Speaker 1: Welcome to the deep dive. Now you gave us a
Speaker 1: really interesting stack of sources, this time a blueprint really
Speaker 1: for modern enterprise tech. And our mission today is, well,
Speaker 1: let's cut through the jargon. We want to make you
Speaker 1: instantly well informed about cloud native application development. This isn't
Speaker 1: about just dry definitions. It's about understanding the core ideas,
Speaker 1: the philosophy, and the tools that really change software.
Speaker 2: Development, fundamentally changed it.
Speaker 1: We're talking about that shift, you know, from apps that
Speaker 1: took months, maybe years to roll out, oh, to systems
Speaker 1: that demand new features in well days.
Speaker 2: And that need for speed, for constant evolution. That's the
Speaker 2: core driver here. If you look at the sources, the
Speaker 2: big move is crystal clear. It's leaving behind the monolithic
Speaker 2: architecture monoliths.
Speaker 1: We all remember that latency pain, don't we These massive
Speaker 1: applications built as just one single giant unit, indivisible. They
Speaker 1: were hard to scale. We know that, But what was
Speaker 1: the real headache, the biggest pain point organizationally? Maybe?
Speaker 2: Well, yeah, scaling was tough. If one tiny part like
Speaker 2: search on an e commerce site got hammered with traffic, right,
Speaker 2: you had to clone the entire huge application, massive resource waste.
Speaker 2: But honestly, maybe worse was deployment. Developer fixes one tiny
Speaker 2: bug deep in some module. They couldn't just deplay that fix.
Speaker 1: Oh no, you had to test everything.
Speaker 2: Everything, full regression testing on the whole beast. So releases
Speaker 2: were slow, risky, took weeks sometimes to coordinate.
Speaker 1: Okay, So micro services come in to fix that, breaking
Speaker 1: the ab down into the smallest logical level loosely coupled services.
Speaker 2: The key advantage is that granular control selective scaling. Search
Speaker 2: service overloaded find just scale that not the whole thing.
Speaker 2: The order service accounts. Yeah, they just sit there doing
Speaker 2: their job. And there's another cool benefit. The sources highlight polyglot.
Speaker 1: Programming, meaning using different languages.
Speaker 2: Right, use the best tool for the specific job. Maybe
Speaker 2: Python for I don't know, a reporting micro service because
Speaker 2: this data libraries are great, but use Java for your
Speaker 2: high performance transaction service. Get that flexibility.
Speaker 1: So the tech enables breaking things up. But let's be honest,
Speaker 1: this whole shift wouldn't have happened without the money side
Speaker 1: changing too.
Speaker 3: Right, That pay as you go model, Oh absolutely, that's
Speaker 3: the economic engine that really drove cloud adoption, paying only
Speaker 3: for what you actually use and what's really interesting is
Speaker 3: how it lowered the barrier to just trying things out, experimentation.
Speaker 3: How so, well, think back if you needed a powerful
Speaker 3: server just to say, run some sample code and validy fixes. Yeah,
Speaker 3: you had to buy the hardware first, a big capital expense.
Speaker 3: Now you rent a virtual machine, maybe a quad core,
Speaker 3: use it for twenty minutes, shut.
Speaker 4: It down, and pay for twenty minutes exactly.
Speaker 2: Just the time you use it. Completely changes the economics
Speaker 2: of development and testing.
Speaker 1: That ability to just grab resources when needed it must
Speaker 1: have slash overheads. Okay, so we know why the shift happened.
Speaker 1: Let's talk about where the cloud environment itself. The sources
Speaker 1: use this great layer by layer metaphor for the Zauce
Speaker 1: categories ISS PIASAUCE. It gets confusing. So where does my
Speaker 1: responsibility end and the cloud providers begin?
Speaker 2: Right? This hierarchy is key because it defines your cost,
Speaker 2: your effort, and how much control you have. Let's start
Speaker 2: at the bottom. Traditional on premise. Ok. Here you manage
Speaker 2: everything the building, the power, the cooling, the servers, the network,
Speaker 2: the OS, middleware, the app, the data, all of it.
Speaker 2: Your problem.
Speaker 1: Total control, total responsibility. So climbing one step up, ISS
Speaker 1: infrastructure as a service.
Speaker 2: What changes with is think is something like an aws
Speaker 2: EC two instance, a basic virtual machine. The cloud provider
Speaker 2: handles the fundamental infrastructure, the servers, storage, networking, the physical stuff.
Speaker 2: But I still manage You're still responsible for the operating system,
Speaker 2: any middleware, the runtime environment, your application and your data.
Speaker 2: Still quite a bit look after.
Speaker 1: Okay, so PASS platform as a service must take more
Speaker 1: off my clate.
Speaker 2: Then it does significantly more with pass. The cloud provider
Speaker 2: manages the OS, the middleware, and the runtime environments. Ah So,
Speaker 2: as the user, you really only need to focus on
Speaker 2: your application code and its associated data. I think Amazon
Speaker 2: Elastic Beanstock. You just upload your.
Speaker 1: Code and the platform handles the rest, provisioning, scaling pretty much.
Speaker 2: Yeah. It abstracts away a lot of the operational burden.
Speaker 1: And then sas software is as a service is just
Speaker 1: the finished product, like logging into Gmail or Office three
Speaker 1: sixty five.
Speaker 2: Exactly, you're purely a consumer, log in, use the software.
Speaker 2: The provider handles everything else.
Speaker 1: But the cloud didn't stop there, did it. There's face
Speaker 1: functions as a service.
Speaker 2: Right, face like AWS LANDA or Azure functions. This is
Speaker 2: like the ultimate level of abstraction. The smallest unit.
Speaker 1: Your smallest unit.
Speaker 2: Yeah, you only manage the function itself, the actual snippet
Speaker 2: of code. The cloud handles everything else, provisioning servers, scaling up,
Speaker 2: scaling down, even scaling to zero when it's not being used,
Speaker 2: and the billing reflects that precisely. Your charge only for
Speaker 2: the exact time your code is running. If it runs
Speaker 2: for say, thirty milliseconds, you pay for thirty milliseconds. That
Speaker 2: efficiency is just game changing.
Speaker 1: Okay, that clarifies the layers the what. But here's the
Speaker 1: crucial part. The mindset shift. Just lifting and shifting your
Speaker 1: old monolith onto an IASVM. That doesn't really unlock the
Speaker 1: cloud's power, does it not at all?
Speaker 2: That's just running your old problems in a new location.
Speaker 1: Right. The sources really stress this. Building true cloud native
Speaker 1: apps means unlearning old habits. You have to design for well.
Speaker 4: Volatility absolutely.
Speaker 2: Cloud native design assumes failure is normal. The old monolith
Speaker 2: mindset was, you know, the server's precious, keep it running
Speaker 2: at all costs. In the cloud, servers are cattle nut pets.
Speaker 2: They're disposable. Components will fail.
Speaker 1: Which brings us right to design factor one, embrace failure. Specifically,
Speaker 1: no single point of failure.
Speaker 2: Correct, and the cornerstone of building resilient systems like this
Speaker 2: is statelessness.
Speaker 1: Okay, statelessness? What does that mean in practice? Give us
Speaker 1: an analogy.
Speaker 2: Okay, think about ordering food. Right, a stateful restaurant, only
Speaker 2: the waiter who took your order knows what you ordered
Speaker 2: or where you are in your meal. If that specific
Speaker 2: waiter goes home, you're stuck. Your state is lost with them, right, I.
Speaker 1: Can see that annoying.
Speaker 2: Very Now a stateless restaurant, your order is written down,
Speaker 2: maybe put into a central system. Any available waiter, any
Speaker 2: server instance can look up your order details and continues
Speaker 2: serving you seamlessly.
Speaker 1: Ah, The system knows, not the individual server exactly.
Speaker 2: The service itself doesn't hold onto session memory between requests
Speaker 2: the state. The data is stored elsewhere, maybe a database
Speaker 2: or cash accessible to all instances. That's what lets you
Speaker 2: easily add a remove server's horizontal scaling. You can have
Speaker 2: one hundred identical interchangeable servers.
Speaker 1: That makes total sense. Okay, So failure is inevitable. The
Speaker 1: system needs to not just survive it, but handle it gracefully.
Speaker 2: Yeah, yes, fail fast, that's crucial, often done using the
Speaker 2: circuit breaker pattern.
Speaker 1: Circuit breaker like in my house sort of.
Speaker 2: You don't want a struggling service to just hang silent
Speaker 2: feeling for minutes, causing backups everywhere else. If a downstream
Speaker 2: service is clearly having trouble, got it off exactly, the
Speaker 2: circuit breaker trips. It stops sending requests that failing service
Speaker 2: for a short period, preventing overload and giving it a
Speaker 2: chance to recover or be replaced. Then importantly, the calling
Speaker 2: service needs to handle that failure gracefully, meaning don't just
Speaker 2: show an error page. If live searches down, maybe pull
Speaker 2: results from a cash or show top selling products, something useful,
Speaker 2: not just a dead end.
Speaker 1: Okay, resilience is built on statelessness and failing fast. But
Speaker 1: if we have potentially hundreds of these small services, managing
Speaker 1: that manually sounds impossible, which leads to design factor two.
Speaker 4: Automation is king absolutely non negotiable.
Speaker 2: With potentially hundreds of micro services, manual management is a
Speaker 2: recipe for disaster. You must automate testing, deployment, that's your
Speaker 2: CICD pipeline.
Speaker 1: And monitoring to eliminate human error, that and just.
Speaker 2: To cope with the scale. This automation is also key
Speaker 2: to building a self healing system self healing like Wolverine. Huh, Well,
Speaker 2: maybe not quite that fast, but the system needs to
Speaker 2: automatically detect and recover from failures without needing a human
Speaker 2: to step in. That could mean Kubernetes automatically restarting a
Speaker 2: failed container instance.
Speaker 1: Or redirecting traffic.
Speaker 2: Or spinning up more instances at the load increases the
Speaker 2: system manages itself.
Speaker 1: Ideally, and for developers actually building these things. The sources
Speaker 1: kept mentioning the twelve factor app philosophy is that like
Speaker 1: a checklist, it's more.
Speaker 2: Set of principles. Yeah. A widely accepted guide for building robust,
Speaker 2: scalable services for the cloud. It covers things like how
Speaker 2: to handle configuration, logs, dependencies.
Speaker 1: What's a key benefit of following those rules?
Speaker 2: Standardization and predictability. Take configuration for example, Factor three says
Speaker 2: stork and figure in the environment, not in the code.
Speaker 1: Why is that so important?
Speaker 2: Because then the exact same compiled code artifact can be
Speaker 2: deployed unchanged across development, test, staging, production. You just change
Speaker 2: the environment variables for database connections, apikeys, et cetera. It
Speaker 2: makes deployments much faster and safer, eliminates a huge source
Speaker 2: of errors.
Speaker 1: Got it? Okay, so we have the design mindset, embrace failure,
Speaker 1: automate everything. Now let's connect that to the toolkit. What
Speaker 1: technologies actually make this happen? How do we deploy and
Speaker 1: run these things?
Speaker 2: Right? The implementation, It really starts with containers. We mentioned
Speaker 2: VM's being heavy.
Speaker 1: Because they include a full OS.
Speaker 2: Right, containers are way lighter. They allow for much greater density.
Speaker 1: Remind us why they're lighter again, what's the core difference?
Speaker 2: They share the host operating system's kernel, so instead of
Speaker 2: each app needing its own complete OS like a VM does.
Speaker 1: Like separate cars with engines, Yeah.
Speaker 2: Containers are more like everyone sharing the car's engine, the
Speaker 2: host to S kernel, but each having their own secure
Speaker 2: passenger cabin built around. You're just packaging the application and
Speaker 2: its dependencies, not the whole OS stack.
Speaker 1: So you can pack way more onto the same hardware,
Speaker 1: more efficient, faster.
Speaker 2: To start up, exactly, huge boost and agility. But then
Speaker 2: if you've got tens, maybe hundreds or thousands of these containers,
Speaker 2: you need management, air traffic control.
Speaker 1: Basically, and that's Kubernetes.
Speaker 2: That's Kubernetes, or k eights as it's often called It's
Speaker 2: become the de facto standard for container orchestration orchestration, meaning
Speaker 2: it automates the deployment, scaling, load balancing, and crucially, the
Speaker 2: healing of containerized applications across a cluster of machines. If
Speaker 2: a container running your service crashes.
Speaker 1: Kates, notices and starts a new one.
Speaker 2: Yep, automatically, it handles that complexity so developers can focus
Speaker 2: on code, not infrastructure babysitting.
Speaker 1: Okay, containers managed by KAS We mentioned speed earlier that
Speaker 1: comes from continuous integration and continuous delivery CICD Right.
Speaker 2: CICD pipelines ensure that your code is constantly being built,
Speaker 2: tested and made ready for deployment. This enables those frequent, small,
Speaker 2: low risk releases we talked about instead of massive, scary
Speaker 2: deployments once a quarter.
Speaker 1: You deploy small changes, maybe multiple times a day.
Speaker 2: That's the goal. But how do you make sure the
Speaker 2: underlying infrastructure, the Kubernetes cluster itself, the networking, the databases
Speaker 2: is set up correctly and consistently every single time?
Speaker 1: Good question. Manual setup seems error prone.
Speaker 2: Highly error prone. That's where infrastructure as code or IAC
Speaker 2: is absolutely viable.
Speaker 1: Infrastructure as code yeah.
Speaker 2: Instead of clicking around in a cloud provider's web console
Speaker 2: to set things up, which is slow and impossible to
Speaker 2: replicate perfectly, you write.
Speaker 1: Scripts scripts that define the infrastructure.
Speaker 2: Exactly, using tools like AWS cloud Formation as your ARM
Speaker 2: templates or Terraform. These scripts define your servers, networks, load balancers, everything.
Speaker 2: You check this code in diversion control just like your
Speaker 2: application code.
Speaker 1: Ah. So it's repeatable, auditable, and testable.
Speaker 2: You can spin up an entire identical environment for development, testing,
Speaker 2: or production just by running the script. It eliminates configuration
Speaker 2: drift and the classic well it worked on my machine problem?
Speaker 1: Okay, that consistency is key. Now we have hundreds of
Speaker 1: services automated deployment. How do we avoid the pay as
Speaker 1: you go model becoming pay way too much? How do
Speaker 1: we manage costs and spot problems?
Speaker 2: That comes down to proactive monitoring and alerting. It's not optional,
Speaker 2: it's essential.
Speaker 1: Not just for finding bugs.
Speaker 2: No, it's critical for cost management too. You need visibility
Speaker 2: into how all these services are behaving. Our resources being underutilized,
Speaker 2: that's wasted money. Are they being overutilized that risks, performance
Speaker 2: issues are failures.
Speaker 1: So you need centralized logging and metrics absolutely.
Speaker 2: Tools like AWS, cloud Watch or open source stacks like
Speaker 2: the ELK stack, Elastic Search, log Stash, Cubana are common.
Speaker 2: They gather logs and metrics from all your micro services
Speaker 2: into one.
Speaker 4: Place so you can see the whole picture and set up.
Speaker 2: Alerts for anomalies, errors, high latency, unused resource consumption so
Speaker 2: you can react before it impacts users or your bill.
Speaker 1: Makes sense. One last piece security Moving from one big
Speaker 1: monolith to hundreds of distributed services must change the security
Speaker 1: game completely.
Speaker 2: It absolutely does. You can't just put a big firewall
Speaker 2: or on the monolith anymore. Cloud data security relies heavily
Speaker 2: on two things. Fine grained access control and network segmentation.
Speaker 1: Okay, break those down.
Speaker 2: Role based access control or RBAC IM and AWS is
Speaker 2: about who can do what principle of least privilege. Users
Speaker 2: or even services themselves only get the absolute minimum permissions
Speaker 2: they need to function. No more generic admin keys floating around.
Speaker 1: And network segmentation that's about.
Speaker 2: Controlling what can talk to what You use virtual networks, subnets,
Speaker 2: security groups essentially internal firewalls to isolate services. The Bayman
Speaker 2: service should only be allowed to talk to the specific
Speaker 2: database that needs and maybe the order service, it shouldn't
Speaker 2: be able to reach the user profile service, for.
Speaker 1: Example, inintaining the blast radius. If something gets compromised.
Speaker 2: Exactly zero, trust principles become much more important.
Speaker 1: Wow. Okay, so looking back, it's quite a journey. We
Speaker 1: went from the monolithic bottleneck to these nimble micro services.
Speaker 1: We navigated the ZIAS models, the pay as you go economics,
Speaker 1: and critically adopted this design mindset focused on resilience, statelessness, automation,
Speaker 1: assuming failure.
Speaker 2: And I think the biggest takeaway really, the thing that
Speaker 2: should guide anyone starting this journey or refining it, is
Speaker 2: that proactive planning right at the design phase. That's what
Speaker 2: saves you those thousands of dollars and man hours down
Speaker 2: the line.
Speaker 1: You can't just tack this stuff on later.
Speaker 2: No, you really can't. Trying to retrofit cloud native ideas
Speaker 2: onto an old monolithic design, it's usually painful and often fails.
Speaker 2: You have to build it in from day.
Speaker 1: One, right, which brings us nicely to our final thought
Speaker 1: for you, the listener to ponder, We talked a lot
Speaker 1: about designing for failure, and the sources mentioned this powerful concept,
Speaker 1: the bulkhead pattern.
Speaker 4: Ah Yes, ship analogy exactly.
Speaker 1: A bulkhead is that watertight wall inside a ship's hull.
Speaker 1: If there's a leak or a fire in one compartment.
Speaker 2: The bulkhead contains it. It stops the disaster from spreading
Speaker 2: and sinking the whole ship. The rest of the vessel
Speaker 2: stays operational.
Speaker 1: So the question for you is where in your business,
Speaker 1: in your systems, in your organization can you deliberately apply
Speaker 1: that bulkhead pattern.
Speaker 2: If one part fails, a key service, a critical process,
Speaker 2: maybe even a team. Have you built the walls, Have
Speaker 2: you designed the isolation points, the statelessness, the network segmentation,
Speaker 2: the circuit breakers, the automation to ensure that failure is contained.
Speaker 1: So that the core mission, the essential operations, can continue
Speaker 1: moving forward even when something inevitably breaks.
Speaker 2: That's the challenge thinking about resilience, not just in code,
Speaker 2: but across the whole system.
Speaker 1: Definitely something to mull over as you apply these cloud
Speaker 1: native concepts. Thank you for joining us for this deep dive.