Snapshot · The MongoDB Podcast
Fortune 500 customers are now integrating MongoDB directly into lakehouse architectures rather than treating transactional databases and data lakes as separate systems.
Where this was said
At 24:33 · chapter starts 24:28
Apurva asks where the partnership is headed, and Vinay paints an ambitious multi-front roadmap. The first trend: customers are refusing to treat MongoDB as a separate transactional database. A Fortune 500 client migrating to a cloud lakehouse — whether on BigQuery, Azure Fabric, or Azure Data Warehouse — wants MongoDB embedded inside the lakehouse itself, enabling unified data management that spans streaming, object storage, and AI analytics in one estate. [1] — Vinay Makkaji "A Fortune 500 client migrating to a cloud lakehouse isn't treating MongoDB as a separate transactional database — it wants MongoDB embedded…" 24:33 The second major frontier is mainframe and AS400/DB400 modernization: decades of enterprise data locked in EBCDIC encoding can now be migrated to MongoDB using out-of-the-box tooling, unlocking that data for AI workloads. Third, digital manufacturing and industrial IoT — connected vehicles, fleet management, factory of the future — represent a 5-year growth runway beyond oil and gas. Farid adds the strategic dimension: MongoDB is no longer just a database choice for Capgemini; it is a co-investment partner protecting partner margins and accelerating multi-year transformation programs. In some cases, [2] — Farid Mohammad "MongoDB is no longer just a database choice for Capgemini — it's a co-investment partner protecting margins and accelerating multi-year tra…" 28:26 this model has already delivered 30 to 50% improvement in outcomes. The memorable closing line: technology builds capability, but partnerships build outcomes.
A Fortune 500 client migrating to a cloud lakehouse isn't treating MongoDB as a separate transactional database — it wants MongoDB embedded inside the lakehouse itself. This signals a broader architectural shift toward unified data management that combines streaming, analytics, and AI in one platform.
MongoDB and Capgemini are targeting mainframe and AS400/DB400 modernization as a major upcoming partnership use case, with MongoDB offering out-of-the-box solutions.
Mainframe and AS400 modernization is emerging as one of the biggest near-term partnership opportunities for MongoDB and Capgemini. Legacy systems storing data in EBCDIC format are being migrated into MongoDB using out-of-the-box tooling, unlocking decades of enterprise data for AI workloads.
Beyond oil and gas, digital manufacturing, industrial IoT, connected vehicles, and fleet management are the next major use cases for the MongoDB–Capgemini partnership. These bridging physical-digital architectures represent a 5-year growth runway for joint solutions.
Vinay Makkaji sees a positive collaboration opportunity over the next 5 years between MongoDB, Capgemini, and hyperscalers across data modernization and industrial IoT.
MongoDB is no longer just a database choice for Capgemini — it's a co-investment partner protecting margins and accelerating multi-year transformation programs. In some cases, this co-innovation approach has delivered 30 to 50% improvement in outcomes.
Capgemini has seen almost 30 to 50% improvement in outcomes in some cases when leveraging the MongoDB partnership to accelerate transformation programs.
Sam's initial MVP was coded in approximately one week using ChatGPT voice mode and copy-pasting code, with no prior technical experience.
Sam argues Discord is 10x better than email for building relationships with younger users who rarely check their inbox.
Sam's monthly operating costs include Cursor ($200), AI image generation ($100), AI video generation ($200), hosting ($100), email marketing ($80), and AI compute ($300–$500).
Sam recommends copying days of Discord chat history into ChatGPT and prompting it to list recurring pain points as a fast, free market research technique.
Bhanu and his team built approximately 50 free tools to attract search traffic, each linked back to SiteGPT.
With AI coding tools like Cursor, Bhanu can now create a new free marketing tool in less than 5 minutes by referencing existing tools.
Bhanu filters Ahrefs keyword results to show only those with a keyword difficulty below 10, making them realistic ranking targets for any decent website.
Bhanu sets a minimum search volume of 1,000 monthly searches when selecting keywords to target with free tools.
PropGPT averaged 20 downloads per day right after launching on the App Store through influencer marketing.
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