How to use this image

All examples in this guide use the public image. If you've mirrored the repository for your own use (for example, to your Docker Hub namespace), update your commands to reference the mirrored image instead of the public one.

For example:

• Public image: dhi.io/<repository>:<tag>
• Mirrored image: <your-namespace>/dhi-<repository>:<tag>

For the examples, you must first use docker login dhi.io to authenticate to the registry to pull the images.

About this image

This Docker Hardened DataHub Ingestion image provides the datahub metadata ingestion CLI in two upstream variants:

Slim variant (default)

The slim variant is a minimal runtime image equivalent to the upstream acryldata/datahub-ingestion:<version>-slim tags. It bundles the most commonly used ingestion connectors:

• Sources: Snowflake, BigQuery, Redshift, MySQL, PostgreSQL, ClickHouse, Glue, dbt, Looker, LookML, Tableau, Power BI, Superset
• Object storage: S3, GCS, Azure Blob Storage (slim variants)
• Streaming: Kafka
• Sinks and core: datahub-rest, datahub-kafka, datahub-business-glossary

Locked variant (-locked)

The locked variant is hardened for airgap and regulated environments. It installs a minimal connector set (base, datahub-rest, datahub-kafka, s3-slim, gcs-slim, abs-slim) at build time and sets UV_INDEX_URL and PIP_INDEX_URL to an unreachable endpoint, so the image cannot reach PyPI at runtime. Use this variant when your deployment policy prohibits unaudited runtime package installation.

Run the datahub container

To display the version:

$ docker run --rm dhi.io/datahub-ingestion:<tag> --version

To list installed ingestion plugins (sources and sinks):

$ docker run --rm dhi.io/datahub-ingestion:<tag> check plugins

To run an ingestion recipe, mount it into the container and pass ingest -c <path>:

$ docker run --rm \
  -v /path/to/recipe.yml:/etc/datahub/recipe.yml:ro \
  -e DATAHUB_GMS_URL=https://your-gms.example.com \
  -e DATAHUB_GMS_TOKEN=$DATAHUB_GMS_TOKEN \
  dhi.io/datahub-ingestion:<tag> ingest -c /etc/datahub/recipe.yml

The container runs as the nonroot user (uid 65532); ensure mounted recipe and credential files are readable by that user.

Customizing the slim variant

If you need an ingestion source that is not in the slim variant, install the extra at build time with a multi-stage build on the -dev variant (which includes uv and apt):

# syntax=docker/dockerfile:1

# Stage 1: install an extra source into a side venv
FROM dhi.io/datahub-ingestion:<tag>-dev AS build
RUN /opt/datahub/bin/uv pip install --python /opt/datahub/bin/python \
    'acryl-datahub[mongodb]'

# Stage 2: runtime image with the extra source
FROM dhi.io/datahub-ingestion:<tag>
COPY --from=build /opt/datahub /opt/datahub

For environments that prohibit runtime package installation (locked variant), build a customized image with the extras baked in at build time instead.

Recipe files

DataHub ingestion is driven by YAML recipes that describe sources, sinks, and transformers. Mount recipes as read-only files and pass them to datahub ingest:

$ docker run --rm \
  -v "$(pwd)/recipes:/recipes:ro" \
  dhi.io/datahub-ingestion:<tag> ingest -c /recipes/snowflake.yml

For the full recipe reference and source-specific configuration options, see the upstream metadata ingestion docs⁠.

Image variants

Docker Hardened Images come in different variants depending on their intended use. Image variants are identified by their tag.

• Runtime variants are designed to run your application in production. These images are intended to be used either directly or as the FROM image in the final stage of a multi-stage build. These images typically:

  • Run as a nonroot user
  • Do not include a shell or a package manager
  • Contain only the minimal set of libraries needed to run the app

• Build-time variants typically include dev in the tag name and are intended for use in the first stage of a multi-stage Dockerfile. These images typically:

  • Run as the root user
  • Include a shell and package manager
  • Are used to build or compile applications

• FIPS variants include fips in the variant name and tag. They come in both runtime and build-time variants. These variants use cryptographic modules that have been validated under FIPS 140, a U.S. government standard for secure cryptographic operations. For example, usage of MD5 fails in FIPS variants. Note that database sources which authenticate over SCRAM-SHA-256 (such as the postgres source) fail on the FIPS variants with could not generate nonce, because libpq cannot generate the SCRAM client nonce under the FIPS OpenSSL provider. For SCRAM-authenticated database ingestion, use a non-FIPS variant or configure the database for a FIPS-compatible auth method (for example, client certificates).

To view the image variants and get more information about them, select the Tags tab for this repository, and then select a tag.

Migrate to a Docker Hardened Image

To migrate your application to a Docker Hardened Image, you must update your Dockerfile. At minimum, you must update the base image in your existing Dockerfile to a Docker Hardened Image. This and a few other common changes are listed in the following table of migration notes.

The upstream acryldata/datahub-ingestion image places the datahub binary on PATH and sets the entrypoint to datahub. The DHI image keeps the same entrypoint and ships datahub at /opt/datahub/bin/datahub (the image's PATH is /opt/datahub/bin:/opt/python/bin:… so datahub … resolves the same way), so existing docker run … ingest -c … invocations work without modification. One important runtime difference is that the upstream image runs as the datahub user (uid 1000), while the Docker Hardened Image runs as the nonroot user (uid 65532).

The following steps outline the general migration process.

1. Find hardened images for your app.

   A hardened image may have several variants. Inspect the image tags and find the image variant that meets your needs.

2. Update the base image in your Dockerfile.

   Update the base image in your application's Dockerfile to the hardened image you found in the previous step. For framework images, this is typically going to be an image tagged as dev because it has the tools needed to install packages and dependencies.

3. For multi-stage Dockerfiles, update the runtime image in your Dockerfile.

   To ensure that your final image is as minimal as possible, you should use a multi-stage build. All stages in your Dockerfile should use a hardened image. While intermediary stages will typically use images tagged as dev, your final runtime stage should use a non-dev image variant.

4. Install additional packages

   Docker Hardened Images contain minimal packages in order to reduce the potential attack surface. You may need to install additional packages in your Dockerfile. To view if a package manager is available for an image variant, select the Tags tab for this repository. To view what packages are already installed in an image variant, select the Tags tab for this repository, and then select a tag.

   Only images tagged as dev typically have package managers. You should use a multi-stage Dockerfile to install the packages. Install the packages in the build stage that uses a dev image. Then, if needed, copy any necessary artifacts to the runtime stage that uses a non-dev image.

   For Alpine-based images, you can use apk to install packages. For Debian-based images, you can use apt-get to install packages.

Troubleshooting migration

The following are common issues that you may encounter during migration.

General debugging

The hardened images intended for runtime don't contain a shell nor any tools for debugging. The recommended method for debugging applications built with Docker Hardened Images is to use Docker Debug⁠ to attach to these containers. Docker Debug provides a shell, common debugging tools, and lets you install other tools in an ephemeral, writable layer that only exists during the debugging session.

Permissions

By default image variants intended for runtime, run as a nonroot user. Ensure that necessary files and directories are accessible to that user. You may need to copy files to different directories or change permissions so your application running as a nonroot user can access them.

To view the user for an image variant, select the Tags tab for this repository.

If you mount recipe files or credential files, ensure they are readable by uid 65532.

Locked variant: PyPI access blocked

The locked variant intentionally blocks PyPI by setting UV_INDEX_URL and PIP_INDEX_URL to an unreachable endpoint. If you see Connection refused or Name or service not known errors from uv or pip at runtime, you are running the locked variant by design. Either bake the package into a custom image with the dev variant, or switch to the slim variant.

Privileged ports

Non-dev hardened images run as a nonroot user by default. As a result, applications in these images can't bind to privileged ports (below 1024) when running in Kubernetes or in Docker Engine versions older than 20.10. To avoid issues, configure your application to listen on port 1025 or higher inside the container, even if you map it to a lower port on the host. For example, docker run -p 80:8080 my-image will work because the port inside the container is 8080, and docker run -p 80:81 my-image won't work because the port inside the container is 81.

No shell

By default, image variants intended for runtime don't contain a shell. Use dev images in build stages to run shell commands and then copy any necessary artifacts into the runtime stage. In addition, use Docker Debug to debug containers with no shell.

To see if a shell is available in an image variant and which one, select the Tags tab for this repository.

Entry point

Docker Hardened Images may have different entry points than images such as Docker Official Images.

To view the Entrypoint or CMD defined for an image variant, select the Tags tab for this repository, select a tag, and then select the Specifications tab.