of-third-quarter-fiscal-2025-earnings-call-302309005.html

SOURCE MongoDB, Inc.

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