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ivanshamaev
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docker-data-environments

Create Docker build patterns and local compose environments for data engineering tools.

14|1|Updated May 5, 2026
Search Tags:#data engineering#docker#docker compose#airflow#spark#buildkit#multi-stage dockerfile
One-click install
npx skills add https://github.com/ivanshamaev/de-agent-skills --skill docker-data-environments
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: docker-data-environments
Source: https://github.com/ivanshamaev/de-agent-skills/tree/main/skills/docker_data_envs
Command: npx skills add https://github.com/ivanshamaev/de-agent-skills --skill docker-data-environments

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides production-minded, reproducible Docker builds and local containerized stacks for data engineering tools, reducing CI/CD build time, configuration drift, and insecure image practices.

Core Features & Use Cases

  • Multi-stage Dockerfiles for data tools: Efficient dbt, Spark, and Airflow images with clear build vs runtime separation.
  • Fast CI/CD via layer caching and BuildKit: Uses cache mounts for pip/apt to speed iterative builds.
  • Private registry and multi-platform delivery: Supports docker buildx for amd64/arm64 and pushing to ghcr.io or Harbor.
  • Local data engineering environment with Docker Compose: Includes a ready-to-run stack (Spark, Airflow, Kafka, MinIO, Postgres) for integration testing.
  • Build-time secrets and security hardening: Uses BuildKit secrets for private PyPI and recommends non-root, slim, read-only filesystem patterns.

Quick Start

Ask your agent to generate a multi-stage Dockerfile for your dbt project and a compose.yaml that brings up Spark + Airflow + Kafka + MinIO + Postgres locally with secure BuildKit secret usage.

Frequently Asked Questions about docker-data-environments

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a multi-stage Dockerfile for dbt, Spark, or Airflow?▼

Multi-stage Dockerfiles for data tools separate build dependencies from runtime environments, reducing final image size. You define a builder stage for installing packages, then copy only necessary artifacts to a minimal runtime base image.

How does BuildKit cache mounts speed up Docker builds for data engineering?▼

BuildKit cache mounts persist package manager downloads, like pip or apt caches, across Docker builds. This prevents re-downloading dependencies on every build, significantly reducing CI/CD build time for data engineering images.

Can I use Docker Compose to run a local data engineering stack with Spark, Airflow, and Kafka?▼

Yes, Docker Compose can orchestrate a ready-to-run local data engineering stack including Spark, Airflow, Kafka, MinIO, and Postgres. This provides a reproducible environment for integration testing on your local machine.

How do I use BuildKit secrets for private PyPI access in Docker builds?▼

BuildKit secrets allow you to securely pass private PyPI credentials during Docker builds without exposing them in the final image layers. You mount the secret temporarily during the build stage to install private dependencies safely.

Does docker buildx support multi-platform builds for amd64 and arm64 data tools?▼

Yes, docker buildx supports multi-platform builds for amd64 and arm64 architectures. This allows you to build and push multi-architecture data tool images to private registries like ghcr.io or Harbor from a single command.

What are the best practices for security hardening in Docker images for data tools?▼

Security hardening for Docker data tools involves using non-root users, minimal slim base images, and read-only filesystems. These practices minimize the attack surface and ensure secure container execution.

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