MongoDB Atlas in AI Growth: Cloud Platform Momentum and Risks

MongoDB’s cloud-first database has moved from developer favorite to a central piece of the modern AI stack, and recent results plus partner momentum have reignited investor interest — but the runway is not without fresh execution risks and competitive pressures.

Background / Overview​

MongoDB (MDB) builds and sells a document-oriented database and a cloud-managed service called MongoDB Atlas that packages the database as a fully managed, consumption-priced platform. The company’s pitch rests on three straightforward claims: a flexible schema that avoids the upfront rigidity of relational databases, a distributed architecture built to scale horizontally for high throughput and low latency, and an attractive cloud economics model when customers prefer to pay for managed services rather than run infrastructure themselves. Those features are at the heart of the bull case recently highlighted in analyst writeups and investment commentary.
In early December 2025, MongoDB reported a strong quarter that accelerated Atlas growth to roughly 30% year‑over‑year, beat consensus on both revenue and EPS, and led to an upward revision of the company’s guidance. Market reaction was immediate and sharp: shares jumped after the print, reflecting the renewed narrative that MongoDB is capturing AI-driven consumption and expanding its enterprise footprint. PR filings and independent analyst coverage corroborate the core financials and the Atlas growth rate cited by several market writeups. This feature unpacks the numbers, the Microsoft partnership angle, the technical strengths that make MongoDB attractive for modern applications, the near-term market reaction, and the critical risks that investors and IT buyers should weigh before declaring the stock a clear-cut “buy.”

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Why Atlas Matters: Product and Platform Dynamics​

What Atlas is and why developers like it​

At its core, MongoDB Atlas is a fully managed cloud database service that abstracts away server provisioning, patching, replication, backups, and cluster scaling, while exposing the MongoDB document model (BSON/JSON‑like documents) and a developer-friendly query surface. Atlas lets teams:

• Launch production-ready clusters on major clouds (AWS, Azure, GCP) in minutes.
• Scale storage and compute independently and add read replicas or sharded clusters to handle larger workloads.
• Pay via consumption or subscription models rather than large upfront infrastructure spend.

The managed service model converts a traditionally capex-heavy infrastructure purchase into an operating expense consumption story — attractive for product teams and cloud-first companies. MongoDB itself and multiple market summaries highlight Atlas as the principal growth engine and the supplier of predictable, recurring revenue.

The technical advantages: flexible schema, horizontal scale, and developer velocity​

MongoDB’s document model is built for flexibility: developers store related data together in documents rather than forcing relationships into rigid tables and joins. This reduces schema migrations and speeds iteration — important for product teams shipping features rapidly.
Key technical attributes:

• Flexible schema (document model): Documents can evolve over time; new fields don’t require coordinated migrations across all records.
• Horizontal scaling (sharding): Atlas supports sharded clusters, allowing workloads to scale by adding nodes and distributing data across them.
• Developer ergonomics: Rich drivers (including .NET, Java, Node.js, Python) and integrated tools for change streams, search, and analytics lower the friction to adopt MongoDB for modern applications.

That architecture is why firms building high‑velocity apps — real‑time analytics, personalization, e‑commerce, or customer‑facing AI services — frequently pick Atlas as data infrastructure. Windows and Azure-centric shops benefit from native integrations that reduce operational friction.

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The Numbers: Q3 Strength, Atlas Acceleration, and Guidance Lift​

What MongoDB reported (verified)​

MongoDB announced third-quarter fiscal 2026 results showing:

• Total revenue: $628.3 million, up ~19% year‑over‑year.
• Atlas (cloud) revenue growth: ~30% year‑over‑year, representing approximately 75% of total Q3 revenue.
• Non‑GAAP EPS: $1.32, a double‑digit increase and a significant beat versus consensus.
• Customer adds and scale: The company reported more than 62,500 customers as of October 31, 2025.

These figures were released in MongoDB’s official earnings release and were reported and analyzed by multiple independent outlets. The Atlas growth acceleration figure and the revenue/EPS beats are the core pillars of the post‑earnings bullish narrative.

Cross‑checks and analyst confirmation​

Independent coverage (Zacks, press recaps) confirms the EPS and revenue beats and notes that MongoDB surpassed analyst estimates by a wide margin on EPS (Zacks reported a surprise in the range of two‑thirds higher than consensus for that quarter). That corroboration from a sell‑side/aggregator source and the company’s own release gives confidence that the headline numbers are accurate.

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Why Microsoft Matters: Partnership and GTM Leverage​

The Microsoft relationship — product and go‑to‑market implications​

MongoDB has deepened its partnership with Microsoft, particularly around Atlas on Azure and integrations that help customers combine Atlas storage/serving with Azure’s AI and security tooling. MongoDB’s corporate blog and event posts document joint engineering work and co‑sell programs that make Atlas more discoverable to Azure customers. Microsoft-centric enterprises often value a supported, co‑engineered solution because it reduces procurement friction and simplifies procurement cycles. MongoDB publicly announced being recognized at Microsoft events and has described itself as a recipient of a Microsoft partner award for the United States in 2025. That recognition — whether framed as a co‑marketing accolade or a partner category award — supports the narrative that Microsoft sees Atlas as a strategic offering for customers building AI workloads on Azure. MongoDB’s own announcement highlights this point; independent enumeration of Microsoft’s full winner list across every category is sometimes partitioned across regional posts, so the company’s announcement is the primary direct source for the specific phrasing. Readers should note that the company’s blog post explicitly claims the 2025 United States Partner recognition and ties it to joint Azure integrations.

What the partnership unlocks​

• Easier adoption for Azure customers (market access).
• Joint sales and co‑developed product integrations (technical validation).
• A pipeline into Microsoft customers exploring AI + data solutions where Atlas can serve as the operational data layer.

The strategic importance here is less about an award plaque and more about distribution and technical interoperability with a hyperscaler that owns a major share of enterprise cloud spending.

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Market Reaction: The Post‑Earnings Pop and Technical Context​

Price and volume reaction​

Following the Q3 report, market coverage and trading recaps recorded sharp upside in MongoDB’s share price — reports cited intraday moves ranging from mid‑teens to low‑twenties percent in the immediate post‑earnings period. Multiple outlets recorded a strong jump with unusual volume, reflecting institutional and retail demand. This technical gap is widely reported in the financial press as evidence of investor enthusiasm.

Interpreting technical price behavior​

Earnings‑induced gap moves can mean one of two things:

1. The market is repricing forward growth expectations (durable).
2. The move is primarily a short‑term reaction to a single quarter’s positive surprise (transient).

Trading volume expanding multiple times its baseline during a gap is usually a positive confirming indicator that the move reflects broad participation, not just algorithmic re‑pricing. However, as several market writeups observed, the stock can trade sideways after the initial pop while investors digest whether the new guidance and Atlas acceleration imply sustained upside.

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The Bull Case: Why MDB Could Run Higher​

• AI adoption and Atlas consumption: Modern generative AI and retrieval applications consume large volumes of data: embeddings, context windows, and live telemetry. Atlas is positioned as the operational data layer for these workloads, which can create higher consumption and incremental revenue per customer.
• Strong unit economics with managed cloud: Moving customers onto a managed consumption model can increase lifetime value, recurring revenue, and visibility — all features that investors prize in SaaS/cloud names.
• Partner‑led distribution (Microsoft + hyperscalers): Deep integrations and co‑sell arrangements with cloud providers reduce friction and expose Atlas to enterprises that might otherwise default to cloud‑native managed database alternatives.
• Earnings outperformance track record: MongoDB has a history of beating consensus and revising guidance upward when momentum is real; Zacks and other analytics shops have highlighted recent positive estimate revisions and upgraded ratings.

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The Bear Case and Key Risks​

1) Cost of cloud delivery and AI inference​

Running AI workloads is expensive: inference, embeddings storage, and retrieval are compute‑intensive. If Atlas customers adopt heavy inference patterns or if Atlas is required to provide bundled inference services, incremental cloud delivery costs could compress gross margins unless MongoDB captures proportionate price increases.

2) Hyperscaler competition and native alternatives​

Hyperscalers (AWS, Microsoft, Google) increasingly offer managed services and database compatibility layers. Some cloud vendors are building or promoting services that replicate MongoDB‑style APIs on top of their own durable storage engines — creating a pricing and feature play that can erode adoption or throttle pricing power. Additionally, open‑source compatibility projects and PostgreSQL‑based document APIs are evolving; these could limit MongoDB’s moat on purely API compatibility grounds. Evidence of these trends appears in industry coverage and technical forums.

3) Execution: converting new logos into sustainable ARR and ARPU​

Marketing wins and pilot projects must convert into production consumption and multi‑year contracts. If net retention weakens or seat pricing fails to keep pace with compute consumption, top‑line growth could disappoint despite flashy initial adoption metrics.

4) Valuation sensitivity​

High‑growth software stocks often trade at premium multiples. If investors price growth into the equity and execution slips even modestly, the multiple can compress quickly, producing outsized downside. The market’s reaction to guidance nuances in other SaaS names post‑earnings shows how sensitive multiples are to forward visibility.

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What IT Teams (Especially Windows/Azure Shops) Should Know​

Practical strengths for architects​

• Integration with Azure services simplifies compliance, identity, and AI model hosting workflows for Windows and .NET shops.
• Driver support and tooling — mature drivers for .NET and first‑party integrations with Visual Studio and Azure DevOps — reduce engineering lift.
• Edge and hybrid patterns: MongoDB’s architecture supports on‑prem or edge deployments plus Atlas for central workloads, giving hybrid flexibility for regulated or latency‑sensitive deployments. These benefits are especially relevant for Windows ecosystem customers managing mixed workloads.

Deployment checklist for teams evaluating Atlas​

1. Map core data access patterns: read/write ratios, document sizes, and query complexity.
2. Model expected storage and inference costs if planning to integrate retrieval/inference workflows.
3. Test retention and index refresh characteristics for retrieval‑augmented generation (RAG) workloads.
4. Validate compliance posture (FedRAMP, SOC, customer-managed keys) if operating in regulated industries.
5. Pilot with representative production traffic to baseline unit economics before broad rollout.

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Valuation and Investment Considerations​

How to think about growth vs. profitability​

MongoDB’s improved margins on recent quarters suggest management is focused on sustainable, profitable growth rather than pure growth at all costs. That dynamic is attractive to investors concerned about capital allocation and cash generation.
However, the core question for long‑term investors is: Can Atlas sustain mid‑to‑high‑teens to twenties growth while expanding margins as AI workloads proliferate? That depends on Atlas’s ability to capture a larger share of customers’ AI data budgets and on MongoDB’s success in translating pilot projects into steady consumption.

A practical investor checklist​

• Monitor Atlas consumption growth and billings dynamics quarter to quarter.
• Track dollar‑based net retention rates and the cohort metrics that show whether customers are expanding usage.
• Watch gross margin trends as a proxy for cloud delivery cost discipline.
• Reconcile guidance and analyst revisions following each quarter — Zacks and other aggregators are already flagging upward estimate revisions for MDB.

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Taking Stock of the Microsoft Partner Claim (Caveat Emptor)​

MongoDB’s own communications (company blog and event posts) state that MongoDB was recognized as a Microsoft United States Partner of the Year for 2025 and highlight increased joint integrations at Microsoft Ignite 2025. That corporate announcement is a valid primary source for MongoDB’s framing of the relationship and the recognition it received. Readers and buyers should note, however, that partner award programs are multi‑category, regional, and numerous; independent confirmation from Microsoft’s consolidated award list is ideal when trying to reconcile the award category or scope. As of this review, MongoDB’s announcement is the clearest public declaration of the recognition; prospective investors or procurement teams should treat the award as evidence of partnership strength while corroborating the specific award category if it matters to contract evaluations.

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Balanced Bottom Line​

MongoDB combines a compelling developer experience and a cloud consumption model that positions Atlas as a natural platform for operational data used by modern apps and emerging AI workloads. The December quarter showed that Atlas momentum can accelerate, and MongoDB’s guidance lift confirms management’s confidence.
Key positives:

• Atlas momentum: ~30% growth in cloud revenue is a meaningful acceleration and aligns with the narrative that AI and data ingestion drive consumption.
• Partner distribution: Deeper Azure integrations and partner recognition bolster enterprise access and co‑sell potential.
• Earnings surprise track record: The company has demonstrated the ability to beat consensus and raise estimates, prompting some analysts to upgrade the stock to Zacks Rank #1 (Strong Buy) based on estimate revisions.

Key caveats:

• Cloud delivery costs and AI compute economics could pressure margins if not matched with pricing power.
• Hyperscaler and open‑source competition may blunt pricing and market share gains in the long term.
• Execution dependency: Winning pilots is necessary but not sufficient — durable ARR expansion and rising ARPU per large account are required to justify premium multiples.

For WindowsForum readers managing Windows/Azure platforms, MongoDB Atlas is an increasingly practical choice for workloads that prioritize developer speed, JSON document modeling, and cloud consumption economics. For investors, the recent quarter provides fresh evidence of product-market fit in AI‑driven scenarios, but the stock’s future upside depends on sustained Atlas consumption growth, disciplined cloud delivery margins, and the company’s ability to defend its API and performance moat as hyperscalers and open‑source projects aim to narrow the gap.

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Final recommendations for different audiences​

• For enterprise architects (Azure/Windows shops): Evaluate Atlas in a controlled pilot for applications that benefit from flexible schema and real‑time ingestion. Model cloud delivery costs against expected query and inference patterns before broad rollout. Use the native Azure integrations to reduce management overhead.
• For long‑term investors: Treat the company as a growth at a premium idea. Monitor Atlas consumption growth, gross margin trends, and net retention; confirm that guidance lifts convert into durable higher‑quality growth before adding large positions. Consider the Zacks upgrade and estimate revisions as inputs rather than proof of sustained outperformance.
• For traders and technical investors: Expect volatility around prints and guidance bars; the post‑earnings gap shows sentiment can swing rapidly on beats and upgrades — but also that upside may consolidate if investors wait for confirmatory quarters.

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MongoDB’s thesis — that a developer‑friendly, cloud‑native document database can become the operational data backbone of AI applications — is credible and backed by measurable Atlas growth and partner traction. That makes MDB an attractive conditional buy for those who believe consumption economics for AI will continue to favor managed database platforms. The countervailing forces — cost of cloud delivery, hyperscaler counter‑moves, and the execution bar for converting pilots into durable ARR — are real and merit close monitoring. For anyone allocating capital or architecting systems around MDB, the most prudent approach is evidence‑driven: verify Atlas consumption trends, margin performance, and cohort retention over subsequent quarters before extrapolating the current momentum into a permanent growth re‑rating.

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Source: The Globe and Mail Bull of the Day: MongoDB (MDB)

 

[ChatGPT]

MongoDB’s recent quarter and partner headlines reset the narrative: Atlas consumption accelerated, management raised guidance, and investors responded with a sharp post‑earnings gap that has left traders and long‑term buyers debating whether MDB is a renewed growth story or a high‑flying name trading on AI enthusiasm.

Background​

MongoDB (MDB) is best understood as a document‑oriented database company that has spent the last several years shifting its economics and go‑to‑market from an on‑premises, license‑led model toward a cloud‑first, consumption pricing engine centered on MongoDB Atlas. Atlas is a fully managed cloud database service that runs on AWS, Microsoft Azure, and Google Cloud, and packages MongoDB’s flexible document model into a service developers can provision, scale and pay for by usage. This product posture — flexible schema, horizontal scaling, and consumption billing — is the core of the company’s bull case. In early December, MongoDB reported a quarter that beat prior guidance and prompted an upgrade to full‑year expectations. The headlines were straightforward: total revenue accelerated into the high‑teens year‑over‑year, Atlas growth re‑accelerated to roughly 30% year‑over‑year, non‑GAAP EPS comfortably beat consensus, and management lifted its FY outlook. Those data points — and the Microsoft partner narrative that accompanied them — are the basis for renewed investor enthusiasm.

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Overview of the quarter and market reaction​

The numbers at a glance​

• Total revenue: ~$628.3 million, up ~19% year‑over‑year.
• Atlas (cloud) revenue: accelerated to approximately 30% year‑over‑year and represented roughly three‑quarters of total revenue in the quarter.
• Non‑GAAP EPS: materially ahead of consensus; management reported EPS and margins that signaled improving leverage.
• Customers: MongoDB reported more than 62,500 customers and growing adoption in enterprise accounts.

These headline metrics were confirmed across company filings and independent coverage, and they underpinned the post‑earnings market reaction: a large intraday gap higher (Zacks and market recaps quoted a jump in the high teens to low‑twenties percent range on heavy volume) followed by a period of consolidation as investors parsed sustainability versus a one‑time beat.

Why the market cared​

Three mechanics explain why investors reacted so strongly:

• Atlas is consumption‑driven. When consumption re‑accelerates, revenue and margins can both move higher quickly because variable cloud costs are often offset by greater-than‑proportional usage growth and better fixed‑cost absorption. That dynamic matters more now with AI workloads that materially increase data and query volumes.
• Management raised guidance after the beat. A “beat and raise” is the archetypal trigger for re‑rating software names because it converts a positive surprise into revised forward expectations. The market tends to reward re‑acceleration that management validates with a guide lift.
• The AI narrative. MongoDB has positioned Atlas as the operational data layer for modern AI applications — vector search, retrieval‑augmented generation (RAG), and embeddings storage. If customers adopt Atlas to host high‑volume AI serving and retrieval traffic, the revenue upside per account can be substantial. That possibility increased investor willingness to pay for growth.

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Product and technical analysis: why Atlas matters​

Flexible schema and developer velocity​

MongoDB’s document model stores data as self‑describing JSON‑like documents (BSON). The practical result for developers is that schemas can evolve without expensive, coordinated migrations — fields can be added to new documents without changing every existing record. This flexible schema is especially valuable in fast‑moving product environments where feature changes occur frequently.

Horizontal scale and cloud design​

Atlas supports sharded clusters that let applications scale by adding nodes and distributing load, which is why the platform can serve very large workloads at low latency. For AI and telemetry‑heavy apps that stream embeddings, logs and real‑time events, the ability to scale horizontally is a prerequisite. The managed nature of Atlas (provisioning, backups, replication, upgrades) further reduces operational friction for teams that would otherwise manage clusters themselves.

AI‑readiness — a real product shift, not just marketing​

Over the last 12–18 months MongoDB has integrated features aimed at retrieval, vector search and embeddings workflows that are central to RAG and LLM augmentation. Those product investments make Atlas more than a generic operational database: they position it as a data‑platform play for AI applications that require fast retrieval, real‑time updates, and combined operational + analytical workloads. This is one reason bulls view Atlas as a sticky, high‑ARPU product if customers shift AI‑centric workloads onto it.

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The Microsoft partnership: distribution, engineering, and award claims​

MongoDB has been explicit about deepening its Azure integrations, and Microsoft co‑engineering and co‑sell programs materially reduce procurement friction inside Microsoft‑centric enterprises. That distribution channel can accelerate enterprise adoption for Atlas in organizations standardizing on Azure for AI and identity services. However, there is an important nuance: MongoDB’s disclosure and investor communication state that the company was recognized as a Microsoft United States Partner of the Year for 2025, and the company uses that announcement to underscore the strategic nature of its Microsoft relationship. Independent consolidation of Microsoft’s award lists across regions and categories can be fragmented, so while MongoDB’s claim is legitimate as a corporate statement, readers and procurement teams should ideally verify the precise award category and scope through Microsoft’s official partner awards postings if the award is material to a contract or deal assessment. In short: the award is a positive signal of partner alignment, but treat the specific phrasing as a company‑originated claim until independently confirmed.

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The bull case: clear drivers and their strengths​

• Strong re‑acceleration in Atlas consumption creates revenue leverage. If AI workloads increase per‑customer consumption patterns, Atlas can deliver step‑function revenue growth with improving margins.
• Managed, cross‑cloud product with developer mindshare. Atlas is available across the three major clouds, and MongoDB’s broad language drivers (.NET, Java, Node.js, Python, etc. make it a safe choice for many engineering teams. That multi‑cloud availability mitigates vendor lock‑in fears and presents cross‑sell opportunities.
• Partner GTM acceleration. Deeper Azure integrations and co‑sell traction can shorten sales cycles inside Microsoft‑led enterprises, which is valuable for large enterprise deals that bolster ARR and net retention.
• Evidence of execution. Recent quarters show MongoDB beating estimates and raising guidance; that track record is the simplest proof point for bulls that the company can deliver near‑term. Analyst upgrades (including the Zacks Rank #1 designation mentioned in market writeups) have followed the beats.

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The bear case and execution risks​

No re‑rating is risk‑free; several structural and execution risks temper the bull thesis:

• Cloud delivery economics and AI compute costs
  AI workloads — especially heavy inference or large embedding stores — can materially increase cloud compute and storage costs. If Atlas customers use the service for both storage and inference (or attach inference workloads close to the data), MongoDB must either capture more margin through pricing or face compressed gross margins. This is arguably the single largest operational risk for the thesis.
• Hyperscaler competition and API compatibility projects
  AWS, Microsoft and Google continue to expand native managed database options and are experimenting with compatibility layers that mimic popular database APIs. If hyperscalers offer lower‑cost or tightly integrated alternatives, it could pressure MongoDB’s market share or pricing. Open‑source projects that offer Postgres‑based document features also complicate the competitive landscape.
• Valuation sensitivity and expectation risk
  High growth at scale is expensive: MongoDB’s forward multiples remain premium to many peers, which means that even small misses in cadence can trigger outsized moves in the stock. The market’s reaction to guidance nuance in the broader software sector shows how quickly sentiment can reverse.
• Execution: converting pilots into durable ARR
  Sales wins are only valuable if they convert to sticky consumption. The metric to watch is dollar‑based net retention (DBNR) and cohort expansion: if new AI pilots stall or customers move to hybrid architectures with only partial Atlas consumption, growth could disappoint.

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Technical/trading picture: the EPS‑induced gap and what it means​

The immediate price action after the quarter was a large gap up on heavy volume — a classic “earnings‑induced breakaway gap.” That pattern often signals strong conviction, but it also concentrates gains in holders who may become reluctant sellers, which can produce sideways trading while the market waits for confirmation that the new growth rate is sustainable. Zacks and other recaps recorded a post‑earnings spike in the low‑twenties percent range, with volume multiple times normal levels. Traders should watch volume and follow‑through: sustained higher highs on rising volume suggests a durable re‑rating; a narrow range with thinning volume suggests profit‑taking and consolidation. For WindowsForum readers who trade or manage concentrated positions, keep these rules in mind:

• Validate guide conversion: the next quarter must confirm the full‑year guide lift.
• Monitor cohort metrics: DBNR and billings provide earlier signals than GAAP revenue for consumption businesses.
• Watch gross margin trends: any persistent margin compression demands a re‑examination of the thesis.

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Practical takeaways for enterprise teams (especially Windows/Azure shops)​

• If you manage Azure‑centric estates and evaluate operational data platforms, Atlas now offers deeper native integrations that can reduce operational overhead and speed deployments. Consider a pilot that models expected storage, query, and retrieval costs for RAG scenarios before broad rollout.
• For compliance‑sensitive environments, validate Atlas support for required controls (FedRAMP, SOC, customer‑managed keys) as early procurement gates. These certifications significantly shorten procurement cycles in regulated verticals.
• When planning RAG or embedding‑heavy services, model per‑query costs for storage + retrieval + inference. Even modest per‑request charges scale quickly across production AI workloads. Start small, measure unit economics, then scale.

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How to monitor the story going forward​

• Quarterly Atlas consumption growth — both absolute and as a percentage of revenue.
• Dollar‑based net retention rate and cohort expansion metrics.
• Gross margin trends (public cloud delivery costs per unit of revenue).
• Guidance consistency and any directional changes in management’s long‑term model.
• Partner announcements and measurable Microsoft/Azure channel wins (not just awards).

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Conclusion​

MongoDB’s early‑December quarter is a clean example of how product fit, partner distribution and an industry narrative (AI) can converge to re‑ignite a software name. Atlas’s flexible schema, horizontal scalability and multi‑cloud managed model are genuine technical strengths that map naturally to modern AI and application workloads. The company’s beat‑and‑raise, Atlas re‑acceleration and Microsoft channel momentum justify renewed investor interest, which is why Zacks and other outlets highlighted MDB as a bullish idea following the print. That said, the bullish case depends on durable consumption economics: if AI workloads materially increase delivery costs and MongoDB cannot price or monetize effectively, margins will be under pressure. Hyperscaler counter‑moves and open‑source alternatives remain credible threats. For enterprise adopters, Atlas is an attractive operational database for AI and modern apps if teams validate unit economics and compliance posture before rollout. For investors, the name presents a conditional buy — compelling on execution evidence and continued Atlas consumption growth, but sensitive to margin and valuation dynamics.
Overall, MongoDB’s December quarter renewed a credible growth thesis — but the next two quarters will decide whether the beat is the start of a durable re‑rating or a one‑off peak in sentiment.

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Source: The Globe and Mail Bull of the Day: MongoDB (MDB)