Podman – Data/ML Engineer Blog

Podman for Data Engineers: What It Is (and Isn’t) in Containerization & Orchestration

You want Docker-like workflows without a background daemon, tighter security, and clean handoffs to Kubernetes. Enter Podman. But let’s be blunt: Podman is a container engine with orchestration adjacent features—not a full-blown cluster orchestrator.

Why this matters

Local dev speed and prod parity are everything in data engineering. You need to:

Build images quickly and securely.
Run services rootless on laptops, CI, or hardened servers.
Hand off YAML cleanly to k8s when it’s time to scale.

Podman gives you a daemonless, Docker-compatible toolset that slots neatly into these needs, plus a few unique tricks (pods, systemd, and kube play/generate kube) that make day-2 ops less painful.

Concept & Architecture — Podman in one page

Positioning

Container engine: Daemonless CLI to build/run containers. Docker-compatible commands (podman run, podman build).
Security-first: Rootless by default, SELinux/AppArmor aware, user namespaces, cgroups.
Pods: Group containers that share a network namespace (mirrors Kubernetes’ pod model).
Orchestration-adjacent:
- Systemd integration via podman generate systemd for host-level lifecycle.
- Kubernetes integration via podman generate kube and podman kube play to bridge local → cluster.
Ecosystem: Buildah (build), Skopeo (copy/sign images), Podman Compose (docker-compose analogue), Podman Machine (VM on macOS/Windows).

What it is not

Not a cluster orchestrator. No built-in scheduling across nodes, no controllers. For multi-node orchestration, use Kubernetes (or OpenShift). Podman’s job is to make that handoff clean.

Quick mental model: Podman vs Docker vs Kubernetes

Capability	Podman	Docker	Kubernetes (for context)
Daemon required	No (daemonless)	Yes (`dockerd`)	N/A (control plane & kubelet)
Rootless runtime	First-class	Available, newer	N/A (runs as system components)
Pods	Yes (local/k8s-style grouping)	No (compose services, but no pods)	Native
Compose-equivalent	Podman Compose	Docker Compose	N/A
Systemd integration	Native (`generate systemd`)	Indirect	N/A
K8s handoff	`generate kube` / `kube play`	Limited (third-party)	Target runtime
Best fit	Secure local/edge, CI, servers	Ubiquitous local dev	Cluster orchestration

Core workflows (with concise examples)

1) Run containers rootless (safely)

# Pull & run MongoDB rootless on host network namespace of a pod
podman run -d --name mongo \
  -p 27017:27017 \
  -v mongo-data:/data/db \
  docker.io/library/mongo:7
podman ps

2) Use Pods to mirror k8s topology locally

# Create a pod with a shared network namespace
podman pod create --name demo-pod -p 8080:8080

# Backend container in the pod
podman run -d --name api --pod demo-pod \
  -e MONGO_URL=mongodb://localhost:27017 \
  docker.io/library/node:20 node server.js

# Sidecar logger in the same pod (shares localhost)
podman run -d --name logger --pod demo-pod \
  docker.io/library/fluentd:latest

3) Generate systemd units for day-2 ops

# Turn an existing container into a persistent systemd service
podman generate systemd --name api --files --new
# Moves systemd unit files to the right place
sudo mv container-api.service /etc/systemd/system/
sudo systemctl daemon-reload
sudo systemctl enable --now container-api

Result: host reboots → service starts automatically. No external orchestrator required for a single host.

4) Round-trip to Kubernetes YAML

# From local pod/containers → k8s manifest
podman generate kube demo-pod > demo-pod.yaml

# From k8s manifest → local run (great for testing manifests)
podman kube play demo-pod.yaml

5) Docker-compose-style dev with Podman Compose

# docker-compose.yml works for most stacks
podman-compose up -d
podman-compose logs -f

6) Build images with Buildah or Podman

# Dockerfile build via Podman
podman build -t analytics:latest .

# Or buildah for fine-grained, daemonless builds
buildah bud -t analytics:latest .

Real example: Local ETL service + Kafka sidecar in a pod

# 1) Create the pod (shared network namespace)
podman pod create --name etl-pod -p 9000:9000

# 2) Run Kafka (single-broker dev) inside the pod
podman run -d --name kafka --pod etl-pod \
  -e KAFKA_CFG_LISTENERS=PLAINTEXT://:9092 \
  -e KAFKA_CFG_ADVERTISED_LISTENERS=PLAINTEXT://localhost:9092 \
  bitnami/kafka:3

# 3) Run a Python ETL API that consumes Kafka
podman run -d --name etl-api --pod etl-pod \
  -e KAFKA_BOOTSTRAP=localhost:9092 \
  -e OUTPUT_BUCKET=s3://my-dev-landing \
  ghcr.io/example/etl-api:latest

# 4) Export to k8s when ready
podman generate kube etl-pod > etl-pod.yaml

Why this works: The ETL API and Kafka share localhost inside the pod—identical to how containers talk inside a Kubernetes pod. Your environment variables and ports stay consistent when you promote to k8s.

Best practices (and honest caveats)

Security & Isolation

Prefer rootless containers. Map volumes to user-writable paths; avoid host-writable privileged mounts.
Keep SELinux/AppArmor profiles enabled; don’t disable for convenience.
Use read-only root FS (--read-only) and drop capabilities (--cap-drop=ALL plus add back only what you need).

Networking & Pods

Use pods to model microservices that must share localhost. It reduces “works on my machine” drift before k8s.
Assign explicit ports only at the pod boundary. Internal containers should bind to localhost.

Images & Supply Chain

Sign and verify images with Skopeo/cosign.
Keep images minimal; pin tags (:7.0.14) for reproducibility in CI.

Systemd Integration

For single-host services or edge nodes, generate systemd units instead of hacking shell scripts.
Set restart policies and health checks in systemd (Restart=on-failure, ExecStartPre=/usr/bin/podman healthcheck run ...).

Kubernetes Handoff

Treat podman generate kube output as a starting point. Add k8s-native resources (Deployments, HPA, PDBs, ConfigMaps, Secrets) before deploying to a cluster.
Validate with podman kube play locally to catch YAML mistakes early.

Tooling & Dev Experience

macOS/Windows users: use Podman Machine (a managed VM). Don’t expect bare-metal performance parity with Linux.
podman-compose covers most Compose files, but some advanced features may need tweaks.

Common Pitfalls

Expect minor CLI differences vs Docker; read error messages—Podman is honest but terse.
Rootless with bind mounts can hit permissions walls; fix with correct UID/GID mapping and :Z on SELinux systems.
Don’t confuse pod with cluster orchestration. For multi-node scheduling, you still need Kubernetes.

FAQ for the “is it orchestration?” question

Q: “Is Podman an orchestration product?”
A: It’s a container engine with orchestration helpers (pods, systemd units, kube round-trip). For real multi-node orchestration, use Kubernetes. Podman makes the path to Kubernetes smoother.
Q: “Can I run production with only Podman?”
A: Yes, for single-host or edge workloads—especially when systemd control is enough. For HA, autoscaling, and rollouts across nodes, graduate to Kubernetes.

Internal link ideas (add these on your site)

“Kubernetes 101 for Data Engineers: From Podman YAML to Deployments”
“Rootless Containers: Security Hardening Checklist”
“Buildah vs docker build: Image Supply Chain for CI”
“Designing Pods: Sidecars, Ambassadors, and Init Containers”
“From Docker Compose to Podman Compose: Migration Notes”

Conclusion & Takeaways

Podman is Docker-compatible, daemonless, and rootless-first—great for secure local dev, CI, and single-host deployments.
Pods give you k8s-like topology locally; generate kube / kube play smooths the handoff to Kubernetes.
It’s not a cluster orchestrator; think of it as the secure engine + day-2 helpers that get you to orchestration cleanly.

Call to action:
Start by containerizing one service with Podman, wrap it with a pod, export the YAML, and validate with podman kube play. Once that’s solid, wire in systemd for persistence—or promote to Kubernetes.

Image prompt

“A clean, modern diagram showing a developer workflow from Podman (rootless container + pod) to systemd on a single host and to Kubernetes via generate kube/kube play — minimalistic, high-contrast, isometric style, labeled arrows.”

Data/ML Engineer Blog