CloudComputing

How to stay employable when the tools keep changing

I was at my desk the other day attempting to achieve what passes for serenity in modern IT, which is to say I was watching a Kubernetes cluster behave like a supermarket trolley with one cursed wheel. Everything looked stable in the dashboard, which, in cloud terms, is the equivalent of a toddler saying “I am being very quiet” from the other room.

That was when a younger colleague appeared at the edge of my monitor like a pop-up window you simply cannot close.

“Can I ask you something?” he said.

This phrase is rarely followed by useful inquiries, such as “Where do you keep the biscuits?” It is invariably followed by something philosophical, the kind of question that makes you suddenly aware you have become the person other people treat as a human FAQ.

“Is it worth it?” he asked. “All of this. The studying. The certifications. The on-call shifts. With AI coming to take it all away.”

He did not actually use the phrase “robot overlords”, but it hung in the air anyway, right beside that other permanent office presence, the existential dread that arrives every Monday morning and sits down without introducing itself.

Being “senior” in the technology sector is a funny thing. It is not like being a wise mountain sage who understands the mysteries of the wind. It is more like being the only person in the room who remembers what the internet looked like before it became a shopping mall with a comment section. You are not necessarily smarter. You are simply older, and you have survived enough migrations to know that the universe is largely held together by duct tape and misunderstood configuration files.

So I looked at him, panicked slightly, and decided to tell him the truth.

The accidental trap of the perfect puzzle piece

The problem with the way we build careers, especially in engineering, is that we treat ourselves like replacement parts for a very specific machine. We spend years filing down our edges, polishing our corners, and making sure we fit perfectly into a slot labelled “Java Developer” or “Cloud Architect.”

This strategy works wonderfully right up until the moment the machine decides to change its shape.

When that happens, being a perfect puzzle piece is actually a liability. You are left holding a very specific shape in a world that has suddenly decided it prefers round holes. This brings us to the trap of the specialist. The specialist is safe, comfortable, and efficient. But the specialist is also the first thing to be replaced when the algorithm learns how to do the job faster.

The alternative sounds exhausting. It is the path of the “Generalist.”

To a logical brain that enjoys defined parameters, a generalist looks suspiciously like someone who cannot make up their mind. But in the coming years, the generalist (confusing as they may be) is the only one safe from extinction. The generalist does not ask “Where do I fit?” The generalist asks, “What am I trying to build?” and then learns whatever is necessary to build it. It is less like being a factory worker and more like being a frantic homeowner trying to fix a leak with a roll of tape and a YouTube video. It is messy, but unlike the factory worker, the homeowner cannot be automated out of existence because the problems they solve are never exactly the same twice.

The four horsemen of the career apocalypse

Once you accept that the future will not reward narrow excellence, you stumble upon an equally alarming discovery regarding the skills that actually matter. The usual list tends to circle around four eternal pillars known to induce hives in most engineers: marketing, sales, writing, and speaking.

If you work in DevOps or cloud, these words likely land with the gentle comfort of a cold spoon sliding down your back. We tend to view marketing and sales as the parts of the economy where people smile too much and perhaps use too much hair gel. Writing and public speaking, meanwhile, are often just painful reminders of that time we accidentally said “utilize” in a meeting when “use” would have sufficed.

But here is a useful reframing I have been trying to adopt.

Marketing and sales are not trickery. They are simply “the message“. They are the ability to explain to another human being why something matters. If you have ever tried to convince a Product Manager that technical debt is real and dangerous, you have done sales. If you failed, it was likely because your marketing was poor.

Writing and speaking are not performance art. They are “the medium“. In a world where AI can generate code in seconds, the ability to write clean code becomes less valuable than the ability to write a clean explanation of why we need that code. The modern career is increasingly about communicating value rather than just quietly creating it in a dark room. The “Artist” focuses on the craft. The “Sellout” focuses on the money. The goal, irritating as it may be, is to become the “Artist-Entrepreneur” who respects the craft enough to sell it properly.

The museum of ideas and the art of dissatisfaction

So how does one actually prepare for this vaguely threatening future?

The advice usually involves creating a “Vision Board” with pictures of yachts and people laughing at salads. I have always found this difficult, mostly because my vision usually extends no further than wanting my printer to work on the first try.

A far more effective tool is the “Anti-vision“.

This involves looking at the life you absolutely do not want and running in the opposite direction. It is a powerful motivator. I can quite easily visualize a future of endless Zoom meetings where we discuss the synergy of leverage, and that vision propels me to learn new skills faster than any promise of a Ferrari ever could.

This leads to the concept of curating a “Museum of Ideas”. You do not need to be a genius inventor. You just need to be a curator. You collect the ideas, people, and concepts that resonate with you, and you try to figure out why they work. It is reverse engineering, which is something we are actually good at. We do it with software all the time. Doing it with our careers feels strange, but the logic holds. You look at the result you want, and you work backward to find the source code.

This process requires you to embrace a certain amount of boredom and dissatisfaction. We usually treat boredom as a bug in the system, something to be patched immediately with scrolling or distraction. But boredom is actually a feature. It is the signal that it is time to evolve. AI does not get bored. It will happily generate generic emails until the heat death of the universe. Only a human gets bored enough to invent something better.

The currency of confidence

So, back to the colleague at my desk, who was still looking at me with the expectant face of a spaniel waiting for a treat.

I told him that yes, it is worth it. But the game has changed.

We are moving from an economy of “knowing things” (which computers do better) to an economy of “connecting things” (which is still a uniquely human mess). The future belongs to the people who can see the whole system, not just the individual lines of code.

When the output of AI becomes abundant and cheap, the value shifts to confidence. Not the loud, arrogant confidence of a television pundit, but the quiet confidence of someone who understands the trade-offs. Employers and clients will not pay you for the code; they will pay you for the assurance that this specific code is the right solution for their specific, messy reality. They pay for taste. They pay for trust.

If the robots are indeed coming for our jobs, the safest position is not to stand guard over one tiny task. It is to become the person who can see the entire ridiculous machine, spot the real problem, and explain it in plain English while everyone else is still arguing about which dashboard is lying.

That, happily, remains a very human talent.

Now, if you will excuse me, I have to start building my museum of ideas right after I figure out why my Linux kernel has decided to panic-dump in the middle of an otherwise peaceful afternoon. I suspect it, too, has been reading about the future and just wanted to feel something.

January 8, 2026 by Computer Science stuff DevOps stuff

Microservices are the architectural equivalent of a midlife crisis

Someone in a zip-up hoodie has just told you that monoliths are architectural heresy. They insist that proper companies, the grown-up ones with rooftop terraces and kombucha taps in the breakroom, build systems the way squirrels store acorns. They describe hundreds of tiny, frantic caches scattered across the forest floor, each with its own API, its own database, and its own emotional baggage.

You stand there nodding along while holding your warm beer, feeling vaguely inadequate. You hide the shameful secret that your application compiles in less time than it takes to brew a coffee. You do not mention that your code lives in a repository that does not require a map and a compass to navigate. Your system runs on something scandalously simple. It is a monolith.

Welcome to the cult of small things. We have been expecting you, and we have prepared a very complicated seat for you.

The insecurity of the monolithic developer

The microservices revolution did not begin with logic. It began with envy. It started with a handful of very successful case studies that functioned less like technical blueprints and more like impossible beauty standards for teenagers.

Netflix streams billions of hours of video. Amazon ships everything from electric toothbrushes to tactical uranium (probably) to your door in two days. Their systems are vast, distributed, and miraculous. So the industry did what any rational group of humans would do. We copied their homework without checking if we were taking the same class.

We looked at Amazon’s architecture and decided that our internal employee timesheet application needed the same level of distributed complexity as a global logistics network. This is like buying a Formula 1 pit crew to help you parallel park a Honda Civic. It is technically impressive, sure. But it is also a cry for help.

Suddenly, admitting you maintained a monolith became a confession. Teams began introducing themselves at conferences by stating their number of microservices, the way bodybuilders flex biceps, or suburban dads compare lawn mower horsepower. “We are at 150 microservices,” someone would say, and the crowd would murmur approval. Nobody thought to ask if those services did anything useful. Nobody questioned whether the team spent more time debugging network calls than writing features.

The promise was flexibility. The reality became a different kind of rigidity. We traded the “spaghetti code” of the monolith for something far worse. We built a distributed bowl of spaghetti where the meatballs are hosted on different continents, and the sauce requires a security token to touch the pasta.

Debugging a murder mystery where the body keeps moving

Here is what the brochures and the medium articles do not mention. Debugging a monolith is straightforward. You follow the stack trace like a detective following footprints in the snow.

Debugging a distributed system, however, is less like solving a murder mystery and more like investigating a haunting. The evidence vanishes. The logs are in different time zones. Requests pass through so many services that by the time you find the culprit, you have forgotten the crime.

Everything works perfectly in isolation. This is the great lie of the unit test. Your service A works fine. Your service B works fine. But when you put them together, you get a Rube Goldberg machine that occasionally processes invoices but mostly generates heat and confusion.

To solve this, we invented “observability,” which is a fancy word for hiring a digital private investigator to stalk your own code. You need a service discovery tool. Then, a distributed tracing library. Then a circuit breaker, a bulkhead, a sidecar proxy, a configuration server, and a small shrine to the gods of eventual consistency.

Your developer productivity begins a gentle, heartbreaking decline. A simple feature, such as adding a “middle name” field to a user profile, now requires coordinating three teams, two API version bumps, and a change management ticket that will be reviewed next Thursday. The context switching alone shaves IQ points off your day. You have solved the complexity of the monolith by creating fifty mini monoliths, each with its own deployment pipeline and its own lonely maintainer who has started talking to the linter.

Your infrastructure bill is now a novelty item

There is a financial aspect to this midlife crisis. In the old days, you rented a server. Maybe two. You paid a fixed amount, and the server did the work.

In the microservices era, you are not just paying for the work. You are paying for the coordination of the work. You are paying for the network traffic between the services. You are paying for the serialization and deserialization of data that never leaves your data center. You are paying for the CPU cycles required to run the orchestration tools that manage the containers that hold the services that do the work.

It is an administrative tax. It is like hiring a construction crew where one guy hammers the nail, and twelve other guys stand around with clipboards coordinating the hammering angle, the hammer velocity, and the nail impact assessment strategy.

Amazon Prime Video found this out the hard way. In a move that shocked the industry, they published a case study detailing how they moved from a distributed, serverless architecture back to a monolithic structure for one of their core monitoring services.

The results were not subtle. They reduced their infrastructure costs by 90 percent. That is not a rounding error. That is enough money to buy a private island. Or at least a very nice yacht. They realized that sending video frames back and forth between serverless functions was the digital equivalent of mailing a singular sock to yourself one at a time. It was inefficient, expensive, and silly.

The myth of infinite scalability

Let us talk about that word. Scalability. It gets whispered in architectural reviews like a magic spell. “But will it scale?” someone asks, and suddenly you are drawing boxes and arrows on a whiteboard, each box a little fiefdom with its own database and existential dread.

Here is a secret that might get you kicked out of the hipster coffee shop. Most systems never see the traffic that justifies this complexity. Your boutique e-commerce site for artisanal cat toys does not need to handle Black Friday traffic every Tuesday. It could likely run on a well-provisioned server and a prayer. Using microservices for these workloads is like renting an aircraft hangar to store a bicycle.

Scalability comes in many flavors. You can scale a monolith horizontally behind a load balancer. You can scale specific heavy functions without splitting your entire domain model into atomic particles. Docker and containers gave us consistent deployment environments without requiring a service mesh so complex that it needs its own PhD program to operate.

The infinite scalability argument assumes you will be the next Google. Statistically, you will not. And even if you are, you can refactor later. It is much easier to slice up a monolith than it is to glue together a shattered vase.

Making peace with the boring choice

So what is the alternative? Must we return to the bad old days of unmaintainable codeballs?

No. The alternative is the modular monolith. This sounds like an oxymoron, but it functions like a dream. It is the architectural equivalent of a sensible sedan. It is not flashy. It will not make people jealous at traffic lights. But it starts every morning, it carries all your groceries, and it does not require a specialized mechanic flown in from Italy to change the oil.

You separate concerns inside the same codebase. You make your boundaries clear. You enforce modularity with code structure rather than network latency. When a module truly needs to scale differently, or a team truly needs autonomy, you extract it. You do this not because a conference speaker told you to, but because your profiler and your sprint retrospectives are screaming it.

Your architecture should match your team size. Three engineers do not need a service per person. They need a codebase they can understand without opening seventeen browser tabs. There is no shame in this. The shame is in building a distributed system so brittle that every deploy feels like defusing a bomb in an action movie, but without the cool soundtrack.

Epilogue

Architectural patterns are like diet fads. They come in waves, each promising total transformation. One decade, it is all about small meals, the next it is intermittent fasting, the next it is eating only raw meat like a caveman.

The truth is boring and unmarketable. Balance works. Microservices have their place. They are essential for organizations with thousands of developers who need to work in parallel without stepping on each other’s toes. They are great for systems that genuinely have distinct, isolated scaling needs.

For everything else, simplicity remains the ultimate sophistication. It is also the ultimate sanity preserver.

Next time someone tells you monoliths are dead, ask them how many incident response meetings they attended this week. The answer might be all the architecture review you need.

(Footnote: If they answer “zero,” they are either lying, or their pager duty alerts are currently stuck in a dead letter queue somewhere between Service A and Service B.)

January 6, 2026 by Cloud stuff Computer Science stuff DevOps stuff SRE stuff

The secret and anxious life of a data packet inside AWS

You press a finger against the greasy glass of your smartphone. You are in a café in Melbourne, the coffee is lukewarm, and you have made the executive decision to watch a video of a cat falling off a Roomba. It feels like a trivial action.

But for the data packet birthed by that tap, this is D-Day.

It is a tiny, nervous backpacker being kicked out into the digital wilderness with nothing but a destination address and a crippling fear of latency. Its journey through Amazon’s cloud infrastructure is not the clean, sterile diagram your systems architect drew on a whiteboard. It is a micro drama of hope, bureaucratic routing, and existential dread that plays out in roughly 200 milliseconds.

We tend to think of the internet as a series of tubes, but it is more accurate to think of it as a series of highly opinionated bouncers and overworked bureaucrats. To understand how your cat video loads, we have to follow this anxious packet through the gauntlet of Amazon Web Services (AWS).

The initial panic and the mapmaker with a god complex

Our packet leaves your phone and hits the cellular network. It is screaming for directions. It needs to find the server hosting the video, but it only has a name (e.g., cats.example.com). Computers do not speak English; they speak IP addresses.

Enter Route 53.

Amazon calls Route 53 a Domain Name System (DNS) service. In practice, it acts like a travel agent with a philosophy degree and multiple personality disorder. It does not just look up addresses; it judges you based on where you are standing and how healthy the destination looks.

If Route 53 is configured with Geolocation Routing, it acts like a local snob. It looks at our packet’s passport, sees “Melbourne,” and sneers. “You are not going to the Oregon server. The Americans are asleep, and the latency would be dreadful. You are going to Sydney.”

However, Route 53 is also a hypochondriac. Through Health Checks, it constantly pokes the servers to see if they are alive. It is the digital equivalent of texting a friend, “Are you awake?” every ten seconds. If the Sydney server fails to respond three times in a row, Route 53 assumes the worst, death, fire, or a kernel panic, and instantly reroutes our packet to Singapore. This is Failover Routing, the prepared pessimist of the group.

The packet doesn’t care about the logic. It just wants an address so it can stop hyperventilating in the void.

CloudFront is the desperate golden retriever of the internet

Armed with an IP address, our packet rushes toward the destination. But hopefully, it never actually reaches the main server. That would be inefficient. Instead, it runs into CloudFront.

CloudFront is a Content Delivery Network (CDN). Think of it as a network of convenience stores scattered all over the globe, so you don’t have to drive to the factory to buy milk. Or, more accurately, think of CloudFront as a Golden Retriever that wants to please you so badly it is vibrating.

Its job is caching. It memorizes content. When our packet arrives at the CloudFront “Edge Location” in Melbourne, the service frantically checks its pockets. “Do I have the cat video? I think I have the cat video. I fetched it for that guy in the corner five minutes ago!”

If it has the video (a Cache Hit), it hands it over immediately. The packet is relieved. The journey is over. Everyone goes home happy.

But if CloudFront cannot find the video (a Cache Miss), the mood turns sour. The Golden Retriever looks guilty. It now has to turn around and run all the way to the origin server to fetch the data fresh. This is the “Edge” of the network, a place that sounds like a U2 guitarist but is actually just a rack of humming metal in a secure facility near the airport.

The tragedy of CloudFront is the Time To Live (TTL). This is the expiration date on the data. If the TTL is set to 24 hours, CloudFront will proudly hand you a version of the website from yesterday, oblivious to the fact that you updated the spelling errors this morning. It is like a dog bringing you a dead bird it found last week, convinced it is still a great gift.

The security guard who judges your shoes

If our packet suffers a Cache Miss, it must travel deeper into the data center. But first, it has to get past the Web Application Firewall (WAF).

The WAF is not a firewall in the traditional sense; it is a nightclub bouncer who has had a very long shift and hates everyone. It stands at the velvet rope, scrutinizing every packet for signs of “malicious intent.”

It checks for SQL injection, which is the digital equivalent of trying to sneak a knife into the club tape-draped to your ankle. It checks for Cross-Site Scripting (XSS), which is essentially trying to trick the club into changing its name to “Free Drinks for Everyone.”

The WAF operates on a set of rules that range from reasonable to paranoid. Sometimes, it blocks a legitimate packet just because it looks suspicious, perhaps the packet is too large, or it came from a country the WAF has decided to distrust today. The packet pleads its innocence, but the WAF is a piece of software code; it does not negotiate. It simply returns a 403 Forbidden error, which translates roughly to: “Your shoes are ugly. Get out.”

The Application Load Balancer manages the VIP list

Having survived the bouncer, our weary packet arrives at the Application Load Balancer (ALB). If the WAF is the bouncer, the ALB is the Maitre D’ holding the clipboard.

The ALB is obsessed with fairness and health. It stands in front of a pool of identical servers (the Target Group) and decides who has to do the work. It is trying to prevent any single server from having a nervous breakdown due to overcrowding.

“Server A is busy processing a login request,” the ALB mutters. “Server B is currently restarting because it had a panic attack. You,” it points to our packet, “you go to Server C. It looks bored.”

The ALB’s relationship with the servers is codependent and toxic. It performs health checks on them relentlessly. It demands a 200 OK status code every thirty seconds. If a server takes too long to reply or replies with an error, the ALB declares it “Unhealthy” and stops sending it friends. It effectively ghosts the server until it gets its act together.

The Origin, where the magic (and heat) happens

Finally, the packet reaches the destination. The Origin.

We like to imagine the cloud as an ethereal, fluffy place. In reality, the Origin is likely an EC2 instance, a virtual slice of a computer sitting in a windowless room in Northern Virginia or Dublin. The room is deafeningly loud with the sound of cooling fans and smells of ozone and hot plastic.

Here, the application code actually runs. The request is processed, and the server realizes it needs the actual video file. It reaches out to Amazon S3 (Simple Storage Service), which is essentially a bottomless digital bucket where the internet hoards its data.

The EC2 instance grabs the video from the bucket, processes it, and prepares to send it back.

This is the most fragile part of the journey. If the code has a bug, the server might vomit a 500 Internal Server Error. This is the server saying, “I tried, but I broke something inside myself.” If the database is overwhelmed, the request might time out.

When this happens, the failure cascades back up the chain. The ALB shrugs and tells the user “502 Bad Gateway” (translation: ” The guy in the back room isn’t talking to me”). The WAF doesn’t care. CloudFront caches the error page, so now everyone sees the error for the next hour.

And somewhere, a DevOps engineer’s phone starts buzzing at 3:00 AM.

The return trip

But today, the system works. The Origin retrieves the video bytes. It hands them to the ALB, which passes them to the WAF (who checks them one last time for contraband), which hands them to CloudFront, which hands them to the cellular network.

The packet returns to your phone. The screen flickers. The cat falls off the Roomba. You chuckle, swipe up, and request the next video.

You have no idea that you just forced a tiny, digital backpacker to navigate a global bureaucracy, evade a paranoid security guard, and wake up a server in a different hemisphere, all in less time than it takes you to blink. It is a modern marvel held together by fiber optics and anxiety.

So spare a thought for the data. It has seen things you wouldn’t believe.

November 23, 2025 by Cloud stuff DevOps stuff SRE stuff

Playing detective with dead Kubernetes nodes

It arrives without warning, a digital tap on the shoulder that quickly turns into a full-blown alarm. Maybe you’re mid-sentence in a meeting, or maybe you’re just enjoying a rare moment of quiet. Suddenly, a shriek from your phone cuts through everything. It’s the on-call alert, flashing a single, dreaded message: NodeNotReady.

Your beautifully orchestrated city of containers, a masterpiece of modern engineering, now has a major power outage in one of its districts. One of your worker nodes, a once-diligent and productive member of the cluster, has gone completely silent. It’s not responding to calls, it’s not picking up new work, and its existing jobs are in limbo. In the world of Kubernetes, this isn’t just a technical issue; it’s a ghosting of the highest order.

Before you start questioning your life choices or sacrificing a rubber chicken to the networking gods, take a deep breath. Put on your detective’s trench coat. We have a case to solve.

First on the scene, the initial triage

Every good investigation starts by surveying the crime scene and asking the most basic question: What the heck happened here? In our world, this means a quick and clean interrogation of the Kubernetes API server. It’s time for a roll call.

kubectl get nodes -o wide

This little command is your first clue. It lines up all your nodes and points a big, accusatory finger at the one in the Not Ready state.

NAME                    STATUS     ROLES    AGE   VERSION   INTERNAL-IP      EXTERNAL-IP     OS-IMAGE             KERNEL-VERSION      CONTAINER-RUNTIME
k8s-master-1            Ready      master   90d   v1.28.2   10.128.0.2       34.67.123.1     Ubuntu 22.04.1 LTS   5.15.0-78-generic   containerd://1.6.9
k8s-worker-node-7b5d    NotReady   <none>   45d   v1.28.2   10.128.0.5       35.190.45.6     Ubuntu 22.04.1 LTS   5.15.0-78-generic   containerd://1.6.9
k8s-worker-node-fg9h    Ready      <none>   45d   v1.28.2   10.128.0.4       35.190.78.9     Ubuntu 22.04.1 LTS   5.15.0-78-generic   containerd://1.6.9

There’s our problem child: k8s-worker-node-7b5d. Now that we’ve identified our silent suspect, it’s time to pull it into the interrogation room for a more personal chat.

kubectl describe node k8s-worker-node-7b5d

The output of describe is where the juicy gossip lives. You’re not just looking at specs; you’re looking for a story. Scroll down to the Conditions and, most importantly, the Events section at the bottom. This is where the node often leaves a trail of breadcrumbs explaining exactly why it decided to take an unscheduled vacation.

Conditions:
  Type             Status  LastHeartbeatTime                 LastTransitionTime                Reason                       Message
  ----             ------  -----------------                 ------------------                ------                       -------
  MemoryPressure   False   Mon, 13 Oct 2025 09:55:12 +0200   Mon, 13 Oct 2025 09:45:30 +0200   KubeletHasSufficientMemory   kubelet has sufficient memory available
  DiskPressure     False   Mon, 13 Oct 2025 09:55:12 +0200   Mon, 13 Oct 2025 09:45:30 +0200   KubeletHasNoDiskPressure     kubelet has no disk pressure
  PIDPressure      False   Mon, 13 Oct 2025 09:55:12 +0200   Mon, 13 Oct 2025 09:45:30 +0200   KubeletHasSufficientPID      kubelet has sufficient PID available
  Ready            False   Mon, 13 Oct 2025 09:55:12 +0200   Mon, 13 Oct 2025 09:50:05 +0200   KubeletNotReady              container runtime network not ready: CNI plugin reporting error: rpc error: code = Unavailable desc = connection error

Events:
  Type     Reason                   Age                  From                       Message
  ----     ------                   ----                 ----                       -------
  Normal   Starting                 25m                  kubelet                    Starting kubelet.
  Warning  ContainerRuntimeNotReady 5m12s (x120 over 25m) kubelet                    container runtime network not ready: CNI plugin reporting error: rpc error: code = Unavailable desc = connection error

Aha! Look at that. The Events log is screaming for help. A repeating warning, ContainerRuntimeNotReady, points to a CNI (Container Network Interface) plugin having a full-blown tantrum. We’ve moved from a mystery to a specific lead.

The usual suspects, a rogues’ gallery

When a node goes quiet, the culprit is usually one of a few repeat offenders. Let’s line them up.

1. The silent saboteur network issues

This is the most common villain. Your node might be perfectly healthy, but if it can’t talk to the control plane, it might as well be on a deserted island. Think of the control plane as the central office trying to call its remote employee (the node). If the phone line is cut, the office assumes the employee is gone. This can be caused by firewall rules blocking ports, misconfigured VPC routes, or a DNS server that’s decided to take the day off.

2. The overworked informant, the kubelet

The kubelet is the control plane’s informant on every node. It’s a tireless little agent that reports on the node’s health and carries out orders. But sometimes, this agent gets sick. It might have crashed, stalled, or is struggling with misconfigured credentials (like expired TLS certificates) and can’t authenticate with the mothership. If the informant goes silent, the node is immediately marked as a person of interest.

You can check on its health directly on the node:

# SSH into the problematic node
ssh user@<node-ip>

# Check the kubelet's vital signs
systemctl status kubelet

A healthy output should say active (running). Anything else, and you’ve found a key piece of evidence.

3. The glutton resource exhaustion

Your node has a finite amount of CPU, memory, and disk space. If a greedy application (or a swarm of them) consumes everything, the node itself can become starved. The kubelet and other critical system daemons need resources to breathe. Without them, they suffocate and stop reporting in. It’s like one person eating the entire buffet, leaving nothing for the hosts of the party.

A quick way to check for gluttons is with:

kubectl top node <your-problem-child-node-name>

If you see CPU or memory usage kissing 100%, you’ve likely found your culprit.

The forensic toolkit: digging deeper

If the initial triage and lineup didn’t reveal the killer, it’s time to break out the forensic tools and get our hands dirty.

Sifting Through the Diary with journalctl

The journalctl command is your window into the kubelet’s soul (or, more accurately, its log files). This is where it writes down its every thought, fear, and error.

# On the node, tail the kubelet's logs for clues
journalctl -u kubelet -f --since "10 minutes ago"

Look for recurring error messages, failed connection attempts, or anything that looks suspiciously out of place.

Quarantining the patient with drain

Before you start performing open-heart surgery on the node, it’s wise to evacuate the civilians. The kubectl drain command gracefully evicts all the pods from the node, allowing them to be rescheduled elsewhere.

kubectl drain k8s-worker-node-7b5d --ignore-daemonsets --delete-local-data

This isolates the patient, letting you work without causing a city-wide service outage.

Confirming the phone lines with curl

Don’t just trust the error messages. Verify them. From the problematic node, try to contact the API server directly. This tells you if the fundamental network path is even open.

# From the problem node, try to reach the API server endpoint
curl -k https://<api-server-ip>:<port>/healthz

If you get ok, the basic connection is fine. If it times out or gets rejected, you’ve confirmed a networking black hole.

Crime prevention: keeping your nodes out of trouble

Solving the case is satisfying, but a true detective also works to prevent future crimes.

  • Set up a neighborhood watch: Implement robust monitoring with tools like Prometheus and Grafana. Set up alerts for high resource usage, disk pressure, and node status changes. It’s better to spot a prowler before they break in.
  • Install self-healing robots: Most cloud providers (GKE, EKS, AKS) offer node auto-repair features. If a node fails its health checks, the platform will automatically attempt to repair it or replace it. Turn this on. It’s your 24/7 robotic police force.
  • Enforce city zoning laws: Use resource requests and limits on your deployments. This prevents any single application from building a resource-hogging skyscraper that blocks the sun for everyone else.
  • Schedule regular health checkups: Keep your cluster components, operating systems, and container runtimes updated. Many Not Ready mysteries are caused by long-solved bugs that you could have avoided with a simple patch.

The case is closed for now

So there you have it. The rogue node is back in line, the pods are humming along, and the city of containers is once again at peace. You can hang up your trench coat, put your feet up, and enjoy that lukewarm coffee you made three hours ago. The mystery is solved.

But let’s be honest. Debugging a Not Ready node is less like a thrilling Sherlock Holmes novel and more like trying to figure out why your toaster only toasts one side of the bread. It’s a methodical, often maddening, process of elimination. You start with grand theories of network conspiracies and end up discovering the culprit was a single, misplaced comma in a YAML file, the digital equivalent of the butler tripping over the rug.

So the next time an alert yanks you from your peaceful existence, don’t panic. Remember that you are a digital detective, a whisperer of broken machines. Your job is to patiently ask the right questions until the silent, uncooperative suspect finally confesses. After all, in the world of Kubernetes, a node is never truly dead. It’s just being dramatic and waiting for a good detective to find the clues, and maybe, just maybe, restart its kubelet. The city is safe… until the next time. And there is always a next time.

October 13, 2025 by Cloud stuff DevOps stuff Kubernetes SRE stuff

What is AWS Nucleus, and why Is it poised to replace EC2?

It all started with a coffee and a bill. My usual morning routine. But this particular Tuesday, the AWS bill had an extra kick that my espresso lacked. The cost for a handful of m5.large instances had jumped nearly 40% over the past year. I almost spat out my coffee.

I did what any self-respecting Cloud Architect does: I blamed myself. Did I forget to terminate a dev environment? Did I leave a data transfer running to another continent? But no. After digging through the labyrinth of Cost Explorer, the truth was simpler and far more sinister: EC2 was quietly getting more expensive. Spot instances had become as predictable as a cat on a hot tin plate, and my “burstable” CPUs seemed to run out of breath if they had to do more than jog for a few minutes.

EC2, our old, reliable friend. The bedrock of the cloud. It felt like watching your trusty old car suddenly start demanding premium fuel and imported spare parts just to get to the grocery store. Something was off.

And then, it happened. A slip-up in a public Reddit forum. A senior AWS engineer accidentally posted a file named ec2-phaseout-q4–2027.pdf. It was deleted in minutes, but the internet, as we know, has the memory of an elephant with a grudge.

(Disclaimer for the nervous: This PDF is my narrative device. A ghost in the machine. A convenient plot twist. But the trends it points to? The rising costs, the architectural creaks? Those are very, very real. Now, where were we?)

The document was a bombshell. It laid out a plan to deprecate over 80% of current EC2 instance families by the end of 2027, paving the way for a “next-gen compute platform.” Was this real? I made some calls. The first partner laughed it off. The second went quiet, a little too quiet. The third, after I promised to buy them beers for a month, whispered: “We’re already planning the transition for our enterprise clients.”

Bingo.

Why our beloved EC2 is becoming a museum piece

My lead engineer summed it up beautifully last week. “Running real-time ML on today’s EC2,” he sighed, “feels like asking a 2010 laptop to edit 4K video. It’ll do it, but it’ll scream in agony the whole time, and you’d better have a fire extinguisher handy.”

He’s not wrong. For general-purpose apps, EC2 is still a trusty workhorse. But for the demanding, high-performance workloads that are becoming the norm? You can practically see the gray hairs and hear the joints creaking.

This isn’t just about cost. It’s about architecture. EC2 was built for a different era, an era before serverless was cool, before WebAssembly (WASM) was a thing, and before your toaster needed to run a Kubernetes cluster. The cracks are starting to show.

Meet AWS Nucleus, the secret successor

No press release. No re:Invent keynote. But if you’re connected to AWS insiders, you’ve probably heard whispers of a project internally codenamed “Nucleus.” We got access to this stealth-mode compute platform, and it’s unlike anything we’ve used before.

What does it feel like? Think of it this way: if Lambda and Fargate had a baby, and that baby was raised by a bare-metal server with a PhD in performance, you’d get Nucleus. It has the speed and direct hardware access of a dedicated machine, but with the auto-scaling magic of serverless.

Here are some of the early capabilities we’ve observed:

  • No more cold starts. Unlike Lambda, which can sometimes feel like it’s waking up from a deep nap.
  • Direct hardware access. Full control over GPU and SSD resources without the usual virtualization overhead.
  • Predictive autoscaling. It analyzes traffic patterns and scales before the spike hits, not during.
  • WASM-native runtime. Support for Node.js, Python, Go, and Rust is baked in from the ground up.

It’s not generally available yet, but internal teams and a select few partners are already building on it.

A 30-day head-to-head test

Yes, we triple checked those cost figures. Even if AWS adjusts the pricing after the preview, the efficiency gap is too massive to ignore.

Your survival guide for the coming shift

Let’s be clear, there’s no need to panic and delete all your EC2 instances. But if this memo is even half-right, you don’t want to be caught flat-footed in a few years. Here’s what we’re doing, and what you might want to start experimenting with.

Step 1: Become a cloud whisperer

Start by pinging your AWS Solutions Architect, not directly about “Nucleus,” but something softer:

“Hey, we’re exploring options for more performant, cost-effective compute. Are there any next-gen runtimes or private betas AWS is piloting that we could look into?”

You’ll be surprised what folks share if you ask the right way.

Step 2: test on the shadow platform

Some partners already have early access CLI builds. If you get your hands on one, you’ll notice some familiar patterns.

# Initialize a new service from a template
nucleus init my-api --template=fastapi

# Deploy with a single command
nucleus deploy --env=staging --free-tier

Disclaimer: Not officially available. Use in isolated test environments only. Do not run your production database on this.

Step 3: Run a hybrid setup

If you get preview access, try bridging the old with the new. Here’s a hypothetical Terraform snippet of what that might look like:

# Our legacy EC2 instance for the old monolith
resource "aws_instance" "legacy_worker" {
  ami           = "ami-0b5eea76982371e9" # An old Amazon Linux 2 AMI
  instance_type = "t3.medium"
}

# The new Nucleus service for a microservice
resource "aws_nucleus_service" "new_api" {
  runtime       = "go1.19"
  source_path   = "./app/api"
  
  # This is the magic part: linking to the old world
  vpc_ec2_links = [aws_instance.legacy_worker.id]
}

We ran a few test loads between legacy workers and the new compute, no regressions, and latency even dropped.

Step 4: Estimate the savings yourself

Even with preview pricing, the gap is noticeable. A simple Python script can give you a rough idea.

# Fictional library to estimate costs
import aws_nucleus_estimator

# Your current monthly bill for a specific workload
current_ec2_cost = 4200 

# Estimate based on vCPU hours and memory
# (These numbers are for illustration only)
estimated_nucleus_cost = aws_nucleus_estimator.estimate(
    vcpu_hours=1200, 
    memory_gb_hours=2400
)

print(f"Rough monthly savings: ${current_ec2_cost - estimated_nucleus_cost}")

This is bigger than just EC2

Let’s be honest. This shift isn’t just about cutting costs or shrinking cold start times. It’s about redefining what “compute” even means. EC2 isn’t being deprecated because it’s broken. It’s being phased out because modern workloads have evolved, and the old abstractions are starting to feel like training wheels we forgot to take off.

A broader pattern is emerging across the industry. What AWS is allegedly doing with Nucleus mirrors a larger movement:

  • Google Cloud is reportedly piloting a Cloud Run variant that uses a WASM-based runtime.
  • Microsoft Azure is quietly testing a system to blur the line between containers and functions.
  • Oracle, surprisingly, has been sponsoring development tools optimized for WASM-native environments.

The foundational idea is clear: cloud platforms are moving toward fast-boot, auto-scaling, WASM-capable compute that sits somewhere between Lambda and Kubernetes, but without the overhead of either.

Is EC2 the new legacy?

It’s strange to say, but EC2 is starting to feel like “bare metal” did a decade ago: powerful, essential, but something you try to abstract away.

One of our SREs shared this gem the other day:

“A couple of our junior engineers thought EC2 was some kind of disaster recovery tool for Kubernetes.”

That’s from a Fortune 100 company. When your flagship infrastructure service starts raising eyebrows from fresh grads, you know a generational shift is underway.

The cloud is evolving, again. But this isn’t a gentle, planned succession. It’s a Cambrian explosion in real-time. New, bizarre forms of compute are crawling out of the digital ooze, and the old titans, once thought invincible, are starting to look slow and clumsy. They don’t get a gold watch and a retirement party. They become fossils, their skeletons propping up the new world.

EC2 isn’t dying tomorrow. It’s becoming a geological layer. It’s the bedrock, the sturdy but unglamorous foundation upon which nimbler, more specialized predators will hunt. The future isn’t about killing the virtual machine; it’s about making it an invisible implementation detail. In the same way, most of us stopped thinking about the physical server racks in a data center, we’ll soon stop thinking about the VM. We’ll just care about the work that needs doing.

So no, EC2 isn’t dying. It’s becoming a legend. And in the fast-moving world of technology, legends belong in museums, admired from a safe distance.

August 11, 2025 by Cloud stuff DevOps stuff SRE stuff

The AWS bill shrank by 70%, but so did my DevOps dignity

Fourteen thousand dollars a month.

That number wasn’t on a spreadsheet for a new car or a down payment on a house. It was the line item from our AWS bill simply labeled “API Gateway.” It stared back at me from the screen, judging my life choices. For $14,000, you could hire a small team of actual gateway guards, probably with very cool sunglasses and earpieces. All we had was a service. An expensive, silent, and, as we would soon learn, incredibly competent service.

This is the story of how we, in a fit of brilliant financial engineering, decided to fire our expensive digital bouncer and replace it with what we thought was a cheaper, more efficient swinging saloon door. It’s a tale of triumph, disaster, sleepless nights, and the hard-won lesson that sometimes, the most expensive feature is the one you don’t realize you’re using until it’s gone.

Our grand cost-saving gamble

Every DevOps engineer has had this moment. You’re scrolling through the AWS Cost Explorer, and you spot it: a service that’s drinking your budget like it’s happy hour. For us, API Gateway was that service. Its pricing model felt… punitive. You pay per request, and you pay for the data that flows through it. It’s like paying a toll for every car and then an extra fee based on the weight of the passengers.

Then, we saw the alternative: the Application Load Balancer (ALB). The ALB was the cool, laid-back cousin. Its pricing was simple, based on capacity units. It was like paying a flat fee for an all-you-can-eat buffet instead of by the gram.

The math was so seductive it felt like we were cheating the system.

We celebrated. We patted ourselves on the back. We had slain the beast of cloud waste! The migration was smooth. Response times even improved slightly, as an ALB adds less overhead than the feature-rich API Gateway. Our architecture was simpler. We had won.

Or so we thought.

When reality crashes the party

API Gateway isn’t just a toll booth. It’s a fortress gate with a built-in, military-grade security detail you get for free. It inspects IDs, pats down suspicious characters, and keeps a strict count of who comes and goes. When we swapped it for an ALB, we essentially replaced that fortress gate with a flimsy screen door. And then we were surprised when the bugs got in.

The trouble didn’t start with a bang. It started with a whisper.

Week 1: The first unwanted visitor. A script kiddie, probably bored in their parents’ basement, decided to poke our new, undefended endpoint with a classic SQL injection probe. It was the digital equivalent of someone jiggling the handle on your front door. With API Gateway, its built-in Web Application Firewall (WAF) would have spotted the malicious pattern and drop-kicked the request into the void before it ever reached our application. With the ALB, the request sailed right through. Our application code was robust enough to reject it, but the fact that it got to knock on the application’s door at all was a bad sign. We dismissed it. “An amateur,” we scoffed.

Week 3: The client who drank too much. A bug in a third-party client integration caused it to get stuck in a loop. A very, very fast loop. It began hammering one of our endpoints with 10,000 requests per second. The servers, bless their hearts, tried to keep up. Alarms started screaming. The on-call engineer, who was attempting to have a peaceful dinner, saw his phone light up like a Christmas tree. API Gateway’s built-in rate limiting would have calmly told the misbehaving client, “You’ve had enough, buddy. Come back later.” It would have throttled the requests automatically. We had to scramble, manually blocking the IP while the system groaned under the strain.

Week 6: The full-blown apocalypse. This was it. The big one. A proper Distributed Denial-of-Service (DDoS) attack. It wasn’t sophisticated, just a tidal wave of garbage traffic from thousands of hijacked machines. Our ALB, true to its name, diligently balanced this flood of garbage across all our servers, ensuring that every single one of them was completely overwhelmed in a perfectly distributed fashion. The site went down. Hard.

The next 72 hours were a blur of caffeine, panicked Slack messages, and emergency calls with our new best friends at Cloudflare. We had saved $9,800 on our monthly bill, but we were now paying for it with our sanity and our customers’ trust.

Waking up to the real tradeoff

The hidden cost wasn’t just the emergency WAF setup or the premium support plan. It was the engineering hours. The all-nighters. The “I’m so sorry, it won’t happen again” emails to customers.

We had made a classic mistake. We looked at API Gateway’s price tag and saw a cost. We should have seen an itemized receipt for services rendered:

  • Built-in WAF: Free bouncer that stops common attacks.
  • Rate Limiting: A Free bartender who cuts off rowdy clients.
  • Request Validation: Free doorman that checks for malformed IDs (JSON).
  • Authorization: Free security guard that validates credentials (JWTs/OAuth).

An ALB does none of this. It just balances the load. That’s its only job. Expecting it to handle security is like expecting the mailman to guard your house. It’s not what he’s paid for.

Building a smarter hybrid fortress

After the fires were out and we’d all had a chance to sleep, we didn’t just switch back. That would have been too easy (and an admission of complete defeat). We came back wiser, like battle-hardened veterans of the cloud wars. We realized that not all traffic is created equal.

We landed on a hybrid solution, the architectural equivalent of having both a friendly greeter and a heavily-armed guard.

1. Let the ALB handle the easy stuff. For high-volume, low-risk traffic like serving images or tracking pixels, the ALB is perfect. It’s cheap, fast, and the security risk is minimal. Here’s how we route the “dumb” traffic in serverless.yml:

# serverless.yml
functions:
  handleMedia:
    handler: handler.alb  # A simple handler for media
    events:
      - alb:
          listenerArn: arn:aws:elasticloadbalancing:us-east-1:123456789012:listener/app/my-main-alb/a1b2c3d4e5f6a7b8
          priority: 100
          conditions:
            path: /media/*  # Any request for images, videos, etc.

2. Protect the crown jewels with API Gateway. For anything critical, user authentication, data processing, and payment endpoints, we put it back behind the fortress of API Gateway. The extra cost is a small price to pay for peace of mind.

# serverless.yml
functions:
  handleSecureData:
    handler: handler.auth  # A handler with business logic
    events:
      - httpApi:
          path: /secure/v2/{proxy+}
          method: any

3. Add the security we should have had all along. This was non-negotiable. We layered on security like a paranoid onion.

  • AWS WAF on the ALB: We now pay for a WAF to protect our “dumb” endpoints. It’s an extra cost, but cheaper than an outage.
  • Cloudflare: Sits in front of everything, providing world-class DDoS protection.
  • Custom Rate Limiting: For nuanced cases, we built our own rate limiter. A Lambda function checks a Redis cache to track request counts from specific IPs or API keys.

Here’s a conceptual Python snippet for what that Lambda might look like:

# A simplified Lambda function for rate limiting with Redis
import redis
import os

# Connect to Redis
redis_client = redis.Redis(
    host=os.environ['REDIS_HOST'], 
    port=6379, 
    db=0
)

RATE_LIMIT_THRESHOLD = 100  # requests
TIME_WINDOW_SECONDS = 60    # per minute

def lambda_handler(event, context):
    # Get an identifier, e.g., from the source IP or an API key
    client_key = event['requestContext']['identity']['sourceIp']
    
    # Increment the count for this key
    current_count = redis_client.incr(client_key)
    
    # If it's a new key, set an expiration time
    if current_count == 1:
        redis_client.expire(client_key, TIME_WINDOW_SECONDS)
    
    # Check if the limit is exceeded
    if current_count > RATE_LIMIT_THRESHOLD:
        # Return a "429 Too Many Requests" error
        return {
            "statusCode": 429,
            "body": "Rate limit exceeded. Please try again later."
        }
    
    # Otherwise, allow the request to proceed to the application
    return {
        "statusCode": 200, 
        "body": "Request allowed"
    }

The final scorecard

So, after all that drama, was it worth it? Let’s look at the final numbers.

Verdict: For us, yes, it was ultimately worth it. Our traffic profile justified the complexity. But we traded money for time and complexity. We saved cash on the AWS bill, but the initial investment in engineering hours was massive. My DevOps dignity took a hit, but it has since recovered, scarred but wiser.

Your personal sanity check

Before you follow in our footsteps, do yourself a favor and perform an audit. Don’t let a shiny, low price tag lure you onto the rocks.

1. Calculate your break-even point. First, figure out what API Gateway is costing you.

# Get your usage data from API Gateway 
aws apigateway get-usage \ --usage-plan-id your_plan_id_here \ 
   --start-date 2024-01-01 
   --end-date 2024-01-31 \ 
   --query 'items'

Then, estimate your ALB costs based on request volume.

# Get request count to estimate ALB costs
aws cloudwatch get-metric-statistics \
    --namespace AWS/ApplicationELB \
    --metric-name RequestCount \
    --dimensions Name=LoadBalancer,Value=app/my-main-alb/a1b2c3d4e5f6a7b8 \
    --start-time 2024-01-01T00:00:00Z \
    --end-time 2024-01-31T23:59:59Z \
    --period 86400 \
    --statistics Sum

2. Test your defenses (or lack thereof). Don’t wait for a real attack. Simulate one. Tools like Burp Suite or sqlmap can show you just how exposed you are.

# A simple sqlmap test against a vulnerable parameter 
sqlmap -u "https://api.yoursite.com/endpoint?id=1" --batch

If you switch to an ALB without a WAF, you might be horrified by what you find.

In the end, choosing between API Gateway and an ALB isn’t just a technical decision. It’s a business decision about risk, and one that goes far beyond the monthly bill. It’s about calculating the true Total Cost of Ownership (TCO), where the “O” includes your engineers’ time, your customers’ trust, and your sleep schedule. Are you paying with dollars on an invoice, or are you paying with 3 AM incident calls and the slow erosion of your team’s morale?

Peace of mind has a price tag. For us, we discovered its value was around $7,700 a month. We just had to get DDoS’d to learn how to read the receipt. Don’t make the same mistake. Look at your architecture not just for what it costs, but for what it protects. Because the cheapest option on paper can quickly become the most expensive one in reality.

July 27, 2025 by Cloud stuff DevOps stuff SRE stuff

The case of the missing EC2 public IP

It was a Tuesday. I did what I’ve done a thousand times: I logged into an Amazon EC2 instance using its public IP, a respectable 52.95.110.21. Routine stuff. Once inside, muscle memory took over, and I typed ip addr to see the network configuration.

And then I blinked.

inet 10.0.10.147/24.

I checked my terminal history. Yes, I had connected to 52.95.110.21. So, where did this 10.0.10.147 character come from? And more importantly, where on earth was the public IP I had just used? For a moment, I felt like a detective at a crime scene where the main evidence had vanished into thin air. I questioned my sanity, my career choice, and the very fabric of the TCP/IP stack.

Suppose you’ve ever felt this flicker of confusion, congratulations. You’ve just stumbled upon one of AWS’s most elegant sleights of hand. The truth is, the public IP doesn’t exist inside the instance. It’s a ghost. A label. A brilliant illusion.

Let’s put on our detective hats and solve this mystery.

Meet the real resident

Every EC2 instance, upon its creation, is handed a private IP address. Please think of this as its legal name, the one on its birth certificate. It’s the address it uses to talk to its neighbors within its cozy, gated community, the Virtual Private Cloud (VPC). This instance, 10.0.10.147, is perfectly happy using this address to get a cup of sugar from the database next door at 10.0.10.200. It’s private, it’s intimate, and frankly, it’s nobody else’s business.

The operating system itself, be it Ubuntu, Amazon Linux, or Windows, is blissfully unaware of any other identity. As far as it’s concerned, its name is 10.0.10.147, and that’s the end of the story. If you check its network configuration, that’s the only IP you’ll see.

# Check the network interface, see only the private IP
$ ip -4 addr show dev eth0 | grep inet
    inet 10.0.10.147/24 brd 10.0.10.255 scope global dynamic eth0

So, if the instance only knows its private name, how does it have a public life on the internet?

The master of disguise

The magic happens outside the instance, at the edge of your VPC. Think of the AWS Internet Gateway as a ridiculously efficient bouncer at an exclusive club. Your instance, wanting to go online, walks up to the bouncer and whispers, “Hey, I’m 10.0.10.147 And I need to fetch a cat picture from the internet.“

The bouncer nods, turns to the vast, chaotic world of the internet, and shouts, “HEY, TRAFFIC FOR 52.95.110.21, COME THIS WAY!”

This translation trick is called 1-to-1 Network Address Translation (NAT). The Internet Gateway maintains a secret map that links the public IP to the private IP. The public IP is just a mask, a public-facing persona. It never actually gets assigned to the instance’s network interface. It’s all handled by AWS behind the scenes, a performance so smooth you never even knew it was happening.

When your instance sends traffic out, the bouncer (Internet Gateway) swaps the private source IP for the public one. When return traffic comes back, the bouncer swaps it back. The instance is none the wiser. It lives its entire life thinking its name is 10.0.10.147, completely oblivious to its international fame as 52.95.110.21.

Interrogating the local gossip

So, the instance is clueless. But what if we, while inside, need to know its public identity? What if we need to confirm our instance’s public IP for a firewall rule somewhere else? We can’t just ask the operating system.

Fortunately, AWS provides a nosy neighbor for just this purpose: the Instance Metadata Service. This is a special, link-local address (169.254.169.254) that an instance can query to learn things about itself, things the operating system doesn’t know. It’s the local gossip line.

To get the public IP, you first have to get a temporary security token (because even gossip has standards these days), and then you can ask your question.

# First, grab a session token. This is good for 6 hours (21600 seconds).
$ TOKEN=$(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 21600")

# Now, use the token to ask for the public IP
$ curl -s -H "X-aws-ec2-metadata-token: $TOKEN" http://169.254.169.254/latest/meta-data/public-ipv4
52.95.110.21

And there it is. The ghost in the machine, confirmed.

A detective’s field guide

Now that you’re in on the secret, you can avoid getting duped. Here are a few field notes to keep in your detective’s journal.

The clueless security guard. If you’re configuring a firewall inside your instance (like iptables or UFW), remember that it only knows the instance by its private IP. Don’t try to create a rule for your public IP; the firewall will just stare at you blankly. Always use the private IP for internal security configurations.

The persistent twin: What if you need a public IP that doesn’t change every time you stop and start your instance? That’s what an Elastic IP (EIP) is for. But don’t be fooled. An EIP is just a permanent public IP that you own. Behind the scenes, it works the same way: it’s a persistent mask mapped via NAT to your instance’s private IP. The instance remains just as gloriously ignorant as before.

The vanishing act. If you’re not using an Elastic IP, your instance’s public IP is ephemeral. It’s more like a hotel room number than a home address. Stop the instance, and it gives up the IP. Start it again, and it gets a brand new one. This is a classic “gotcha” that has broken countless DNS records and firewall rules.

The case is closed

So, the next time you log into an EC2 instance and see a private IP, don’t panic. You haven’t SSH’d into your smart fridge by mistake. You’re simply seeing the instance for who it truly is. The public IP was never missing; it was just a clever disguise, an elegant illusion managed by AWS so your instance can have a public life without ever having to deal with the paparazzi. The heavy lifting is done for you, leaving your instance to worry about more important things. Like serving those cat pictures.

July 12, 2025 by Cloud stuff

The core AWS services for modern DevOps

In any professional kitchen, there’s a natural tension. The chefs are driven to create new, exciting dishes, pushing the boundaries of flavor and presentation. Meanwhile, the kitchen manager is focused on consistency, safety, and efficiency, ensuring every plate that leaves the kitchen meets a rigorous standard. When these two functions don’t communicate well, the result is chaos. When they work in harmony, it’s a Michelin-star operation.

This is the world of software development. Developers are the chefs, driven by innovation. Operations teams are the managers, responsible for stability. DevOps isn’t just a buzzword; it’s the master plan that turns a chaotic kitchen into a model of culinary excellence. And AWS provides the state-of-the-art appliances and workflows to make it happen.

The blueprint for flawless construction

Building infrastructure without a plan is like a construction crew building a house from memory. Every house will be slightly different, and tiny mistakes can lead to major structural problems down the line. Infrastructure as Code (IaC) is the practice of using detailed architectural blueprints for every project.

AWS CloudFormation is your master blueprint. Using a simple text file (in JSON or YAML format), you define every single resource your application needs, from servers and databases to networking rules. This blueprint can be versioned, shared, and reused, guaranteeing that you build an identical, error-free environment every single time. If something goes wrong, you can simply roll back to a previous version of the blueprint, a feat impossible in traditional construction.

To complement this, the Amazon Machine Image (AMI) acts as a prefabricated module. Instead of building a server from scratch every time, an AMI is a perfect snapshot of a fully configured server, including the operating system, software, and settings. It’s like having a factory that produces identical, ready-to-use rooms for your house, cutting setup time from hours to minutes.

The automated assembly line for your code

In the past, deploying software felt like a high-stakes, manual event, full of risk and stress. Today, with a continuous delivery pipeline, it should feel as routine and reliable as a modern car factory’s assembly line.

AWS CodePipeline is the director of this assembly line. It automates the entire release process, from the moment code is written to the moment it’s delivered to the user. It defines the stages of build, test, and deploy, ensuring the product moves smoothly from one station to the next.

Before the assembly starts, you need a secure warehouse for your parts and designs. AWS CodeCommit provides this, offering a private and secure Git repository to store your code. It’s the vault where your intellectual property is kept safe and versioned.

Finally, AWS CodeDeploy is the precision robotic arm at the end of the line. It takes the finished software and places it onto your servers with zero downtime. It can perform sophisticated release strategies like Blue-Green deployments. Imagine the factory rolling out a new car model onto the showroom floor right next to the old one. Customers can see it and test it, and once it’s approved, a switch is flipped, and the new model seamlessly takes the old one’s place. This eliminates the risk of a “big bang” release.

Self-managing environments that thrive

The best systems are the ones that manage themselves. You don’t want to constantly adjust the thermostat in your house; you want it to maintain the perfect temperature on its own. AWS offers powerful tools to create these self-regulating environments.

AWS Elastic Beanstalk is like a “smart home” system for your application. You simply provide your code, and Beanstalk handles everything else automatically: deploying the code, balancing the load, scaling resources up or down based on traffic, and monitoring health. It’s the easiest way to get an application running in a robust environment without worrying about the underlying infrastructure.

For those who need more control, AWS OpsWorks is a configuration management service that uses Chef and Puppet. Think of it as designing a custom smart home system from modular components. It gives you granular control to automate how you configure and operate your applications and infrastructure, layer by layer.

Gaining full visibility of your operations

Operating an application without monitoring is like trying to run a factory from a windowless room. You have no idea if the machines are running efficiently if a part is about to break, or if there’s a security breach in progress.

AWS CloudWatch is your central control room. It provides a wall of monitors displaying real-time data for every part of your system. You can track performance metrics, collect logs, and set alarms that notify you the instant a problem arises. More importantly, you can automate actions based on these alarms, such as launching new servers when traffic spikes.

Complementing this is AWS CloudTrail, which acts as the unchangeable security logbook for your entire AWS account. It records every single action taken by any user or service, who logged in, what they accessed, and when. For security audits, troubleshooting, or compliance, this log is your definitive source of truth.

The unbreakable rules of engagement

Speed and automation are worthless without strong security. In a large company, not everyone gets a key to every room. Access is granted based on roles and responsibilities.

AWS Identity and Access Management (IAM) is your sophisticated keycard system for the cloud. It allows you to create users and groups and assign them precise permissions. You can define exactly who can access which AWS services and what they are allowed to do. This principle of “least privilege”, granting only the permissions necessary to perform a task, is the foundation of a secure cloud environment.

A cohesive workflow not just a toolbox

Ultimately, a successful DevOps culture isn’t about having the best individual tools. It’s about how those tools integrate into a seamless, efficient workflow. A world-class kitchen isn’t great because it has a sharp knife and a hot oven; it’s great because of the system that connects the flow of ingredients to the final dish on the table.

By leveraging these essential AWS services, you move beyond a simple collection of tools and adopt a new operational philosophy. This is where DevOps transcends theory and becomes a tangible reality: a fully integrated, automated, and secure platform. This empowers teams to spend less time on manual configuration and more time on innovation, building a more resilient and responsive organization that can deliver better software, faster and more reliably than ever before.

June 11, 2025 by Cloud stuff DevOps stuff SRE stuff

The strange world of serverless data processing made simple

Data isn’t just “big” anymore. It’s feral. It stampedes in from every direction, websites, mobile apps, a million sentient toasters, and it rarely arrives neatly packaged. It’s messy, chaotic, and stubbornly resistant to being neatly organized into rows for analysis. For years, taming this digital beast meant building vast, complicated corrals of servers, clusters, and configurations. It was a full-time job to keep the lights on, let alone do anything useful with the data itself.

Then, the cloud giants whispered a sweet promise in our ears: “serverless.” Let us handle the tedious infrastructure, they said. You just focus on the data. It sounds like magic, and sometimes it is. But it’s a specific kind of magic, with its own incantations and rules. Let’s explore the fundamental principles of this magic through Google Cloud’s Dataflow, and then see how its cousins at Amazon, AWS Glue and AWS Kinesis, perform similar tricks.

The anatomy of a data pipeline

No matter which magical cloud service you use, the core ritual is always the same. It’s a simple, three-step dance.

  1. Read: You grab your wild data from a source.
  2. Transform: You perform some arcane logic to clean, shape, enrich, or otherwise domesticate it.
  3. Write: You deposit the now-tamed data into a sink, like a database or data warehouse, where it can finally be useful.

This sequence is called a pipeline. In the serverless world, the pipeline is not a physical thing but a logical construct, a recipe that tells the cloud how to process your data.

Shaping the data clay

Once data enters a pipeline, it needs to be held in something. You can’t just let it slosh around. In Dataflow, data is scooped into a PCollection. The ‘P’ stands for ‘Parallel’, which is a hint that this collection is designed to be scattered across many machines and processed all at once. A key feature of a PCollection is that it’s immutable. When you apply a transformation, you don’t change the original collection; you create a brand-new one. It’s like a paranoid form of data alchemy where you never destroy your original ingredients.

Over in the AWS world, Glue prefers to work with DynamicFrames. Think of them as souped-up DataFrames from the Spark universe, built to handle the messy, semi-structured data that Glue often finds in the wild. Kinesis Data Analytics, being a specialist in fast-moving data, treats data as a continuous stream that you operate on as it flows by. The concept is the same, an in-memory representation of your data, but the name and nuances change depending on the ecosystem.

The art of transformation

A pipeline without transformations is just a very expensive copy-paste command. The real work happens here.

Dataflow uses the Apache Beam SDK, a powerful, open-source framework that lets you define your transformations in Java or Python. These operations are fittingly called Transforms. The beauty of Beam is its portability; you can write a Beam pipeline and, in theory, run it on other platforms (like Apache Flink or Spark) without a complete rewrite. It’s the “write once, run anywhere” dream, applied to data processing.

AWS Glue takes a more direct approach. You can write your transformations using Spark code (Python or Scala) or use Glue Studio, a visual interface that lets you build ETL (Extract, Transform, Load) jobs by dragging and dropping boxes. It’s less about portability and more about deep integration with the AWS ecosystem. Kinesis Data Analytics simplifies things even further for its real-time niche, letting you transform streams primarily through standard SQL queries or, for more complex tasks, by using the Apache Flink framework.

Running wild and scaling free

Here’s the serverless punchline: you define the pipeline, and the cloud runs it. You don’t provision servers, patch operating systems, or worry about cluster management.

When you launch a Dataflow job, Google Cloud automatically spins up a fleet of worker virtual machines to execute your pipeline. Its most celebrated trick is autoscaling. If a flood of data arrives, Dataflow automatically adds more workers. When the flood subsides, it sends them away. For streaming jobs, its Streaming Engine further refines this process, making scaling faster and more efficient.

AWS Glue and Kinesis Data Analytics operate on a similar principle, though with different acronyms. Glue jobs run on a pre-configured amount of “Data Processing Units” (DPUs), which it can autoscale. Kinesis applications run on “Kinesis Processing Units” (KPUs), which also scale based on throughput. The core benefit is identical across all three: you’re freed from the shackles of capacity planning.

Choosing your flow batch or stream

Not all data processing needs are created equal. Sometimes you need to process a massive, finite dataset, and other times you need to react to an endless flow of events.

  • Batch processing: This is like doing all your laundry at the end of the month. It’s perfect for generating daily reports, analyzing historical data, or running large-scale ETL jobs. Dataflow and AWS Glue are both excellent at batch processing.
  • Streaming processing: This is like washing each dish the moment you’re done with it. It’s essential for real-time dashboards, fraud detection, and feeding live data into AI models. Dataflow is a streaming powerhouse. Kinesis Data Analytics is a specialist, designed from the ground up exclusively for this kind of real-time work. While Glue has some streaming capabilities, they are typically geared towards continuous ETL rather than complex real-time analytics.

Picking your champion

So, which tool should you choose for your data-taming adventure? It’s less about which is “best” and more about which is right for your specific quest.

  • Choose Google Cloud Dataflow if you value portability. The Apache Beam model is a powerful abstraction that prevents vendor lock-in and is exceptionally good at handling both complex batch and streaming scenarios with a single programming model.
  • Choose AWS Glue if your world is already painted in AWS colors. Its primary strength is serverless ETL. It integrates seamlessly with the entire AWS data stack, from S3 data lakes to Redshift warehouses, making it the default choice for data preparation within that ecosystem.
  • Choose AWS Kinesis Data Analytics when your only concern is now. If you need to analyze, aggregate, and react to data in milliseconds or seconds, Kinesis is the sharp, specialized tool for the job.

The serverless horizon

Ultimately, these services represent a fundamental shift in how we approach data engineering. They allow us to move our focus away from the mundane mechanics of managing infrastructure and toward the far more interesting challenge of extracting value from data. Whether you’re using Dataflow, Glue, or Kinesis, you’re leveraging an incredible amount of abstracted complexity to build powerful, scalable, and resilient data solutions. The future of data processing isn’t about building bigger servers; it’s about writing smarter logic and letting the cloud handle the rest.

June 8, 2025 by Cloud stuff

How AI transformed cloud computing forever

When ChatGPT emerged onto the tech scene in late 2022, it felt like someone had suddenly switched on the lights in a dimly lit room. Overnight, generative AI went from a niche technical curiosity to a global phenomenon. Behind the headlines and excitement, however, something deeper was shifting: cloud computing was experiencing its most significant transformation since its inception.

For nearly fifteen years, the cloud computing model was a story of steady, predictable evolution. At its core, the concept was revolutionary yet straightforward, much like switching from owning a private well to relying on public water utilities. Instead of investing heavily in physical servers, businesses could rent computing power, storage, and networking from providers like AWS, Google Cloud, or Azure. It democratized technology, empowering startups to scale into global giants without massive upfront costs. Services became faster, cheaper, and better, yet the fundamental model remained largely unchanged.

Then, almost overnight, AI changed everything. The game suddenly had new rules.

The hardware revolution beneath our feet

The first transformative shift occurred deep inside data centers, a hardware revolution triggered by AI.

Traditionally, cloud servers relied heavily on CPUs, versatile processors adept at handling diverse tasks one after another, much like a skilled chef expertly preparing dishes one by one. However AI workloads are fundamentally different; training AI models involves executing thousands of parallel computations simultaneously. CPUs simply weren’t built for such intense multitasking.

Enter GPUs, Graphics Processing Units. Originally designed for video games to render graphics rapidly, GPUs excel at handling many calculations simultaneously. Imagine a bustling pizzeria with a massive oven that can bake hundreds of pizzas all at once, compared to a traditional restaurant kitchen serving dishes individually. For AI tasks, GPUs can be up to 100 times faster than standard CPUs.

This demand for GPUs turned them into high-value commodities, transforming Nvidia into a household name and prompting tech companies to construct specialized “AI factories”, data centers built specifically to handle these intense AI workloads.

The financial impact businesses didn’t see coming

The second seismic shift is financial. Running AI workloads is extremely costly, often 20 to 100 times more expensive than traditional cloud computing tasks.

Several factors drive these costs. First, specialized GPU hardware is significantly pricier. Second, unlike traditional web applications that experience usage spikes, AI model training requires continuous, heavy computing power, often 24/7, for weeks or even months. Finally, massive datasets needed for AI are expensive to store and transfer.

This cost surge has created a new digital divide. Today, CEOs everywhere face urgent questions from their boards: “What is our AI strategy?” The pressure to adopt AI technologies is immense, yet high costs pose a significant barrier. This raises a crucial dilemma for businesses: What’s the cost of not adopting AI? The potential competitive disadvantage pushes companies into difficult financial trade-offs, making AI a high-stakes game for everyone involved.

From infrastructure to intelligent utility

Perhaps the most profound shift lies in what cloud providers actually offer their customers today.

Historically, cloud providers operated as infrastructure suppliers, selling raw computing resources, like giving people access to fully equipped professional kitchens. Businesses had to assemble these resources themselves to create useful services.

Now, providers are evolving into sellers of intelligence itself, “Intelligence as a Service.” Instead of just providing raw resources, cloud companies offer pre-built AI capabilities easily integrated into any application through simple APIs.

Think of this like transitioning from renting a professional kitchen to receiving ready-to-cook gourmet meal kits delivered straight to your door. You no longer need deep culinary skills, similarly, businesses no longer require PhDs in machine learning to integrate AI into their products. Today, with just a few lines of code, developers can effortlessly incorporate advanced features such as image recognition, natural language processing, or sophisticated chatbots into their applications.

This shift truly democratizes AI, empowering domain experts, people deeply familiar with specific business challenges, to harness AI’s power without becoming specialists in AI themselves. It unlocks the potential of the vast amounts of data companies have been collecting for years, finally allowing them to extract tangible value.

The Unbreakable Bond Between Cloud and AI

These three transformations, hardware, economics, and service offerings, have reinvented cloud computing entirely. In this new era, cloud computing and AI are inseparable, each fueling the other’s evolution.

Businesses must now develop unified strategies that integrate cloud and AI seamlessly. Here are key insights to guide that integration:

  • Integrate, don’t reinvent: Most businesses shouldn’t aim to create foundational AI models from scratch. Instead, the real value lies in effectively integrating powerful, existing AI models via APIs to address specific business needs.
  • Prioritize user experience: The ultimate goal of AI in business is to dramatically enhance user experiences. Whether through hyper-personalization, automating tedious tasks, or surfacing hidden insights, successful companies will use AI to transform the customer journey profoundly.

Cloud computing today is far more than just servers and storage, it’s becoming a global, distributed brain powering innovation. As businesses move forward, the combined force of cloud and AI isn’t just changing the landscape; it’s rewriting the very rules of competition and innovation.

The future isn’t something distant, it’s here right now, and it’s powered by AI.

June 7, 2025 by Cloud stuff Computer Science stuff
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Exploring the Cloud: Insights into DevOps, Linux & Architectural Evolution