MongoDB vs VAST Data

Bank of America Backs MongoDB, Raises Price Target

Bank of America reiterated its buy rating on MongoDB and raised its 12-month price target to $540 from $450, citing accelerating AI adoption that boosts demand for data management systems. Analyst Koji Ikeda said MongoDB is positioned to win meaningful share of future AI workloads, praising its ability to handle large datasets, memory, scale, and real-time transactional data. The bank urged investors to view any fear-driven weakness as a buying opportunity. FactSet and LSEG data cited in the piece show MDB has rebounded strongly year-to-date and that the majority of Wall Street analysts rate the stock buy or strong buy.

• Bank of America reiterated its buy rating on MongoDB and raised its 12-month price target to $540 from $450.
• Bank of America analyst Koji Ikeda wrote that "MongoDB will win [a] meaningful share of future AI workloads."
• MongoDB was said to have rebounded 86% since March 31 and has more than doubled in the past year, per FactSet data.

Decoupling CRM from MDM for Device Management

The article describes an architecture for integrating Mobile Device Management (MDM) with an internal CRM without making the MDM a core dependency. Using NestJS, MongoDB/Mongoose, and TanStack Start with Fleet as the initial provider, the author separates business state (CRM-owned device records, device assignments, and immutable device action audit logs) from technical state (MDM-owned OS, hardware IDs, last check-in). Key patterns include a DeviceProvider abstraction (so different MDMs like Fleet, Intune, or Jamf can be swapped), a background DeviceSyncWorker that synchronizes technical device data into MongoDB every five minutes, and creation of DeviceAction audit records before executing destructive operations (lock/wipe) via the provider. The result is vendor independence, faster reads, auditable operations, and simpler frontend development.

• Author implemented an MDM-agnostic Device Management module using NestJS, MongoDB/Mongoose, and TanStack Start with Fleet as the initial MDM provider.
• The CRM owns three business concepts: Device (with a decoupled providerId), DeviceAssignment (historical ledger), and DeviceAction (immutable administrative audit record).
• A DeviceProvider interface defines required provider operations (getDevice, listDevices, lockDevice, wipeDevice); provider-specific implementations (e.g., FleetProvider, IntuneProvider) satisfy that interface.

How to Export FTDC From MongoDB Atlas

This technical how-to shows how to extract FTDC (Full Time Diagnostic Data Capture) from MongoDB Atlas using an undocumented Atlas Admin API (v1.0) that creates a log collection job, polls for success, and downloads a bundle containing per-member diagnostic.data directories including the live metrics.interim file. The author lists the three required inputs (programmatic API key, project ID, replica set name), provides example curl calls, and describes bundle metadata (expiration ~30 days). Atlas sets diagnosticDataCollectionDirectorySizeMB to 400MB, which typically yields 2–5 days of FTDC under real load (longer on idle clusters). The post also warns that the built-in clusterMonitor role grants read access to the oplog and demonstrates a more restrictive diagnostics-only custom role. The author references tooling (Big Hole, keyhole) for reading FTDC locally.

• MongoDB servers write FTDC (Full Time Diagnostic Data Capture) into a diagnostic.data folder with roughly 5,700 metrics sampled once per second.
• MongoDB Atlas exposes a v1.0 Admin API endpoint (logCollectionJobs) that can package FTDC on demand, requiring a programmatic API key, project ID, and replica set name.
• The FTDC extraction flow uses three API steps: create a logCollectionJob, poll the job until SUCCESS, and download the resulting bundle (example curl sequence provided).

Beamr and VAST Data Partner for AI Video Enhancement

Beamr Imaging Ltd., a video optimization technology company, has joined the VAST Cosmos Community as a Technology Partner. The collaboration with VAST Data, the AI Operating System company, aims to deliver new AI-powered video workflows for broadcasters and content owners. The partnership will debut at IBC 2026 in Amsterdam with a demonstration combining the VAST AI Operating System, NVIDIA Video Super Resolution, and Beamr's content-adaptive bitrate technology. This workflow enables broadcasters to upscale, AI-enhance, and modernize their media archives while controlling storage and delivery costs. The pipeline runs on NVIDIA RTX PRO GPUs and can be deployed in the cloud or on-premises. Beamr plans to collaborate with VAST Data on joint go-to-market initiatives.

• Beamr Imaging Ltd. joined the VAST Cosmos Community as a Technology Partner.
• The partnership will debut at IBC 2026, September 11-14, in Amsterdam.
• The demonstration combines VAST AI Operating System, NVIDIA Video Super Resolution, and Beamr's content-adaptive bitrate technology.

VAST Data and AMD Partnership

VAST Data Expands Collaboration with AMD to Advance AI Infrastructure for the Inference Era

VAST AI Operating System with AMD EPYC™ CPUs, Instinct™ GPUs, and Pensando™ AI NICs delivers efficient AI inference for cloud, enterprise, and agentic AI workloads.