The Six Five team discusses MongoDB Local NYC 2023.
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Transcript:
Daniel Newman: MongoDB.local. This is the one of now what’s going to be 30 of these events. So instead of MongoDB World, the company decided to go glocal. They’re going glocal, they’re going to do 30 events, 19 countries. They’re going to be kind of doing a wave of announcements. Yesterday they did a bunch and I would say it was a big seminal moment for MongoDB in the advancement of its product portfolio. The Six Five was there, we were on the ground. We did what, eight? Eight interviews or eight conversations.
Patrick Moorhead: Eight Conversations, eight videos.
Daniel Newman: And you can find them all, we’ll put some links in the show notes. We talked to company’s head of product, we talked to some of their biggest customers in financial services like Wells Fargo and Citi. We talked to partners like Cisco. We talked to some startups, some cool startups like Ada and One AI. And it was a great day, it was a great day. So what did the company talk about though? Well, their CEO, Dave got on stage and basically focused in on the evolution of the product and the influence that AI is going to have on how the product was developed. Their head of engineering said something interesting in a later session that was designed for the analysts where we talked about when we started developing this product, we’ve kind of asked ourselves, what would we have developed the product to look like if we had developed it for the AI era?
And whether this is just great marketecture or this was actually what they believe they said, we would’ve designed it exactly like we designed it today. Which is an interesting inflection because something that’s been on my mind a lot is how was the database, data pipeline, data fabric needing to change for the AI data center? And we’re hearing different things about storage. And I had a long interaction with the executive team about this. I won’t do it with everybody now, but the fact is, where does software embed into hardware? Where in the stack an operational database like this, where does AI layer on it? What’s the real AI value? Pat, you asked some great questions about this as opposed to just-
Patrick Moorhead: Aw, thanks buddy.
Daniel Newman: As opposed to just accessing data, meaning making data accessible, the big wins, the big areas, vector search, stream processing, the company made some additional announcements in both of those areas that I thought were really interesting. One of the great examples the company gave with using vector search came down to being able to take different data sets that could be… They did this automotive one where they could take a sound of a vehicle, pair it with a large language model, and then pair it with all of the manuals across an entire portfolio of automobiles to then help a technician, based on just the sound a car makes, auto generate the text that would ultimately give them the workaround to fix a problem with a vehicle. Things like that, that require many different data formats to be simultaneously utilized to both access the data, the schema across structured and unstructured to then create a generative application. And that’s really where MongoDB can be pretty special.
And then of course it made some really good declarations about Atlas, that basically the company has made that full pivot to now Cloud being its leading growth engine, it’s got a multi-Cloud approach, the company is basically saying, we don’t care where. You can put it in Google, you can put it in Microsoft, you can put it in AWS. Clearly developing partnerships with all of them. So the company had some very declarative, some product advancements. I mean, look, the generative AI opportunity of the moment is enterprise search. It’s the ability to take your proprietary data and pair it up with these large language models. And the search feature is interesting because MongoDB is clearly making a run at Elastic and saying, look, we can do it and we can build it right in.
And maybe my last point to keep this moving is, I sense more and more whether it’s stream processing and what they’re doing there and the impact with Kafka or it’s the enterprise search, they’re kind of looking at TAM expansion and growth by just feature crawl, feature creep, feature crawl where they’re saying, look, you used to buy this feature from Confluent and you bought this feature from Elastic, you can just do this all in MongoDB and Atlas and you can just send all the money to us. And that’s going to put pressure on some of these point solutions to either innovate or integrate in order to stay relevant. And of course, MongoDB is doing a little bit more analytics too, which puts pressure on some of your traditional data analytics warehouses. They don’t want to say that they’re a data warehouse, but they’re kind of saying, we can do some of that stuff. So that’s another interesting thing.
Patrick Moorhead: I’m not a data warehouse.
Daniel Newman: It’s not about MongoDB, but maybe it’s about MongoDB, Pat.
Patrick Moorhead: Hey, you say Kafka, I say Kafka. Let me try to fill in-
Daniel Newman: Kafka, Kafka, Kafka, Kafka.
Patrick Moorhead: You say Kafka, I say Kafka. So anyway-
Daniel Newman: Is it right? Is one right?
Patrick Moorhead: I have no clue.
Daniel Newman: Okay.
Patrick Moorhead: Let me ask my son who uses Kafka. So I’m going to hit a little bit of a different incremental angle here. This was all about growth for the company. And I know the company is obviously very customer focused, but I’m going to look at it from a corporate standpoint. And there’s only so many ways you can grow. You can acquire somebody, you can do it organically, you can take somebody else’s share, you can create a new market or you can do something different. So a lot of elements of growth here. So what are some new elements, new things to sell? Even though they make most of their money today on OLTP database, they do have time series, full text search, and analytics, but they added stream processing and vector search. And like you intelligently said, Dan, there are other people in this market who do this.
And Elasticsearch popped up for me and apparently this is not in competition with Confluent on the stream processing side, but in collaboration with. I need to see the architectural diagram before I can say as Confluent did have a booth at the event. The other way you can grow is take market share and one way to do that, add new products or you can go vertical. And what we saw is Atlas for Industries as a big announcement and the first vertical that came out was financial services. We both talked to Citibank and who else? Citibank financial-
Daniel Newman: Wells Fargo.
Patrick Moorhead: Wells Fargo, who had some very nice things to say. And the fine point that they put on it is, hey, when it comes to things like regulatory, we’re special. So it totally made sense. Some of the other things that I appreciated was the simplification story. And we’re seeing this same simplification story in security and we’re seeing it in observability which says, Hey, all these best in breed one-offs are great, but you the enterprise has to integrate this all together. And by the time that you’ve integrated it all together, you’re probably three revisions behind on the best of breed and you’ve potentially created some security holes and you’ve spent a lot of money. And I like the approach here of simplification that says, hey, it’s OLTP, time series, full tech search, analytics stream, or vector search. You have one model and that’s a document model and you have a unified API.
And the final thing is the company’s great. They have a SQL migrator tool that says, hey, if you hate your SQL provider, whether it’s DB2, whether it’s Oracle, come on in, we’ll bring you in. We’ll not only help you migrate your data but will help you migrate your applications. This is called the relational migrator. So whether it’s the application code going from SQL to, can I call MQL or M-Q-L? And schema and data migration, they have you covered. So I just love to see these potential moves that they’re making to steal money from other people. There is market making and that’s where vector search comes in because vector search is very much a big part of the LLM stories that come out. Andreessen Horowitz published a really good schematic on what everything is together. And basically vector search they’re saying is the absolute foundation for all this. Text is important, but vector search when it comes to images, code, music, videos, that stuff really lights it up. So check out our eight videos on the Six Five podcast on LinkedIn, Twitter, Facebook, YouTube, of course.
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
