Dockerizing your project
Multi-stage Dockerfiles for each service, with a slim base image, a non-root user and a proper .dockerignore file.
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Docker makes application packaging more repeatable, but does not by itself remove differences in hardware, networking, configuration or data. We review the existing environment and prepare Dockerfiles and Compose configuration around the project requirements and acceptance tests.
This service packages and deploys applications with Docker and Compose. We review dependencies, the target platform, persistent data, access and recovery requirements before implementation. Dockerfiles, Compose configuration and deployment and rollback documentation are delivered within the agreed scope. Private registries, mirrors, image scanning and CI integration depend on project needs. A mirror reduces repeated downloads; offline operation requires all images and other build dependencies to be available and must be tested separately.
Multi-stage Dockerfiles for each service, with a slim base image, a non-root user and a proper .dockerignore file.
Services, networks, volumes and environment variables defined in compose.yaml, with a healthcheck and restart policy for every container.
Harbor or Docker Registry on your own infrastructure, plus a local pull-through mirror so builds don't depend on Docker Hub being reachable.
Image scanning with Trivy, dropped Linux capabilities, read-only filesystems where possible, and no containers with access to docker.sock.
Log driver and rotation settings so logs never fill the disk, and CPU and memory limits per container.
Separate ordinary application configuration from code and images. Do not bake passwords or keys into Dockerfiles, build arguments, image environment variables or committed files. Use build secret mounts and an appropriate runtime secrets mechanism. Define persistent storage, backups and recovery tests: removing and replacing a container can lose data in its writable layer. Logs, health checks, migrations and scheduled jobs also need an execution plan and an owner.
Publishing a database port such as 3306:3306 can expose it to other hosts. On Linux bridge networks, Docker rules can bypass the expected ufw path; test access externally for the actual backend and topology. A mutable tag such as latest is not a fixed rollback reference: record the exact digest and retain that image for rollback. Version tags can also change; maintain a separate update policy. depends_on alone does not establish application readiness; use a suitable health check with condition: service_healthy and test runtime failures separately. Review secrets and log size limits as well.
The number of services matters, and their shape matters more. A Laravel or Django app built the conventional way is usually quick to containerize. An older app tied to a specific PHP build, compiled extensions or files in fixed OS paths takes considerably longer.
Other factors are the number of environments, whether a private registry and mirror must be built from scratch, how much data has to move onto volumes, and whether image builds should be wired into CI. We set the exact scope after looking at the repository and the current server.
Built images do not automatically inherit later base-image fixes. Review scan results, dependency versions and available fixes, then rebuild, test and deploy in a controlled way. Scanning and rebuilding do not guarantee the absence of vulnerabilities. Review disk usage with docker system df and identify data and rollback images before cleanup. Test Engine, Compose and registry changes in a staging environment first.
We look at the codebase, dependencies, database and current deployment process, and decide what goes into containers and what stays outside.
We write the Dockerfiles, cut image size and build time, and run everything in a test environment.
We install and configure Docker Engine, move data onto volumes and put the services behind Nginx or Traefik.
You get the documentation, and we walk your team through a real update and a real rollback.
For most small and mid-sized projects, Docker Compose on one or two servers is enough. Kubernetes starts to make sense when you run many services, need automatic scaling, or have a team to maintain it. We'll assess this with you in the first session.
It can, as long as the data lives on a persistent volume with regular backups. For busy databases we usually recommend running them directly on a server or a dedicated VM. The call depends on data size and recovery requirements.
A private registry or mirror can reduce repeated downloads, but initial pulls, tag freshness checks and other build dependencies may still require external access. For offline operation, prepare the required images and dependencies in the authorized environment and separately test builds and recovery without external access.
Yes. We work with your developers on image structure, a local development setup with Compose, and the path to automated deployment. Your team can do the implementation, or we can.
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