Ready to scale your company or are you working for a company that is in the process of scaling up? Find out how you can build your software architecture or what changes you need to do to your current one so your product doesn’t break. Join Lee Atchinson and Ken Gavranovic to learn what are some best practices around architecting and operating for scale: risk management, moving to microservices, continuous vs future releases.
To learn more about Lee visit leeatchison.com
To learn more about Ken visit kengavranovic.com
Ken Gavranovic: Lee, so good to see you again. It's been a while and I'm excited to talk about "Architecting for Scale". You've now got the second edition out, right?
Lee Atchison: That's correct. It's great to see you, Ken. I always love talking to you about these topics or frankly, any topic.
Ken Gavranovic: We always have great conversations. I think the thing that's always interesting to me about somebody that takes the time to write a book is: what's the first thing that inspired you? There was a time you were like, "Hey, I've got this thing I'm seeing as a problem and I wanna share my view with the world." Tell me what originally inspired you to start writing "Architecting for Scale”.
What problem does “Architecting for Scale” solve?
Lee Atchison: I worked at Amazon for many years. I learned a lot about building scalable systems there. And when I left Amazon, I moved to what at the time was a relatively small startup called New Relic, which had maybe 100 total people in the company, a little bit less than 100 people. They were running into a lot of scaling problems, of trying to grow from being a small system to being a much larger system. I saw them struggle with the same sorts of things that we had already solved at Amazon. And so, I became the scalability expert, if you will, and helped with putting in processes, and procedures, things like risk management matrices, and service tiers, and all those different processes, to help them get to the point where they can start managing their systems and scale them without having availability outages.
When I did that, I realized New Relic wasn't unique. There was nothing special about the problems they were running into, and that all companies of probably similar size, were running into similar sorts of problems. And that's where I thought, I bet you there's a book here, I bet you I could write this into a book. I got connected with O'Reilly and suggested that book, and they were excited about the idea. And I went from there. The first version came out in 2016 based on my experiences with helping get New Relic through the initial push. As the book started growing in popularity, I started getting interactions with lots of other companies. I'd often get comments like, "Oh, you should hear what we ran into." and started seeing the breadth of companies and the problems they've had with scalability, in general. I fed a lot of that learning into the second edition of the book, which came out about a year ago.
Ken Gavranovic: That makes sense. I know we had some fun times talking with a lot of enterprises across the globe at New Relic because when we think about transformation, it's always people, process, and technology. It's all three of those coming together and bringing a book that hits on the technical parts but also some of the people process parts, I know that's a crucial component that you hit on a lot in the book.
Lee Atchison: It's very clear that you can't solve scaling and availability by code. You can't solve it by just wishing it away. It's a combination of developers and management doing the right things, and putting processes and procedures in place to do the right things. And all those things together, really have to work as a cohesive whole, for you to be able to build a system that's highly available and still be able to scale. Just as a simple example, we've talked about this topic a lot, Ken, risk management.
Risk management/ Risk matrix
Lee Atchison: The ability to come up with and figure out what the risks of your application are, to reduce availability risk to your application, and be able to scale, that's as much of a process issue, as it is a technical issue. When you build technical debt into the application, dealing with that technical debt and understanding which of it you need to remove versus living with, that's a process issue, not a technical issue. And putting all those things into place requires a whole cohesive process that's not just technology, not just management, not just engineering, not just a key. It involves everybody working together to make that happen across all the different disciplines that are important in building an application.
Ken Gavranovic: Lee, let's double-click for a moment on the risk matrix. I always think that's interesting because a lot of times when people first go to a risk matrix, they say, "Okay, let me identify what the key risks of the business are. What are some shaky things"? But it's also a really powerful tool to not only understand risk but also understand where you should invest. Think about the number of times that you and I have talked to large enterprises where maybe after a major outage or a major impact, they came back and realized that for years, they had underinvested in that particular area because maybe it was running okay. But no one had a risk matrix, a data point to show that this area is risky and it makes sense to invest. So, even for technology leaders, it's a great tool to talk about investment with their business partners.
Lee Atchison: Absolutely. I think the under-recognized value of maintaining and building risk matrices is the ability to communicate with upper management. When your management comes to you and is frustrated because you're not getting the things done you need to get done or they think that you need to get done, and you start talking to them about all these other things you need to do, it's hard to explain and have the conversations to understand what's important and what's not. When management wants new features and engineering wants to improve things and operations just want things to work no matter what all of these different competing requirements can't be prioritized against each other. What the risk matrix does is it puts all of those things together into a single format and it starts putting measures on these things. So, you can tell that not doing this capability is gonna cost us this amount later on because of this risk that's going along with it. And by doing that, you can communicate both technical risks up, but also product risk down throughout the organization and start being able to prioritize and make decisions a lot better on what things are the most important things to work on first. Is it more important to remove this technical debt or add this new feature? What's more important? How do you make those decisions? A risk matrix can help you accomplish that.
Ken Gavranovic: I love how you emphasize that in the book and it allows people to take the term technical debt and, again, translate it into risk, or business terms. Business leaders, when you're like, "Hey, we have technical debt," they are usually like, "Hey, you're just looking for more investment." But when you can talk on these other terms, it helps a lot. Another of the things I like in the book is that you not only give the framework of how to set it up but you give people actionable examples that they can go to your website and download templates, right?
Lee Atchison: That's correct. There are 1,000 different ways to do a risk matrix. I have a model that I prefer. I know you have a model that you prefer and I make those available. It’s a simple Excel spreadsheet format available from my website, you can download and get to. And if you go to leeatchison.com, you can get access to those there. I believe you can also get access to them from O'Reilly. But there's nothing magic about it. It's not like you have to use a particular format or it won't work, that you have to do this, that, and the other thing. But knowing where to start and having an idea of where to start can be a great way to synergize your organization. I've seen companies that have taken that initial format and said, "Well, this just isn't gonna work at all, but they took it and started with it." And what they ended up with was something that looks completely different and I have a hard time recognizing it. You can still see it's a risk matrix and it has the things that they care about the most built into it. It's not just likelihood and severity, which are the two things that I focus on primarily in a risk matrix, but they've also included things like revenue impact. What is the revenue impact for doing or not doing a particular item within the risk matrix? And it's a step further. It's a step deeper into the processes that work for them and that's great. So they started with the initial idea of the risk matrix, took the initial templates, and built on it from there.
Best practices for moving to or starting with microservices
Ken Gavranovic: Let's talk a little bit about deconstructing the monolith into microservices. You mentioned this with New Relic. I think companies that are building new, oftentimes start with microservices. So, I wanna hit on that too. But let's talk about companies that maybe still have some monoliths first. They're moving to microservices, trying to go to a services architecture, and thinking about SLOs and SOIs. You have a lot of great information in the book about going through that transformation. What are some of the key takeaways that companies should consider that they can uncover in the book?
Lee Atchison: Sure. A lot of the devil's in the details. There's a fear from many people that are moving monoliths to microservices. The main fear is past experiences that haven't gone so well. If you look at the things that can go wrong in that sort of transformation, there are a few common things, that appears that people can always run into. And by dealing with them upfront, you end up in a much better shape as time goes on. One of the big ones is where do I wanna put my service boundaries? Do I split this monolith into 5 services or 500 services? What's the right size and the right model for that to work? And it turns out, there's a lot that's involved in deciding where you wanna put a service boundary. And I have a whole process I go through for right-sizing microservices if you will. I call it the Goldilocks process. You know, it's not too small or too big. It's just right. And it's all about the structure of the data, the structure of the code, the size of the team it takes to support the code, and how you build and make that model work as you go along. It's too much to properly get involved in this level of conversation but there's a lot of information there, and a lot of articles I've written on this topic as well too, that talk through the process of how do you decide where to put a boundary into your microservice.
That's number one, I think number two, and in many ways, maybe this is number one is knowing when to stop and when not to stop. I've run into many, many, many companies that have started down the process of building microservices and stopped it halfway in the middle. And this isn't just true with microservices. I've seen it with other migrations as well, cloud migrations, and other things. They find out that the cost is higher than expected, the difficulty is higher than expected.
The problem is, there's not a linear grasp between the amount of effort you put into migration and how much benefit you get out of it. When you put effort into a migration, what happens at first is, you get a negative benefit out of it because you go into this trough, where things are worse for your application than they were before. You've got a monolith that's broken into 10 parts that are all intertwined in weird ways. And I'd say it's more complex and more difficult to manage the situation.
But then you get to a point where you keep investing in the migration and you start seeing the benefits coming out of that. They start outweighing the cost going in. There's a translation point where the benefits outweigh the total cost that you put in. And that's when the migration is a success. But too many people run into that cost of the migration and in the middle decide it's too expensive, they can't complete this, and they stop. They've gone to the point where they've invested money that's wasted and they've made the application and the technical debt in the application worse now than they were before.
I've even seen companies stop at that point and then try and convince everyone that this was a success. You know, we proved this was a hard exercise. So, we got good learnings out of this. And therefore, things are better off because of it. But everyone looking at the mess of code that's available to them says, "There's nothing better about this. This is a bad situation we're in." But they stop investing.
The second piece of advice is to commit to it and commit to completion because there are going to be low points. There are going to be pointed in it where you're convinced it's not going to work. Trust the process and realize that it will work and it will be complete. Maybe it'll be more expensive than you initially thought. That does happen sometimes. But you'll come up with a better world when you're finally done.
Ken Gavranovic: I think I know the answer but are there any key trends that you see of the people who went all the way and the people who stopped in the middle? Is there any secret sauce that if you don't do this, you won't get to the other end?
Lee Atchison: Absolutely, planning is key. Knowing what you wanna do and how you wanna do it is critical. When we're talking specifically about microservice architecture, it’s deciding which direction you're going to go, are you gonna start at the bottom and move up or the top and move down is critical. I usually prefer the bottom up, you know, start with critical components, like user identity and things like that, and then build on top of that versus starting at the user experience and moving down. Have a plan for which direction you wanna go and what the steps along the way are. Dealing with the process changes that are going to be required to make this happen as you go along is also critical. You know, things like CICD processes are nice to have for monoliths. They're essential for microservice architectures. Building those in place before you need them is a critical aspect, focusing on those process and procedure improvements as you go.
And I think the biggest thing is to understand it's an investment. It's taking time away from building new features, yes. But the result is you're gonna be in a better situation and be able to better respond to faster-moving trends, in general. And that's going to make you more responsive and be able to build a better business. But you don't get there overnight.
Ken Gavranovic: I think that makes sense. I think you and I have talked about this. I think making sure the business comes along for the ride and they understand the business benefits like business agility. You just hit on that, I think so spot on.
Lee Atchison: When I was at Amazon, the very first project ever worked on was moving from the Obidos monolithic amazon.com website into their service-oriented architecture. I was one of the managers that drove that migration project. That project worked, it was successful and the result was that the things we were able to work on in the future grew astronomically as a result of that migration. You can just see it on the website. You would go from slow-moving, new features being added occasionally, but not very often. Then we invested in the migration and no new features appeared on the website for a long time, a year, year-and-a-half. And then finally, we finished, and the pent-up demand of all these new capabilities and new trials and tests just magically started appearing and started growing within the organization and visible to the outside world in a very, very, fast manner. And that innovation, that growth was only possible because of that investment we put in to migrate.
The level of management support necessary to do that was huge because the cost of not innovating for some time was extremely high for a company like Amazon, but they saw the investment and they made it work. And this had support up to Jeff Bezos. I mean, Jeff Bezos was involved and knew about the migration going on. I have had conversations with him about it, as we were going through the migration process. And he was one of the first people when we finished the project, to come down at our launch process and thanked us for all of our work. This had huge management support from the very top of the organization down to make these changes. These changes were fundamental to allow the retail organization to continue to innovate, grow, and come up with new ideas as time went on.
Ken Gavranovic: I love that, the business leader getting involved and supporting the team. I think that's so critical. You're usually not quick to share your Amazon experience but thanks for bringing Beanstalk to the world. I know so many companies use that every single day. And that must feel great. Do you mention that in the book? I can't remember.
Lee Atchison: I'm not sure if I mentioned Beanstalk in the book or not but I'll tell you, people don't know when we're recording this, but AWS re: Invent is going on as we speak right now. And just yesterday was Adam Selipsky's first keynote presentation. He was my manager for Beanstalk and he's now running AWS. He went for a 15-year history of AWS. In the early history, he mentioned S3, EC2, RDS, and Beanstalk. And to hear Elastic Beanstalk mentioned on that stage was a neat feeling because that was my baby. That was the thing that they brought me in to help build that product.
And what was innovative about Beanstalk, it isn't a complex service. But it was Amazon's or AWS's first platform as a service versus infrastructure as a service. And it took the existing infrastructure services that were there, which were just a handful of services at the time, and it put a wrapper around them in a way that provides a business value to customers. So instead of having to compute EC2 units and storage units and a database, and just all these random components, you had a web application. And you can make a web application and install it. And we will do all of the infrastructures for you to make all that work. It was an innovative service at the time because it was the first one that did that. Now, a company like AWS has, what, 200, 300 services. Most of them are platform-level or application-level capabilities. But at the time, that was innovative. That was a different direction for AWS to go. It was great to see that mentioned during the keynote.
Beyond the “Architecting at Scale” book ⏤ real-life struggles
Ken Gavranovic: Lee, a lot of people read your book and reach out. I think you worked with a lot of companies now, helping them on these journeys after they've read the book. What are some of the key things where someone read something in the book and is looking to you to go that extra detail of how you implement the concept? What were key themes people learned from the book and that they are reaching out and asking you about?
Lee Atchison: I'll tell you one of the most common things I do with clients is a survey of their processes, their procedures, and less so their code, but the architecture of the application. And I tell them, "Here are places you're gonna run into problems. Here are places that are causing you problems today, whether you're aware of them or not." In some cases, they are aware, they're not. But, sometimes it's just hearing an outsider come in and say, "Yeah you know where the problem is. It's right here." And their response is to say, "Yeah, but it's great to hear that someone else agrees with that or someone who knows what they're talking about agrees with that. That's what we thought would be the issue."
That alone is extremely valuable. So, I would say I spend most of my consulting time doing that sort of analysis. It's like, let me spend a few days talking to you, to your engineers, and your organization, and come up with a list, if you will, or a recommendation of where you really should focus on to improve availability and scalability, in particular, but also general modernization to increase flexibility and growth and all those sorts of things.
Often the results of that are things that you already know are true, but hearing it is good confirmation. Almost exclusively, when I've been able to do that successfully, companies have come back and said, "Yeah, that's changed the direction that we were moving. And we took what you said to light." Maybe even changed staffing as a result of some of my decisions and helped to put better procedures and processes in place to do the right things, to get rid of the technical debt, and to make things go in the right direction.
As far as specific and helpful tools, the risk matrix is probably the number one most important tool that people take from my book and I'll talk about it in my consulting. They'll take that and grow with it. The second one is moving to microservices and plans to make that happen. Third is probably data management, data sizing, and data construction. A lot of people are still falling into the trap of having the database deliver too much work. Let's put all of our data into a single database and use complex database constructs to recall all the data we need as we need them versus localizing data where it's needed, making data part of the service and so you deconstruct your data, just like you deconstruct your service code and see how data is much more manageable that way in a much larger organization. Those sorts of things are probably the biggest things that people take out of what they get out of my book anyway.
Next step: Operating for scale
Ken Gavranovic: That makes a lot of sense. You also talk a little bit about operating for scale, like service level objectives, error budgets. What are some of the key takeaways when you start building a system that's going to operate at a scale that you need to make sure to implement?
Lee Atchison: I always say that availability and scalability are the same things. I firmly believe that because what usually happens is, as you scale, your availability suffers. And as you build an application, it has more and more customers, you end up making decisions that impact availability more than necessary and that has an impact on scalability. So, often, when I'm talking about the operational aspect of things, I'm focused more on the availability aspects than on the scalability access.
So, things like zero-downtime deployments, things like, you know, the security processes to keep bad actors out. I'm not a security expert but security processes and procedures are part of your risk management processes. You have to inject them into that whole process as well. Things like, you know, testing at scale. Believe it or not, I'm a firm believer that unit testing, code-level testing, you know, is less critical than production testing. And I do most of my business, as I recommend to my clients, what I'd recommend to them is to spend most of their time testing with full production in a production environment. Game days. What happens if I pull a data center offline in production? Do things just continue to work or do they break? What happens if I randomly crash computers once an hour across my data center? What happens? Doing that sort of testing, random testing, doing that in production, not in the staging environment, not on a desktop but in a production environment, the net result of doing those sorts of things is you end up with a much higher reliability system and a greater trained staff to be able to deal with problems as they crop up. And the cost to, you know, the initial days of, you know, what happens when I turn off the computer, well, my whole system goes down? Those days do happen at the beginning. But the cost of that is insignificant compared to the savings later when you get to the point where random problems occurred, that you're not planning on and your system's ability to have self-heal self-recover from them. Self-healing is a big aspect of the things I teach from the decoding aspects, and they play into the operational aspects.
So, I'm a huge fan of production level, you know, game day sort of testing, and that being the most valuable testing you do to your system. I hear people focus on test suites but you can't release code unless you write an extensive test suite that goes with that code. Well, most of the time, I could say, I don't care whether you write test suites or not, because, for the most part, most of the time, the types of bugs that are hard to find, hard to identify, and cost your business money aren't the bugs that are caught by a developer-level test suite. They're the bugs caught by production operational environments.
Ken Gavranovic: Now, here are a couple of things. I think, you know, when you're going to services, making sure you have defined inputs and outputs and have the monitoring on that level necessarily versus just the code inside because you might not catch things, but a lot of people I think might confuse game days with a DR exercise because I know, you know, we've talked to many large enterprises and a lot of times, they quote DR or they might mix up with a game day is a tabletop exercise versus a true game day. Can you kind of share the differences from your perspective on what a true game day is and when you're running game days in your organization versus a DR exercise versus a DR tabletop exercise?
Lee Atchison: Sure. Sure. So, when you are running your application production, turning off production level resources or causing them to behave inappropriately, and just seeing what happens without telling people that you're going to do this, without, you know, giving a heads up to your development organization, that is what I mean by game day. That has nothing to do with the formal process of disaster recovery. You know, you can have all the paper plans in the world for what to do when your system fails, and we're gonna roll over to this environment, and we have these levels of backups and all these things work fine. On paper, they can work fine. In reality, they don't. And nothing beats being prepared for a catastrophe than causing a catastrophe to see what happens.
Ken Gavranovic: I love that. And you think about...
Lee Atchison: It sounds crazy to me to be thinking about this. But boy, I would much rather see what happens when I destroy a data center during the day when my staff is around and perhaps at a low traffic time for my website or lower traffic time on my website. I'd much rather do that than to have it happen in the middle of the night when my on-call engineer is just waking up from going to bed two hours ago and is still groggy, and all my customers are complaining. You know, I'd much rather have the former than the latter.
And so, I want to make sure that everything works by testing, that it works, as opposed to doing the paper exercise to see whether it works. And so, disaster recovery planning scenarios that you typically get from... here's my DR report, so uncovered. That paper's worth the price of the paper it's written on and that's really about it because unless you are certain that things are going to work in that scenario by testing it, it's an irrelevant sheet of paper.
So many times, I've seen disaster recovery scenarios fail because somebody forgot something. You know, one of the stories I tell in my book and I tell the story a lot in my talks is I tell the story about an apartment, you know, living in an apartment and having a garage in that apartment. Now, this apartment that I'm living in, has poor quality power. And so, I buy a generator that I can use when the power goes out. And this is part of my disaster planning scenario. If the power goes out, I have a generator, I can still keep my refrigerator working and I don't lose food. So, when I don't need it, though, I put the generator in the garage. The problem is the garage is one of these apartment garages with only the main door, one door, that's the only door into the garage. And it's got a garage door opener on it. And nobody thought to put in a mechanical release lever for this garage door opener that's accessible from the outside. So, when the power goes out to the apartment complex, you can't open the garage door. So, you have this generator to solve the problem when the power goes out but you can't get access to the generator because the power is out.
You know, but you don't think of that until you go through this scenario of turning off the power, setting up the generator, and seeing if it works. And then you see that, oops, there's a flaw in my plan here that doesn't work because I didn't think about how do I open a garage door when there's no power. That step in that process didn't enter the DR playbook but you would notice it instantly when you try it out to see what happens.
So, I use that example a lot. It's a great example, you know, of multiple levels of problems that when... You know, most of the time when you have a major outage, it's not one thing that went wrong. It's two or three or four things in a row that goes wrong. Disaster recovery plans tell you what happens when one thing goes wrong. So, when the power goes out, do this, that's great. When such and such happens, do this, that's great. But they don't talk about what happens when cascade things occur. So, in this example, two problems occurred, the power went out, and nobody thought about what would happen to the garage door when the power went out. So, that combination of those two mistakes together meant that you had a disaster recovery plan that was inaccessible to you.
Ken Gavranovic: Yeah, I always think big outages are always if, if, if. It's like three things happen, and then you have a big issue. I love how you also talk about, like, you know, there are terms like Chaos Monkey, but building in your systems chaos, so it's constantly running, which forces that constant, you know, focus on resiliency by design?
Lee Atchison: Right. Absolutely. Thank you for mentioning Chaos Monkey. You know, the whole idea of having a production environment that is chaotic and filled with unexpected things happening is valuable, not a problem. So, people say, "I want my production to run smooth." No, you don't. You want it to run as chaotically as possible, so that as many things that could go wrong, happen, so you build the processes in to be able to respond to them so that when they happen, and you're not expecting them, that you're able to self-heal and self-recover from them.
You know, if you have a solid, continuous deployment process with a solid ability to back out of a problem, you know, very, very quickly and roll back with the ability to quickly respond and get a new capability out quickly, an outage doesn't have to be a disaster. It doesn't have to require... You know, if you make a mistake in a code and it causes a feature to disappear, well, you roll it back and fix that problem quickly. And you'll know never to do that change again, right? There are all sorts of things you can do that create in that risk...that cause a chaotic production environment. And you need to be able to build defensive code that can live in that environment. And very quickly, if you build code that isn't defensive enough to live in this chaotic environment, you'll learn the moment you deploy it, there's a problem with it. Very, very quickly, because you're gonna hit those corner kind of cases that are causing these problems very, very fast.
And so, you tend to produce higher-quality code, higher-quality releases, more responsive releases, more self-healing releases over time, by having a chaotic production environment. When you don't have a chaotic production environment, when you have a smoothly operating production environment, you get complacent and you start assuming things are always going to be smooth and operate normally. So that when a problem occurs, it becomes a bigger problem, a much more serious problem.
Ken Gavranovic: Lee, in private lives we both run systems that, you know, half-hour outage is $40 million. High pressure. I always think about when you build chaos into your system, like, that gives you peace of mind because you know, the system is constantly under the attack that you created and you know it's resilient by design. So, I always think, like, that gives the team a peace of mind where you're gonna have fewer issues by making that investment, which means less late-night calls, means less, you know, high-priority RCA reports and all that, less D&R plans, all that good stuff.
Lee Atchison: Exactly. Exactly. Yeah. You know, chaos shouldn't be feared. Chaos is value. Chaos is an example to learn. And the more chaos you have, the more you learn, the more capable your system is, the more robust your system is, the more stable it's going to be as time goes on. If your application can run in a chaotic environment, it can run anywhere. If it can run in a smooth environment only, it can only run in a smooth environment.
Ken Gavranovic: Lee, there are so many other things that you can learn and unpack in "Architecting for Scale". I know we don't have that much more time. Anything else that you think we should bring up that, you know, might be a tidbit that a reader can just take and run with and get some value right away?
Lee Atchison: Oh, jeez. We talked about risk management. We've talked about the chaos thing. We haven't talked too much about CICD but, you know, it should be obvious from what we're talking about here that high quality, continuous deployment, and continuous integration environment is critical to running a chaotic production environment that's highly available and highly scaled. You know, companies like Amazon do hundreds and thousands of releases an hour. And there are still customers today, clients today... I had one client, in particular, I was talking to not that long ago, that is still tied into the weekly release process. And the weekly release process, they'll release, every Thursday, whether they need to or not something. And guess what? Those releases are costly. They're problematic. They're undisciplined because they only occur every week, you know. But when you're releasing continuously, you know, hundreds of times a day, you know, then the release process is just an extension of the development process. And you're able to respond and fix things a lot faster.
So, we didn't talk too much about that but I would say, that would be one more thing I would throw into that mix, and make sure that you understand is that that process, that CICD process is essential to maintaining a robust, chaotic environment.
Ken Gavranovic: I agree. And I think that's also a good point as you talked about making the full switch to services because if you're deploying a monolith still or a hybrid Frankenstein monolith, where you half went to services, it's really hard to have like canary deploys or things that are checking and saying that this thing is functioning correctly. And so, it really, I think limits company's ability to have those continuous deployments, you know, the bigger, you know, uglier, bulkier the system is. So, I think to me, it's just a great point of why they need to follow through.
Lee Atchison: Yep. Yep. Yes, exactly. Yeah, you know, you're right. That's one of the common feedbacks with CICD systems is I can't deploy my monolith that fast. It just takes so much effort to do it. I can't automate it because it takes 30 people touching these 20 files all at the same time to make it work. Well, that's the problem, though.
Ken Gavranovic: Hey, and maybe it's so complex that even if I did automate all of those things, I don't know for sure if a particular thing was broken but, like, in your book, if you have services, and they're discreet and they're sized the right way, you know what they're supposed to do. So, you know, if they're functioning or not, and so you can kind of release without fear.
Lee Atchison: Yep.
Ken Gavranovic: And I think that's a good point. Well, Lee, I'm so excited about your next event... Do you have any more books coming out soon or anything you can share? Yeah.
Lee Atchison: Yeah. Yeah, I've got a couple in the process. I've been talking to publishers, but nothing I can easily talk about right now. But stay tuned. There is more coming. But look at my website. Besides my main book, I have a couple of... You know, I've got articles in other O'Reilly books and a couple of reports written, they're part of the O'Reilly ecosystem. And I have a book that I wrote for Redis Labs on caching, that they give away to their customers. And so, take a look at that. So, I've got several books out there. Certainly "Architecting for Scale" is my main one, but there's gonna be a couple of ideas in the works here. There'll be another big one coming out here, if not next year, then very early the year after.
Ken Gavranovic: And Lee, if companies are kind of on that journey, on that mission of transformation, they can go to leeatchinson.com and learn more, and not only will you help them, but you also, once you do it, like, even come with a keynote to explain the benefits, you know, to the organization, to the tech group. You still do that too, right?
Lee Atchison: Absolutely. Yeah, I can... You know, just from the content that I write that can be helpful to you but if you wanna engage me more directly, I can come in. I can give a presentation to your team. I can, you know, do, you know, an educational webinar or seminar to your team or I can do one of these analyze and comment and make recommendation processes. It's a little bit deeper integration or I can do more than that. It all depends on what you're looking for. But absolutely. I just gave a keynote last week to a company that was going through an availability, launching a whole new availability project, a little more than a project and a program that they're working on, and brought me in to talk about availability from my book. And I do those sorts of things all the time.
Ken Gavranovic: Lee, it was great catching up. Look forward to our future videos and I will always love to catch up. But if you haven't seen it yet, definitely pick up "Architecting for Scale". I have it in my book...right behind me and especially the second edition. So, good stuff, Lee. Great talking to you.
Lee Atchison: Thank you, Ken. Great talking to you and I'll talk to you soon.
Ken Gavranovic: All right, take care.
About the authors
Lee Atchison is a software architect, author, public speaker, and recognized thought leader on cloud computing and application modernization.
Ken Gavranovic is a member of Thinkers50 with more than 25 years of experience as a successful Fortune 500 Executive, business owner, trusted advisor, professional trainer, certified executive coach, angel investor, respected community leader and more.