PostHole
Compose
Login
SE Radio 561: Dan DeMers on Dataware
Dan DeMers of Cinchy.com joins host Jeff Doolittle for a conversation about data collaboration and dataware. Dataware platforms leverage an operational data fabric to liberate data from apps and other silos and connect it together in real-time data networks. They explore a range of key topics, including zero-copy integration, encapsulation and information hiding, handling changes to data models over time, and latency and access issues. The discussion also explores dataware management and security concerns, as well as the concept of 'data plasticity' as an analogy to neuroplasticity, which is where the nervous system can respond to stimuli such as injuries by reorganizing its structure, functions, or connections.
Dan DeMers of Cinchy.com joins host Jeff Doolittle for a conversation about data collaboration and dataware. Dataware platforms leverage an operational data fabric to liberate data from apps and other silos and connect it together in real-time data networks. They explore a range of key topics, including zero-copy integration, encapsulation and information hiding, handling changes to data models over time, and latency and access issues. The discussion also explores dataware management and security concerns, as well as the concept of ‘data plasticity’ as an analogy to neuroplasticity, which is where the nervous system can respond to stimuli such as injuries by reorganizing its structure, functions, or connections.
Show Notes
From the Show
From IEEE Computer Society
From SE Radio
- Episode 523: Jessi Ashdown and Uri Gilad on Data Governance
- Episode 507: Kevin Hu on Data Observability
- Episode 484: Audrey Lawrence on Timeseries Databases
- Episode 456: Tomer Shiran on Data Lakes
- Episode 417: Alex Petrov on Database Storage Engines
- Episode 397: Pat Helland on Data Management with Microservices
Transcript
Transcript brought to you by IEEE Software magazine.
This transcript was automatically generated. To suggest improvements in the text, please contact [email protected] and include the episode number and URL.
Jeff Doolittle 00:00:17 Welcome to Software Engineering Radio. I’m your host, Jeff Doolittle. I’m excited to invite Dan DeMers as our guest on the show today for a conversation about data collaboration and dataware. Dan DeMers is co-founder and CEO of Cinchy and a pioneer in dataware technology. Previously, he was an IT executive at some of the most complex global financial institutions in the world, where he was responsible for delivering mission-critical projects, greenfield technologies, and multimillion dollar technology investments. After realizing that half of all IT resources were wasted on integration, he created Cinchy with a vision to simplify the enterprise and provide the rightful owners of data with universal control of their information. Dan, welcome to the show.
Dan DeMers 00:00:59 Thanks for having me. Happy to be here.
Jeff Doolittle 00:01:00 So your bio seems to give a bit of a sense of what dataware might be. So, give us a brief introduction to what dataware is and why our listeners should be interested in it.
Dan DeMers 00:01:12 Sure. The easiest way to understand dataware is to actually just remind ourselves what is software? Because there was a day where software didn’t exist and then it came into existence, and today we take it for granted. But so, what did software do? It separated the form from function, right? We had machines, machines existed prior to software, post-software, though, you have machines but machines can then be programmed, which is the instruction, the logic, i.e. the software. And that changed and transformed how you think about machines. Right now, from that point forward, the more programmable a machine is the longer that machine is going to last, the more versatility is going to have, the more function that’s going to be able to be capable of doing because you can defer that till after the manufacturing process. A brilliant major shift and changed the world and continues to change the world today.
Dan DeMers 00:01:59 Well, dataware is really just the next step in that inevitable decoupling. And this time it’s not separating the form from function, it’s separating the knowledge from the function, from the logic. So, it’s essentially decoupling data from the software, and that magically simplifies everything, quite frankly. And it starts with relieving software from all the complexity of how to store data, how to integrate data, how to share data, how to protect and control data, and can now allow the software to do what it was originally intended to do, which is implement the functionality, implement the logic, the actual program, and let dataware solve the data problem in the same way that software lets hardware solve the physical machinery problem.
Jeff Doolittle 00:02:40 So what are some of the challenges that people face in shifting first maybe their thinking from the current paradigm to what you’re describing. And then after that, maybe we can start digging a little bit more into some of the technical challenges. But maybe first start with sort of what does it take for somebody conceptually to kind of transition from the current paradigm to more of this dataware approach that you’re advocating?
Dan DeMers 00:03:00 Right. I would say it’s a really good question, and I don’t know if I’ve even cracked the code on that, in spite of giving that a whole lot of time and energy, because it is both strangely simple and complex. And what I’ve come to realize though is it’s easier to explain the concept of dataware sometimes to a child that has no existing reference frame on how it works. And I learned that just even through explaining it to my kids. I’ve got three young boys and their friends, and they would just kind of naturally get it. Whereas someone who has 30 years of experience and has gone through several iterations and understands data lakes and data warehouses and data mesh and data fabric and all these latest buzzwords; dataware is hard for them to get their head around.
Dan DeMers 00:03:44 And what I’ve also come to realize is, so it’s an unlearning journey as much as it is a learning journey, but there’s also just a lot of almost like collateral damage from the overhyping of data-related technologies. Like, if you go back to data warehouse and data marts and data master, data fabric and data virtualization and master data management and, each of these things, if you read the marketing materials of the vendors when it was coming out, it sounds like it’s going to save the world, right? But it doesn’t. It solves an individual problem and sometimes even creates additional problems. So, there’s all this noise of what were really false hype cycles, right? That weren’t major shifts. Software is the last major shift, right? That was a big deal; that genuinely changed the world and continues to software’s eating the world and continues to, but dataware eats the software that’s eating the world. So, it’s a combination of unlearning and making it feel practical in a context that you understand. That’s what I’ve found. But again, I haven’t cracked the code, so I don’t know, maybe we can figure it out together.
Jeff Doolittle 00:04:50 Well then how does dataware relate then to applications maybe in a way that’s different from what’s previously been thought of?
[...]