Packages

System monitoring (metrics, logs, traces — full observability)

We learn about outages from customers, not from the system

Log centralization and distributed tracing

We spend an hour finding which service failed — while prod is down

Alerting and proactive response to anomalies

Alerts fire at night, but we do not know what to do

Defining and managing SLI, SLO, SLA

Clients demand guarantees, but we cannot back them with numbers

Measuring availability (uptime) and latency

We say “the system is stable”, but there are no figures

Incident management (runbooks, operational procedures)

Every incident is chaos — nobody knows their role

Full incident response lifecycle

We fight fires, but they repeat a week later

Post-mortem analysis and process improvement

After an outage — silence; we only talk about learning

Root Cause Analysis (RCA)

We fixed the symptom, but the root cause is still there

Load testing and stress testing

It works with 100 users, but fails at 1000

Autoscaling and resource management optimization

We pay twice as much for servers as we need to

Automated testing (unit, integration, end-to-end)

Every release breaks something that used to work

Environment management and multi-environment workflows

On staging it works — on prod it does not

Standardization of repositories and CI/CD processes

Every project is wired differently — nobody can navigate it

Developer and preview environments (dev-test environments)

A developer waits three days to test a feature

Infrastructure as Code (IaC) and configuration management

The server was tuned for two months — impossible to reproduce

Secure secrets management

Database keys live in a GitHub repo

Performance testing and infrastructure optimization

The system is slow, but the bottleneck is unclear

Automated rollbacks and backups

After a bad release, recovery takes two hours

Dependency and library update management

Libraries are old; updating is scary — everything will break

Scalable and fault-tolerant infrastructure

Under load everything falls over, though we were warned

Disaster recovery and backup strategies

What if the whole data center fails? — We do not know

Release management and versioning

We cannot tell which version is running in prod

Pipeline as Code and test data management

CI/CD is black magic — nobody dares touch it

Multi-cloud, hybrid, and on-premises solutions

We fear lock-in to one cloud — we will lose flexibility

High-availability (HA) architecture

The site is down two hours a month — we lose clients and money

Disaster recovery and fault-tolerant systems

What if the cloud fails? We have no plan B

Advanced network configurations (peering, private endpoints)

Data crosses the public internet — that is not safe

Legacy migration to the cloud with minimal downtime

Old system on bare metal — we are afraid to move

Architecture optimization / re-architecture

The system grew organically — now it is slow and expensive

Serverless solutions (Lambda, Cloud Functions)

We run servers 24/7 though traffic is only during the day

Docker containerization

The app behaves differently on different machines

Kubernetes cluster design and deployment

K8s feels like magic — three months of setup and still scary

HPA / VPA autoscaling

Load is zero at night, but servers still burn money

Service mesh (Istio / Linkerd)

Microservices cannot talk securely and in a controlled way

Kubernetes observability and monitoring

Inside K8s is a black hole — we see nothing

Kubernetes cluster security

Any pod can reach secrets for the whole cluster

Kubernetes cost and resource optimization

We pay three times more for the cluster than we should

IAM and secrets management (e.g. Vault)

Nobody knows who has access to what

SAST / DAST / dependency scanning

Vulnerabilities in code still reach production

Infrastructure hardening and Zero Trust

We assume the internal network is safe — it is not

Business process automation (workflows, APIs)

People spend three hours a day on manual reports

Real-time data sync and event-driven architecture

Data diverges across systems — reporting becomes hell

Monolith → microservices migration

One monolith — even a small change needs a full release

Developer experience (onboarding, docs, tooling)

A new hire spends two weeks just setting up the environment

24/7 support, on-call, and maintenance

On weekends nobody can bring prod back up

IT audit, security audit, and evolution roadmap

We do not know how ready we are for attacks or growth