Data-Sleek founder and CEO, Franck Leveneur appeared alongside Couchbase CMO, John Kreisa on DM Radioโs Really Real-Time Data hosted by Eric Kavanagh. During the interview, Leveneur, Kavanagh, and Kreisa discussed the importance of data management, the role of real-time data, and the future of AI in business.
In the first segment of the interview, Kavanagh, Leveneur, and Kreisa discuss the evolution of real-time data management and the challenges faced in delivering personalized experiences using modern technologies. Leveneur and Kreisa share their insights on the importance of JSON-based databases, the rise of hybrid transactional analytical processing (HTAP), and the integration of vector databases with large language models.
Kavanagh and Kreisa begin the discussion with highlights from Couchbaseโs Capella platform. Couchbase combines an operational data store with a columnar store for real-time analytics. Their platform reduces latency and provides for adaptive applications. Kreisa emphasizes the flexibility of JSON-based document databases in handling diverse data structures while delivering personalized experiences.
Leveneur, with almost three decades of experience in databases, discusses the challenges of managing structured and unstructured data from various sources. He underscores the importance of choosing the right database engine and architecture early in an organizationโs data journey.
The first segment also touches on the potential of vector databases, which convert text and imagery into numerical values. This enables efficient comparison and consensus-based analysis. The interview explores the implications of these technologies for real-time data management and the integration of large language models into workflows.

Key Takeaways
- Using real-time data to offer hyper-personalized experiences is increasingly critical for data-driven enterprises.
- Choosing the right database architecture is criticalโchanges can be costly and disruptive.
- Vector databases are essentially consensus engines.
THe Role of Real-Time Data Interview: Segment 1
Host: Eric Kavanagh
Guests: Franck Leveneur (Founder, Data-Sleek), John Kreisa (CMO, Couchbase)
Broadcasted May 23, 2024
Find the full podcast on DM-Radio.Biz Here.
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Eric Kavanagh: Ladies and gentlemen hello and welcome back once again to the longest-running show in the world about data. Itโs called DM radio. Yours truly, Eric Kavanaugh here, in year 17 of the data management radio show. Weโve been rocking and rolling for quite some time now and some things change a lot. Some things never change. And one thing that will never ever change is the need for data. Thatโs what the show is about. Itโs all about data management and data persistence.
Obviously a lot of attention is on AI these days. Do you know what AI needs to do its job? Data, lots and lots and lots of data. Today weโre talking about one of my favorite topics which is really real time data.
That actually is a bit of a play off on one of my favorite movies, Repo Man. The music for our show is from the movie Repo Man from way back in 1983. I think with Emilio Estevez and a couple other characters.
This one scene where he says he had the dream and โitโs really real, itโs realistic.โ So much in fact, that the first article I ever wrote in this industry was back in 2002 and I want to say and itโs all about real time data.
So 22 years ago we were talking about real-time data and like things have changed since then, thereโs a lot going on. There are a lot of different engines out there to do it.
Itโs a lot less expensive to do it and the computer is much more powerful these days. We have distributed architectures. There are lots of different ways you can โfry the fishโ these days if you will, and weโre going to find out about those from our guests today.
Exploring the Role of Database Vendors
Eric Kavanagh: Weโve got John Kreisa from Couchbase and also Franck Leveneur from Data-Sleek. Both experts in the data field. When push comes to shove, like I said, there are lots of different ways you can do this.
Buying enterprise software is a very serious matter. You want to make sure that you buy the right technologies. You want a vendor whoโs going to work with you, be there for you, change, and adapt over time.
And of course, Couchbase is one such company. Theyโre one of the many companies that spun out to compete against the former Goliath of Oracle, for example, IBM, and DB2.
I remember watching this database explosion a number of years ago and itโs really quite impressive. You got a whole bunch of different databasesโthereโs like a dozen or more of these open-source databasesโand theyโre all fit for purpose. Theyโre all doing interesting things.
These days I think thereโs like 147 established database vendors. So what does that mean? It means you got a lot of choice, but you got to figure out where does one database engine excel versus another. Thatโs what weโre going to find out today.
So with that, John Krysa, what brings you in from Couchbase? Tell us in the real time world. You guys have done some interesting stuff lately. Whatโs the latest with Couchbase?
Couchbaseโs Real-Time Application
John Kreisa: Thanks, good to be with you again Eric, and good to be with your audience. Iโm John Kreisa, Iโm the chief marketing officer here at Couchbase and we offer Couchbase Capella, which is a cloud database platform for modern applications.
The modern applications that include operational data, but weโve also added a column or data store for that real-time processing and analytics of data. It eliminates some important latencies and serves some real important needs in terms of giving enterprises the ability to deploy applications.
Adaptive applications, as we would call them, to their customers. This can react to real-time data, to real-time inputs, and make them much more situationally aware and hyper-personalized. You can get that experience and take a wide variety of data into the database and give those experiences.
Eric Kavanagh: Itโs interesting, you know, you were mentioning before the show with this new columnar store. Youโve added to do what amounts to, in database analytics, is the term we used to use. You think about how we got here, and obviously there are some big goliaths that are still around.
For example, IBM of course, and Oracle. Theyโre all still selling software. But you know, when you can bolt on a functionality like that, you are really serving all sorts of different purposes. To your point, historically, you would have had to have some other tool. You pull the data out into that tool and thatโs what you do. Itโs like that increases not only latency but it creates another choke point. It creates another bottleneck. It creates another place that things can break.
So when you can do that inside, I mean, and the real question is, you know, โhow do you set that up and how do you get it all running?โ But I think it makes complete sense to have the one system that youโre using as your foundation. Your data foundation serves both of these purposes. We can do that today efficiently, right?
John Kreisa: Yeah thatโs right. By having them side by side in the same architecture, it reduces as you said that latency. Thereโs no ETL process to move data to another system where it gets processed.
In addition, thereโs an impedance mismatch which is overcome. We are a document-based database, based on Json. So the columnar store also operates in Json so the data can transfer seamlessly between the two. That just gives a much faster, better experience for providing those analytics back into the applications and back into the operational store. Thatโs the core foundation of Couchbase.
As you said, in-memory architecture distributed for really, really interactive applications. Our customers run their most mission-critical and business-critical applications on Couchbase. So bringing that analytic capability close in there, the feedback weโve had from customers has been super positive.
Navigating the Challenges of Large Language Models in Workflows
Eric Kavanagh: Yeah and you know, I heard a quote, this is a number of years ago. Itโs probably almost 10 years ago now, but Iโll never forget this. A guy said that Json is the jpeg of data. Right?
John Kreisa: I havenโt heard that one, but I like it.
Eric Kavanagh: Of course Json is this architecture, right? Itโs a hierarchy. Basically, there used to be HTML and wasnโt it xtm l or something like that?
John Kreisa: XML. Certainly description language.
Eric Kavanagh: Yeah, itโs still there. I mean people still use XML, but Json just won the battle. Json is everywhere. Maybe just to explain to the audience and the business world out there why a Json architecture matters to be able to capture all sorts of different architectures of data.
I think thatโs the key. Itโs not just columns and rows. Youโre talking about a whole hierarchy with nested data and all kinds of different things. Because youโre a Json database by architecture, that means you can absorb all kinds of differentโtraditionally unwieldyโdata types. Is that about right?
John Kreisa: Yeah, thatโs correct. I mean the document using the document based on Json as the fundamental storage structure and representation of the data gives you a lot of flexibility because itโs self-describing.
You know what kind of structure and data is coming in but itโs not limited to rows and columns. It can actually be widely variable in terms of how you set it up so that a document database can handle time series data. It can handle graph-like data, it can handle medical data, medical records, it can handle transactional data.
Thereโs no doubt that Couchbase is being used for transactional applications, which are serving financially-related applications. Weโve got all that flexibility. That comes by making the choice to use documents and a Json based document as the core infrastructure. So lots of flexibility there.
Json: The De Facto Reference Architecture
Eric Kavanagh: Iโve got another client that was doing some really interesting stuff with Json and they view Json as a de facto reference architecture. I thought that was very interesting and you can use that as a personalization window into different entities or people so in that Json structure you can bring in characteristics of the entity or the person or the group or whatever and then that becomes key to your personalization efforts.
John Kreisa: Yes, thatโs right. Itโs a metadata, if you will. Itโs stored amongst the very data itself which gives you more flexibility on how you create an application. Which is reacting to how you know which user youโre in, and what the situation that userโs in, and how youโre serving up that data. The experience you give themโa really hyper personalized experienceโthatโs key to it.
Delivering a Personalized Experience
Eric Kavanagh: Well and so Iโll throw one last question over at you and then weโll bring Franck into the conversation here. Personalization is going to be the key to success, it seems to me. And itโs like anything else in this industry, weโve talked about it for decades. Itโs not new. Weโve talked about it for a long, long time.
Lots of people have been kind of jaded on the concept because it never quite got there. I think it didnโt get there because of the architectures, because of the compute power, because of lots of different factors. But now itโs like, โno really guys, we really, really can do this now,โ right?
John Kreisa: Yeah I think youโre right. I think it is a combination of network speeds and processing power flexibility thatโs in the application. Then a lot of times the analytics really did have to go to a separate system to get the Insight you needed to do that personalization. Now thereโs a lot of things out there, and we were talking before about Ai and what thatโll do.
Thereโs certainly fraud detection that uses machine learning type AI applications which are operating in real time. But those are very complex architectures. Now itโs something where more applications can have that architecture and deliver that personalized experience.
The Evolution of the Data Management Industry
Eric Kavanagh: Thatโs good stuff. Letโs bring in Franck Leveneur. Franck, youโre in this space. You consult with your clients all the time on real- time data. We were joking that itโs not new. I wrote about it 22 years ago and it was new then, itโs not new now. Itโs been around a long time, but it really is real-time now. What are your thoughts on the evolution of this industry and how close we are to delivering on long offered promises? What do you think?
Franck Leveneur: Thank you. Well first of all, I just want to introduce myself a little bit. I have about 25 years of experience in databases. I started with Microsoft SQL back in the day and then moved into MySQL. I worked also on AWS optimizing MySQL, DBS, and Aurora, and I saw the evolution of how you used to manage a database on the server. Then after AWS came in and the database was being managed by AWS. DBAs were afraid that we were going to be out of jobs.
But I think whatโs important is to embrace the technology, to learn about it. Thereโs always going to be some work needed. Real-time analytics, real-time data are definitely something that have evolved enormously and especially with mobile devices, IoT devices, 5Gs. I think all those are going to accelerate the data feeds. AI, of course, being able to use cameras and detect whatever it is.
I was watching some videos on YouTube yesterday where some cameras can detect the behavior of people on the street, whether they are dancing or whatever. [Whether] they are wearing a weapon, etc. So all this data is going to be fed somewhere. Some analytics are going to be done in the background. But thereโs always going to be something happening.
The challenge is going to be to choose the storage, to choose the technology behind that in order to process this very quickly and do whatever needs to be done. The challenge is also about real-time data in the different data structures.
There are structured and unstructured data. Now we have sound, we have video, we have documents, we have text, we have JSON. That gets fed with APIs. I would say itโs interesting and it brings a lot of challenges. It makes you think architect, find the right solution, and find the right database engine to do the job.
Sometimes you choose the wrong one and then you have to migrate, which can be costly. Iโve seen that a couple of times, especially for startups, where theyโll pick a database and theyโll grow very quickly. Because they have a lot of data, and they have success, then they have to migrate because theyโre abusing the database. Theyโre running crazy queries on it.
Some people are very creative. They come up with multi-line and hundreds of lines of SQL queries, and then theyโre surprised that their application is not working properly. A lot of people donโt know that transactions are transactions, and analytics are analytics. They are two different worlds, two different behaviors of the data. One is constantly evolving. Itโs alive. The other one is at rest, and youโre just querying the history. So itโs a totally different behavior.
The Rise of HTAP and Managing Transactional Analytics
Eric Kavanagh: And thereโs this thing thatโs not terribly new either. I think it was probably nine years ago or so that I was looking into it. HTAP as they call it. HTAP, hybrid transactional analytical processing. Monty Widenius is a guy who was working on some of that stuff as I recall.
Franck Leveneur: Yes
Eric Kavanagh: What they would do is they had sort of a sniffer, meaning when the query comes through, the sniffer will look at this and say, โHm is this a transactional workload or an analytical workload?โ Itโll route accordingly. Now that stuff, have you seen that in practice, and how well does that work from your perspective?
Franck Leveneur: Well itโs actually a very good question because weโve been seeing these issues where, like you said, they want to use a database for both the analytical and the transaction. Back in 2016/2014, I discovered a database engine called, at the time was called MSQL. Itโs called SingleStore.
Eric Kavanagh: Sure, yes.
Franck Leveneur: Thatโs what they do. Itโs HTAP, theyโre able to do transactions, but they can also do analytics. And now theyโre also moving into the vector engine. So itโs a pretty scalable solution. Thatโs the thing with Snowflake is to separate the compute from the storage.
I think itโs also critical right now, today, to be able to not worry about your storage. That used to be a problem where your data space goes too quickly. You run out of space and then your application goes down.
Now, I think, is a thing of the past. SingleStore has transitioned to that. They use actually S3 as a storage layer and then some caching mechanism, and then you can scale the computer as much as you need. They share the data for you. Itโs an efficient platform even for data ingestion, being able to plug into history and ingest data. Itโs very useful.
Vector Databases and Workflow Integration
Eric Kavanagh: Yeah, and Iโm glad you brought up vector databases. We should probably talk about that a little bit at least. Because, of course, these are the tools of choice to go alongside a large language model to host your embeddings, basically.
What these engines do is they just convert text and imagery to numeric values and then they convert back to text or imagery on the other side. So youโre always going to have a little bit of lossy nature to it, because you are doing this conversion. But nonetheless, the vector databases are very, very good at comparison and contrasting. Theyโre reallyโwhat did someone say? Theyโre really consensus engines.
What theyโre doing is theyโre taking so much of this data. You have, like a dog starts here, and goes like this, and all dogs go like this. Or theyโre in this general area. Cats are maybe over here, highways are over here. And somewhere in that expanse, you can mix and match different things. But theyโre really consensus engines. So theyโre doing different things than you would do for real-time data, for example.
Nonetheless theyโre a huge force out there. Everybodyโs working on their Vector databases. I look at this whole, and our first break is coming up here, but this whole new gen AI spin has clearly captured the imagination of companies everywhere. It is not fit for every purpose in the data world, it is not fit for deterministic use cases. It is very fit for stochastic use cases, for discovery, for playing around with ideas, and learning things.
But Iโm here to say that as a front-end, these large language models are going to fundamentally change how we interact with information and how executives interact, how working people interact. Itโs a big deal. That vector database is a big part of that equation. But donโt touch that, folks. Weโll be right back in 1 minute. Youโre listening to DM radio.
This interview has been edited lightly for clarity.
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