Fernando SRE

Choosing your message queue AWS SQS or GCP Pub/Sub

In the world of modern software, applications are rarely monolithic islands. Instead, they are bustling cities of interconnected services, each performing a specific job. For this city to function smoothly, its inhabitants, microservices, functions, and components need a reliable way to communicate without being directly tethered to one another. This is where message brokers come in, acting as the city’s postal service, ensuring that messages are delivered efficiently and reliably.

Two of the most prominent cloud-based postal services are Amazon Web Services’ Simple Queue Service (SQS) and Google Cloud’s Pub/Sub. Both are exceptional at what they do, but they operate on different philosophies. Understanding their unique characteristics is crucial for any cloud architect or DevOps engineer aiming to build robust, scalable, and event-driven systems. This guide will explore their differences to help you choose the right service for your application’s needs.

A quick look at our contenders

Before we examine the details, let’s get a general feel for each service.

AWS SQS is the seasoned veteran of message queuing. Think of it as a highly organized system of mailboxes. A service writes a letter (a message) and places it into a specific mailbox (a queue). The recipient service then comes to that mailbox and picks up its mail when it has the capacity to process it. It’s a straightforward, incredibly reliable system that has been battle-tested for years.

GCP Pub/Sub operates more like a global newspaper subscription. A publisher (your service) doesn’t send a message to a specific recipient. Instead, it publishes a message to a “topic,” like a news flash for the “user-signup” channel. Any service that has subscribed to that topic instantly receives a copy of the message. It’s designed for broad, real-time distribution of information on a global scale.

The delivery dilemma Push versus Pull

The most fundamental difference between SQS and Pub/Sub lies in how messages are delivered. This is often referred to as the “push vs. pull” model.

The pull model, which is SQS’s native approach, is like checking your P.O. box. The consumer application is responsible for periodically asking the queue, “Is there any mail for me?” This gives the consumer complete control over the rate of consumption. If it’s overwhelmed with work, it can slow down its requests or stop asking for new messages altogether. This is ideal for batch processing or any workload where you need to manage the processing pace carefully.

The push model, where Pub/Sub shines, is akin to home mail delivery. When a message is published, Pub/Sub actively “pushes” it to all subscribed endpoints, such as a serverless function or a webhook. The recipient doesn’t have to ask; the message just arrives. This is incredibly efficient for real-time notifications and event-driven workflows where immediate reaction is key. While Pub/Sub also supports a pull model, its architecture is optimized for push-based delivery.

Comparing key features

Let’s break down how these two services stack up in a few critical areas.

Message ordering

Sometimes, the sequence of events is just as important as the events themselves. For these cases, AWS SQS offers a specific FIFO (First-In, First-Out) queue type. This works exactly like a single-file line at a bank; the first person to get in line is the first one to be served. It provides a strict guarantee that messages will be processed in the exact order they were sent, which is critical for tasks like processing financial transactions or application logs.

GCP Pub/Sub, in contrast, does not have a dedicated FIFO queue type. Instead, it achieves partial ordering through the use of ordering keys. You can assign a key to messages (for example, a userId), and Pub/Sub will ensure that all messages with that specific key are delivered in order. However, it doesn’t guarantee order between different keys. To reuse the analogy, it’s less like a single line and more like a deli with separate ticket numbers for the butcher and the bakery. It keeps orders straight within each department, but not across the entire store.

Scale and reach

This is where their architectural differences become clear. SQS is a regional service. It’s incredibly scalable and resilient, but its scope is confined to a single AWS region.

Pub/Sub is inherently global. You publish a message once, and it can be delivered to subscribers in any region around the world with low latency. If your application has a global user base and you need to propagate events worldwide, Pub/Sub has a distinct advantage.

Message size and retention

Think of SQS as being for postcards and letters. It supports messages up to 256 KB. It can hold onto these messages for up to 14 days, giving your consumers plenty of time to process them.

Pub/Sub, on the other hand, can handle larger packages, with a maximum message size of 10 MB. However, its standard retention period is shorter, at 7 days.

Special delivery options

SQS has a native feature called Delay Queues. This allows you to postpone the delivery of a new message for up to 15 minutes. It’s like writing a post-dated check; the message sits in the queue but is invisible to consumers until the timer expires. This is useful for scheduling tasks without a complex scheduling service. Pub/Sub does not offer a similar built-in feature.

When to choose AWS SQS

SQS is your go-to choice when you need a dependable, orderly mailroom for your application. It excels in scenarios where:

Strict ordering is non-negotiable. For task sequencing or financial ledgers, SQS FIFO is the gold standard.
You need to control the pace of consumption. The pull model is perfect for decoupling a fast producer from a slower consumer or for batch processing jobs.
Task scheduling is required. The native delay queue feature is a simple yet powerful tool.
Your application’s architecture is primarily contained within a single AWS region.

When to choose GCP Pub/Sub

Pub/Sub is the right tool when you’re building a global broadcasting system or a highly reactive, event-driven platform. Consider it when:

You need to fan-out messages to many consumers. Pub/Sub’s topic-and-subscription model is designed for this.
Global distribution with low latency is a priority. Its global nature is a massive benefit for distributed systems.
You are sending large messages. The 10 MB limit offers much more flexibility than SQS.
A push-based model fits your architecture. It integrates seamlessly with serverless functions for instant, event-triggered execution.

A final word

So, after all this technical deliberation, which digital courier should you entrust with your precious data packets? The one that meticulously forms a single, orderly queue, or the one that shouts your message through a global megaphone to anyone who will listen?

The truth is, there’s no single “best” service. There’s only the one whose particular brand of crazy best matches your application’s personality. Is your app a stickler for the rules, demanding every event be processed in perfect sequence, lest it have a digital panic attack? Then the quiet, predictable, and slightly obsessive SQS is your soulmate. Or is your app more of a drama queen, needing to announce every minor update to the entire world, immediately? Then the boisterous, globe-trotting Pub/Sub is probably already sliding into your DMs.

Ultimately, the best way to choose is to put them to the test. Think of it as a job interview. Give them both a trial run with your actual workload and see which one handles the pressure with more grace, or at least breaks in a more interesting, less catastrophic way. Go on, do it for science. And for the future sanity of your on-call engineer.

July 6, 2025 by Fernando SRE Cloud stuff

An irreverent tour of Linux disk space and RAM mysteries

Linux feels a lot like living in a loft apartment: the pipes are on display, every clank echoes, and when something leaks, you’re the first to squelch through the puddle. This guide hands you a mop, half a dozen snappy commands that expose where your disk space and memory have wandered off to, plus a couple of click‑friendly detours. Expect prose that winks, occasionally rolls its eyes, and never ever sounds like tax law.

Why checking disk and memory matters

Think of storage and RAM as the pantry and fridge in a shared flat. Ignore them for a week, and you end up with three half‑finished jars of salsa (log files) and leftovers from roommates long gone (orphaned kernels). A five‑minute audit every Friday spares you the frantic sprint for extra space, or worse, the freeze just before a production deploy.

Disk panic survival kit

Get the big picture fast

df is the bird’s‑eye drone shot of your mounted filesystems, everything lines up like contestants at a weigh‑in.

# Exclude temporary filesystems for clarity
$ df -hT -x tmpfs -x devtmpfs

-h prints friendly sizes, -T shows filesystem type, and the two -x flags hide the short‑lived stuff.

Zoom in on space hogs

du is your tape measure. Pair it with a little sort and head for instant gossip about the top offenders in any directory:

# Top 10 fattest directories under /var
$ sudo du -h --max-depth=1 /var 2>/dev/null | sort -hr | head -n 10

If /var/log looks like it skipped leg day and went straight for bulking season, you’ve found the culprit.

Bring in the interactive detective

When scrolling text gets dull, ncdu adds caffeine and colour:

# Install on most Debian‑based distros
$ sudo apt install ncdu

# Start at root (may take a minute)
$ sudo ncdu /

Navigate with the arrow keys, press d to delete, and feel the instant gratification of reclaiming gigabytes, the Marie Kondo of storage.

Visualise block devices

# Tree view of drives, partitions, and mount points
$ lsblk -o NAME,SIZE,FSTYPE,MOUNTPOINT --tree

Handy when that phantom 8 GB USB stick from last week still lurks in /media like an uninvited houseguest.

Memory and swap reality check

Check the ledger

The free command is a quick wallet peek, straightforward, and slightly judgemental:

$ free -h

Focus on the available column; that’s what you can still spend without the kernel reaching for its credit card (a.k.a. swap).

Real‑Time spy cam

# Refresh every two seconds, ordered by RAM gluttons
$ top -o %MEM

Prefer your monitoring colourful and charming? Try htop:

$ sudo apt install htop
$ htop

Use F6 to sort by RES (resident memory) and watch your browser tabs duke it out for supremacy.

Meet RAM’s couch‑surfing cousin

Swap steps in when RAM is full, think of it as sleeping on the living‑room sofa: doable, but slow and slightly undignified.

# Show active swap files or partitions
$ swapon --show

Seeing swap above 20 % during regular use? Either add RAM or conjure an emergency swap file:

$ sudo fallocate -l 2G /swapfile
$ sudo chmod 600 /swapfile
$ sudo mkswap /swapfile
$ sudo swapon /swapfile

Remember to append it to /etc/fstab so it survives a reboot.

Prefer clicking to typing

Yes, there’s a GUI for that. GNOME Disks and KSysGuard both display live graphs and won’t judge your typos. On Ubuntu, you can run:

$ sudo apt install gnome-disk-utility

Launch it from the menu and watch I/O spikes climb like toddlers on a sugar rush.

Quick reference cheat sheet

Show all mounts minus temp stuff
Command: df -hT -x tmpfs -x devtmpfs
Memory aid: df = disk fly‑over
Top ten heaviest directories
Command: du -h –max-depth=1 /path | sort -hr | head
Memory aid: du = directory weight
Interactive cleanup
Command: ncdu /
Memory aid: ncdu = du after espresso
Live RAM counter
Command: free -h
Memory aid: free = funds left
Spot memory‑hogging apps
Command: top -o %MEM
Memory aid: top = talent show
Swap usage
Command: swapon –show
Memory aid: swap on stage

Stick this list on your clipboard; your future self will thank you.

Wrapping up without a bow

You now own the detective kit for disk and memory mysteries, no cosmic metaphors, just straight talk with a wink. Run df -hT right now; if the numbers give you heartburn, take three deep breaths and start paging through ncdu. Storage leaks and RAM gluttons are inevitable, but letting them linger is optional.

Found an even better one‑liner? Drop it in the comments and make the rest of us look lazy. Until then, happy sleuthing, and may your logs stay trim and your swap forever bored.

Edge computing reshapes DevOps for the real-time era

A new frontier at your doorstep

When Amazon started placing delivery lockers in neighborhoods, packages arrived faster and more reliably. Edge computing follows a similar logic, bringing computational power closer to the user. Instead of sending data halfway around the world, edge computing processes it locally, dramatically reducing latency, enhancing privacy, and maintaining autonomy.

For DevOps teams, this shift isn’t trivial. Like switching from central mail hubs to neighborhood lockers, it demands new strategies and skills.

CI/CD faces a new reality

Classic cloud pipelines are centralized, much like a single distribution center. Edge computing flips that model upside-down, scattering deployments across numerous tiny locations. Deploying updates to thousands of edge devices isn’t the same as updating a handful of cloud servers.

DevOps teams now battle version drift, a scenario similar to managing software on thousands of smartphones with different versions. The solutions? Smaller, incremental updates and lightweight build artifacts, ensuring that pushing changes doesn’t overwhelm limited network bandwidth or hardware resources.

Designing for when things go dark

Planning a family dinner knowing there’s a possibility of a power outage means stocking up on candles and sandwiches. Similarly, edge devices must be designed for disconnection, ensuring operations continue uninterrupted during network downtime.

Offline-first architectures become critical here. Techniques like local queuing and eventual data reconciliation help edge applications function seamlessly, even if connectivity is lost for hours or days. Managing schema migrations carefully is crucial; it’s akin to updating recipes without knowing if family members received the memo.

Keeping data consistently in sync

Imagine organizing a city-wide neighborhood watch: push notifications ensure quick alerts, while pull mechanisms periodically fetch updates. Edge deployments use similar synchronization tactics.

Techniques such as Conflict-Free Replicated Data Types (CRDTs) help manage data consistency, even when devices are offline or slow to respond. DevOps engineers also need to factor in bandwidth budgeting, using intelligent compression and prioritizing data to ensure crucial information reaches its destination promptly.

Observability without seeing everything

Monitoring edge deployments is like managing a fleet of food trucks spread across the city. You can’t constantly keep an eye on every truck. Instead, you rely on periodic check-ins and key signals.

Telemetry sampling, data aggregation at the edge, and effective back-pressure management prevent network floods. Selecting a few meaningful metrics, like checking a truck’s gas gauge rather than tracking every sandwich sold, helps quickly pinpoint issues without drowning in data.

Incident response across the edge

Responding to issues at thousands of remote locations is challenging, like troubleshooting vending machines scattered nationwide without direct access.

Edge incident response leverages runbook templates, policy-as-code, and remote diagnostics tools. Because traditional SSH access isn’t always viable, tactics like automated self-healing and structured escalation paths blending central SRE teams with local staff become indispensable.

Bridging cloud and edge

Integrating IoT devices into your infrastructure is similar to securely registering visitors at a large event, you need clear identification, managed credentials, and accurate headcounts.

Edge computing uses secure onboarding, rotating credentials, and message brokers that maintain state coherence across the network. Digital twins represent device states virtually, helping maintain consistent and accurate information between edge and cloud environments. Cost-effective strategies determine whether workloads run locally or in centralized clouds.

Preparing for what’s next

Edge computing evolves rapidly, with emerging standards like WebAssembly (WASM) running applications directly at the edge, and maturing tools like OpenTelemetry simplifying observability.

DevOps teams should embrace these changes early. Developing skills in hardware awareness and basic radio frequency (RF) knowledge becomes increasingly valuable. Experimenting now, rigorously measuring results, and sharing insights ensures teams stay ahead.

Innovate and adapt for the road ahead

Edge computing is reshaping DevOps in real-time. Thriving in this era requires adapting practices, tooling, and mindset. Bring your computational lockers closer to home, plan proactively for network disruptions, streamline synchronization, enhance remote observability, and respond intelligently to incidents.

By preparing today, your DevOps team can confidently navigate tomorrow’s distributed landscape. Embracing edge computing means more than just keeping pace with technology; it positions your team to deliver faster, more reliable services, capitalize on emerging business opportunities, and maintain a competitive advantage. Investing now in the right tools, processes, and skills not only safeguards against future challenges but also unlocks potential for innovation, growth, and sustained success in a rapidly evolving technological world.

In short, the future belongs to those who embrace change and adapt quickly; let your team be among them.

July 3, 2025 by Fernando SRE Cloud stuff DevOps stuff SRE stuff

Free that stuck Linux port and get on with your day

A rogue process squatting on port 8080 is the tech-equivalent of leaving your front-door key in the lock: nothing else gets in or out, and the neighbours start gossiping. Ports are exclusive party venues; one process per port, no exceptions. When an app crashes, restarts awkwardly, or you simply forget it’s still running, it grips that port like a toddler with the last cookie, triggering the dreaded “address already in use” error and freezing your deployment plans.

Below is a brisk, slightly irreverent field guide to evicting those squatters, gracefully when possible, forcefully when they ignore polite knocks, and automatically so you can get on with more interesting problems.

When ports act like gate crashers

Ports are finite. Your Linux box has 65535 of them, but every service worth its salt wants one of the “good seats” (80, 443, 5432…). Let a single zombie process linger, and you’ll be running deployment whack-a-mole all afternoon. Keeping ports free is therefore less superstition and more basic hygiene, like throwing out last night’s takeaway before the office starts to smell.

Spot the culprit

Before brandishing a digital axe, find out who is hogging the socket.

lsof, the bouncer with the clipboard

sudo lsof -Pn -iTCP:8080 -sTCP:LISTEN

lsof prints the PID, the user, and even whether our offender is IPv4 or IPv6. It’s as chatty as the security guard who tells you exactly which cousin tried to crash the wedding.

ss, the Formula 1 mechanic

Modern kernels prefer ss, it’s faster and less creaky than netstat.

sudo ss -lptn sport = :8080

fuser, the debt collector

When subtlety fails, fuser spells out which processes own the file or socket:

sudo fuser -v 8080/tcp

It displays the PID and the user, handy for blaming Dave from QA by name.

Tip: Add the -k flag to fuser to terminate offenders in one swoop, great for scripts, dangerous for fingers-on-keyboard humans.

Gentle persuasion first

A well-behaved process will exit graciously if you offer it a polite SIGTERM (15):

kill -15 3245     # give the app a chance to clean up

Think of it as tapping someone’s shoulder at closing time: “Finish your drink, mate.”

If it doesn’t listen, escalate to SIGINT (2), the Ctrl-C of signals, or SIGHUP (1) to make daemons reload configs without dying.

Bring out the big stick

Sometimes you need the digital equivalent of cutting the mains power. SIGKILL (9) is that guillotine:

kill -9 3245      # immediate, unsentimental termination

No cleanup, no goodbye note, just a corpse on the floor. Databases hate this, log files dislike it, and system-wide supervisors may auto-restart the process, so use sparingly.

One-liners for the impatient

sudo kill -9 $(sudo ss -lptn sport = :8080 | awk 'NR==2{split($NF,a,"pid=");split(a[2],b,",");print b[1]}')

Single line, single breath, done. It’s the Fast & Furious of port freeing, but remember: copy-paste speed correlates strongly with “oops-I-just-killed-production”.

Automate the cleanup

A pocket Bash script

#!/usr/bin/env bash
port=${1:-3000}
pid=$(ss -lptn "sport = :$port" | awk 'NR==2 {split($NF,a,"pid="); split(a[2],b,","); print b[1]}')

if [[ -n $pid ]]; then
  echo "Port $port is busy (PID $pid). Sending SIGTERM."
  kill -15 "$pid"
  sleep 2
  kill -0 "$pid" 2>/dev/null && echo "Still alive; escalating..." && kill -9 "$pid"
else
  echo "Port $port is already free."
fi

Drop it in ~/bin/freeport, mark executable, and call freeport 8080 before every dev run. Fewer keystrokes, fewer swearwords.

systemd, your tireless janitor

Create a watchdog service so the OS restarts your app only when it exits cleanly, not when you manually murder it:

[Unit]
Description=Watchdog for MyApp on 8080

[Service]
ExecStart=/usr/local/bin/myapp
Restart=on-failure
RestartPreventExitStatus=64   # don’t restart if we SIGKILLed

Enable with systemctl enable myapp.service, grab coffee, forget ports ever mattered.

Ansible for the herd

- name: Free port 8080 across dev boxes
  hosts: dev
  become: true
  tasks:
    - name: Terminate offender on 8080
      shell: |
        pid=$(ss -lptn 'sport = :8080' | awk 'NR==2{split($NF,a,"pid=");split(a[2],b,",");print b[1]}')
        [ -n "$pid" ] && kill -15 "$pid" || echo "Nothing to kill"

Run it before each CI deploy; your colleagues will assume you possess sorcery.

A few cautionary tales

Containers restart themselves. Kill a process inside Docker, and the orchestrator may spin it right back up. Either stop the container or adjust restart policies.
Dependency dominoes. Shooting a backend API can topple every microservice that chats to it. Check systemctl status or your Kubernetes liveness probes before opening fire .
Sudo isn’t seasoning. Use it only when the victim process belongs to another user. Over-salting scripts with sudo causes security heartburn.

Wrap-up

Freeing a port isn’t arcane black magic; it’s janitorial work that keeps your development velocity brisk and your ops team sane. Identify the squatter, ask it nicely to leave, evict it if it refuses, and automate the routine so you rarely have to think about it again. Got a port-conflict horror story involving 3 a.m. pager alerts and too much caffeine? Tell me in the comments, schadenfreude is a powerful teacher.

Now shut that laptop lid and actually get on with your day. The ports are free, and so are you.

June 28, 2025 by Fernando SRE DevOps stuff Linux Stuff SRE stuff

Why Kubernetes Ingress feels outdated and Gateway API is stepping up

Kubernetes has transformed container orchestration, rapidly pushing the boundaries of scalability and flexibility. Yet some core components haven’t evolved as gracefully. Kubernetes Ingress is a prime example; it’s beginning to feel like using an old flip phone when everyone else has moved on to smartphones.

What’s driving this shift away from the once-reliable Ingress, and why are more Kubernetes professionals turning to Gateway API?

The rise and limits of Kubernetes Ingress

When Kubernetes introduced Ingress, its appeal lay in its simplicity. Its job was straightforward: route HTTP and HTTPS traffic into Kubernetes clusters predictably. Like traffic lights at a busy intersection, it provided clear and reliable outcomes: set paths and hostnames, and your Ingress controller (NGINX, Traefik, or others) took care of the rest.

However, as Kubernetes workloads grew more complex, this simplicity became restrictive. Teams began seeking advanced capabilities such as canary deployments, complex traffic management, support for additional protocols, and finer control. Unfortunately, Ingress remained static, forcing teams to rely on cumbersome vendor-specific customizations.

Why Ingress now feels outdated

Ingress still performs adequately, but managing it becomes increasingly cumbersome as complexity rises. It’s comparable to owning a reliable but outdated vehicle; it gets you to your destination but lacks modern conveniences. Here’s why Ingress feels out-of-date:

Limited protocol support – Only HTTP and HTTPS are supported natively. If your applications require gRPC, TCP, or UDP, you’re out of luck.
Vendor lock-in with annotations – Advanced routing features and authentication mechanisms often require vendor-specific annotations, locking you into particular solutions.
Rigid permission models – Managing shared control across multiple teams is complicated and inefficient, similar to having a single TV remote for an entire household.
No evolutionary path – Ingress will remain stable but static, unable to evolve as the Kubernetes ecosystem demands greater flexibility.

Gateway API offers a modern alternative

Gateway API isn’t merely an upgraded Ingress; it’s a fundamental rethink of how Kubernetes handles external traffic. It cleanly separates roles and responsibilities, streamlining interactions between network administrators, platform teams, and developers. Think of it as a well-run restaurant: chefs, managers, and servers each have clear roles, ensuring smooth and efficient operation.

Additionally, Gateway API supports multiple protocols, including gRPC, TCP, and UDP, natively. This eliminates reliance on awkward annotations and vendor lock-in, resembling an upgrade from single-purpose appliances to versatile multi-function tools that adapt smoothly to emerging needs.

When Gateway API becomes essential

Gateway API won’t suit every situation, but specific scenarios benefit from its use. Consider these questions:

Do your applications require sophisticated traffic handling, like canary deployments or traffic mirroring?
Are your services utilizing protocols beyond HTTP and HTTPS?
Is your Kubernetes cluster shared among multiple teams, each needing distinct control?
Do you seek portability across cloud providers and wish to avoid vendor lock-in?
Do you often desire modern features that are unavailable through traditional Ingress?

Answering “yes” to any of these indicates that Gateway API isn’t just helpful, it’s essential.

Deciding to move forward

Ingress isn’t entirely obsolete. For straightforward HTTP/HTTPS routing for smaller services, it remains effective. But as soon as your needs scale up, involve complex traffic management, or require clear team boundaries, Gateway API becomes the superior choice.

Technology continuously advances, and your infrastructure must evolve with it. Gateway API isn’t a futuristic solution; it’s already here, enhancing your Kubernetes deployments with greater intelligence, flexibility, and manageability.

When better tools appear, upgrading isn’t merely sensible, it’s crucial. Gateway API represents precisely this meaningful advancement, ensuring your Kubernetes environment remains robust, adaptable, and ready for whatever comes next.

June 26, 2025 by Fernando SRE Cloud stuff DevOps stuff Kubernetes SRE stuff

Achieving perfect elasticity in Kubernetes with multidimensional autoscaling

Running a Kubernetes environment can feel like a high-stakes game of guesswork. We estimate our application’s needs, define our resource requests, and hope we’ve struck the right balance. Too generous, and we’re paying for cloud resources that sit idle. Too conservative, and we risk sluggish performance or critical outages when real-world demand spikes. It’s a constant, stressful effort to manually tune a system that is inherently dynamic.

There is, however, a more elegant path. It involves moving away from this static guesswork and towards building a truly adaptive infrastructure. This is not about simply adding more tools; it’s about creating a self-regulating system that breathes with the rhythm of your workload. This is the core promise of a well-orchestrated Kubernetes autoscaling strategy. Let’s explore how to build it, piece by piece.

The three pillars of autoscaling

To build our adaptive system, we need to understand its three fundamental components. Think of them as the different ways a professional restaurant kitchen responds to a dinner rush.

The Horizontal Pod Autoscaler HPA

When a flood of orders hits the kitchen, the head chef doesn’t ask each cook to work twice as fast. The first, most logical step is to bring more cooks to the line. This is precisely what the Horizontal Pod Autoscaler does. It acts as the kitchen’s manager, watching the incoming demand (typically CPU or memory usage). As orders pile up, it adds more identical pod replicas, more “cooks”, to handle the load. When the rush subsides, it sends some cooks home, ensuring you’re only paying for the staff you need. It’s the frontline response to fluctuating demand.

The Vertical Pod Autoscaler VPA

Now, consider a specialized station, like the grill. What if the single grill cook is overwhelmed, not by the number of orders, but because their workspace is too small and inefficient? Simply adding another grill cook might just create more chaos in a cramped space. The better solution is to give the specialist a bigger, better grill station. This is the domain of the Vertical Pod Autoscaler. The VPA doesn’t change the number of pods. Instead, it meticulously observes the real-world resource consumption of a single pod over time and adjusts its allocated CPU and memory, its “workspace”, to be the perfect size. It answers the question, “How much power does this one cook need to do their job perfectly?”

The Cluster Autoscaler CA

What happens if the kitchen runs out of physical space? You can’t add more cooks or bigger grills if there’s no room for them. This is where the Cluster Autoscaler comes in. It is the architect of the kitchen itself. The CA doesn’t pay attention to individual orders or cooks. Its sole focus is space. When it sees pods that can’t be scheduled because no node has enough capacity, our “cooks without a counter”, it expands the kitchen by adding new nodes to the cluster. Conversely, when it sees entire sections of the kitchen sitting empty for too long, it smartly downsizes the space to keep operational costs low.

From static blueprints to dynamic reality

When we first deploy an application on Kubernetes, we manually define its resources.requests, and resources.limits. This is like creating a static architectural blueprint for our kitchen. We draw the lines based on our best assumptions.

But a blueprint doesn’t capture the chaotic, dynamic flow of a real dinner service. An application’s actual needs are rarely static. This is where the VPA transforms our approach. It moves us from relying on a fixed blueprint to observing the kitchen’s real-time workflow. It provides the data-driven intelligence to continuously refine and optimize our initial design, shifting us from a world of reactive fixes to one of proactive optimization.

How a great platform elevates the craft

Anyone can assemble a kitchen, but the difference between a home setup and a Michelin-star facility lies in the integration, quality, and advanced tooling. In the Kubernetes world, this is the value a managed platform like Google Kubernetes Engine (GKE) provides.

While HPA, VPA, and CA are open-source concepts, managing them yourself is like building and maintaining that professional kitchen from scratch. GKE offers them as fully managed, seamlessly integrated services.

Effortless setup. Enabling these autoscalers in GKE is a simple, declarative action, removing significant operational overhead.
An expert consultant, the VPA’s “recommendation-only” mode is a game-changer. It’s like having a master chef observe your kitchen and leave detailed notes on how to improve efficiency, all without interrupting service. This free, built-in guidance is invaluable for right-sizing your workloads.

However, GKE’s most significant innovation is a technique that solves a classic Kubernetes puzzle: The Multidimensional Pod Autoscaler (MPA).

Historically, trying to use HPA (more cooks) and VPA (better workspaces) on the same workload was a recipe for conflict. The two would issue contradictory signals, leading to instability. GKE’s MPA acts as the master head chef, intelligently coordinating both actions. It allows you to scale horizontally and vertically at the same time, ensuring your kitchen can both add more cooks and give them better equipment in one fluid motion. This is the ultimate expression of elasticity.

A practical blueprint for your strategy

With this understanding, you can now design a robust autoscaling strategy:

For Your Stateless Dishes (e.g., web frontends, APIs)
Start with the HPA to handle variable traffic. As you mature, graduate to the MPA to achieve a superior level of efficiency by scaling in both dimensions.
For Your Stateful Specialties (e.g., databases, message queues)
Rely on the VPA to meticulously right-size these critical components, ensuring they always have the exact resources needed for stable and reliable performance.
For the Entire Kitchen
Let the Cluster Autoscaler work in the background as your ever-vigilant architect, always ensuring there is enough underlying infrastructure for your applications to thrive.

An autonomous future awaits

We started with a stressful guessing game and have arrived at the blueprint for an intelligent, self-regulating infrastructure. By thoughtfully combining HPA, VPA, and CA, we evolve from being reactive system administrators to proactive cloud architects.

This journey culminates with tools like GKE’s Multidimensional Pod Autoscaler. The MPA is more than just another feature; it represents a paradigm shift. It solves the fundamental conflict between scaling out and scaling up, allowing our applications to adapt with a new level of intelligence. With MPA, workloads can simultaneously handle sudden traffic surges by adding replicas, while continuously right-sizing the resource footprint of each instance. This dual-axis scaling eliminates the trade-offs we once had to make, unlocking a state of true, cost-effective elasticity.

The path to this autonomous state is an incremental one. The best first step is to harness the power of observation. Start today by enabling VPA in recommendation-only mode on a non-production workload. Listen to its insights, understand your application’s real needs, and use that data to transform your static blueprints. This is the foundational skill that will empower you to confidently adopt multidimensional scaling, creating a dynamic, living system ready to meet any challenge that comes its way.

June 21, 2025 by Fernando SRE Cloud stuff DevOps stuff Kubernetes

Linux commands for the pathologically curious

We all get comfortable. We settle into our favorite chair, our favorite IDE, and our little corner of the Linux command line. We master ls, grep, and cd, and we walk around with the quiet confidence of someone who knows their way around. But the terminal isn’t a neat, modern condo; it’s a sprawling, old mansion filled with secret passages, dusty attics, and bizarre little tools left behind by generations of developers.

Most people stick to the main hallways, completely unaware of the weird, wonderful, and handy commands hiding just behind the wallpaper. These aren’t your everyday tools. These are the secret agents, the oddballs, and the unsung heroes of your operating system. Let’s meet a few of them.

The textual anarchists

Some commands don’t just process text; they delight in mangling it in beautiful and chaotic ways.

First, meet rev, the command-line equivalent of a party trick that turns out to be surprisingly useful. It takes whatever you give it and spits it out backward.

echo "desserts" | rev

This, of course, returns stressed. Coincidence? The terminal thinks not. At first glance, you might dismiss it as a tool for a nerdy poetry slam. But the next time you’re faced with a bizarrely reversed data string from some ancient legacy system, you’ll be typing rev and looking like a wizard.

If rev is a neat trick, shuf is its chaotic cousin. This command takes the lines in your file and shuffles them into a completely random order.

# Create a file with a few choices
echo -e "Order Pizza\nDeploy to Production\nTake a Nap" > decisions.txt

# Let the terminal decide your fate
shuf -n 1 decisions.txt

Why would you want to do this? Maybe you need to randomize a playlist, test an algorithm, or run a lottery for who has to fix the next production bug. shuf is an agent of chaos, and sometimes, chaos is exactly what you need.

Then there’s tac, which is cat spelled backward for a very good reason. While the ever-reliable cat shows you a file from top to bottom, tac shows it to you from bottom to top. This might sound trivial, but anyone who has ever tried to read a massive log file will see the genius.

# Instantly see the last 5 errors in a huge log file
tac /var/log/syslog | grep -i "error" | head -n 5

This lets you get straight to the juicy, most recent details without an eternity of scrolling.

The obsessive organizers

After all that chaos, you might need a little order. The terminal has a few neat freaks ready to help.

The nl command is like cat’s older, more sophisticated cousin who insists on numbering everything. It adds formatted line numbers to a file, turning a simple text document into something that looks official.

# Add line numbers to a script
nl backup_script.sh

Now you can professionally refer to “the critical bug on line 73” during your next code review.

But for true organizational bliss, there’s column. This magnificent tool takes messy, delimited text and formats it into beautiful, perfectly aligned columns.

# Let's say you have a file 'users.csv' like this:
# Name,Role,Location
# Alice,Dev,Remote
# Bob,Sysadmin,Office

cat users.csv | column -t -s,

This command transforms your comma-vomit into a table fit for a king. It’s so satisfying it should be prescribed as a form of therapy.

The tireless workers

Next, we have the commands that just do their job, repeatedly and without complaint.

In the entire universe of Linux, there is no command more agreeable than yes. Its sole purpose in life is to output a string over and over until you tell it to stop.

# Automate the confirmation for a script that keeps asking
yes | sudo apt install my-awesome-package

This is the digital equivalent of nodding along until the installation is complete. It is the ultimate tool for the lazy, the efficient, and the slightly tyrannical system administrator.

If yes is the eternal optimist, watch is the eternal observer. This command executes another program periodically, showing its output in real time.

# Monitor the number of established network connections every 2 seconds
watch -n 2 "ss -t | grep ESTAB | wc -l"

It turns your terminal into a live dashboard. It’s the command-line equivalent of binge-watching your system’s health, and it’s just as addictive.

For an even nosier observer, try dstat. It’s the town gossip of your system, an all-in-one tool that reports on everything from CPU stats to disk I/O.

# Get a running commentary of your system's vitals
dstat -tcnmd

This gives you a timestamped report on cpu, network, disk, and memory usage. It’s like top and iostat had a baby and it came out with a Ph.D. in system performance.

The specialized professionals

Finally, we have the specialists, the commands built for one hyper-specific and crucial job.

The look command is a dictionary search on steroids. It performs a lightning-fast search on a sorted file and prints every line that starts with your string.

# Find all words in the dictionary starting with 'compu'
look compu /usr/share/dict/words

It’s the hyper-efficient librarian who finds “computer,” “computation,” and “compulsion” before you’ve even finished your thought.

For more complex relationships, comm acts as a file comparison counselor. It takes two sorted files and tells you which lines are unique to each and which they share.

# File 1: developers.txt (sorted)
# alice
# bob
# charlie

# File 2: admins.txt (sorted)
# alice
# david
# eve

# See who is just a dev, just an admin, or both
comm developers.txt admins.txt

Perfect for figuring out who has access to what, or who is on both teams and thus doing twice the work.

The desire to procrastinate productively is a noble one, and Linux is here to help. Meet at. This command lets you schedule a job to run once at a specific time.

# Schedule a server reboot for 3 AM tomorrow.
# After hitting enter, you type the command(s) and press Ctrl+D.
at 3:00am tomorrow
reboot
^D (Ctrl+D)

Now you can go to sleep and let your past self handle the dirty work. It’s time travel for the command line.

And for the true control freak, there’s chrt. This command manipulates the real-time scheduling priority of a process. In simple terms, you can tell the kernel that your program is a VIP.

# Run a high-priority data processing script
sudo chrt -f 99 ./process_critical_data.sh

This tells the kernel, “Out of the way, peasants! This script is more important than whatever else you were doing.” With great power comes great responsibility, so use it wisely.

Keep digging

So there you have it, a brief tour of the digital freak show lurking inside your Linux system. These commands are the strange souvenirs left behind by generations of programmers, each one a solution to a problem you probably never knew existed. Your terminal is a treasure chest, but it’s one where half the gold coins might just be cleverly painted bottle caps. Each of these tools walks the fine line between a stroke of genius and a cry for help. The fun part isn’t just memorizing them, but that sudden, glorious moment of realization when one of these oddballs becomes the only thing in the world that can save your day.

June 14, 2025 by Fernando SRE DevOps stuff Linux 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 Fernando SRE 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.

Read: You grab your wild data from a source.
Transform: You perform some arcane logic to clean, shape, enrich, or otherwise domesticate it.
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 Fernando SRE 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 Fernando SRE Cloud stuff Computer Science stuff