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Inside Hopsworks

Hopsworks provides a complete self-service development environment for feature engineering and model training. You can develop programs as Jupyter notebooks or jobs, you can manage the Python libraries in a project using its conda environment, you can manage your source code with Git, and you can orchestrate jobs with Airflow.

Jupyter Notebooks#

Hopsworks provides a Jupyter notebook development environment for programs written in Python, Spark, Flink, and SparkSQL. You can also develop in your IDE (PyCharm, IntelliJ, etc), test locally, and then run your programs as Jobs in Hopsworks. Jupyter notebooks can also be run as Jobs.

Source Code Control#

Hopsworks provides source code control support using Git (GitHub, GitLab or BitBucket). You can securely checkout code into your project and commit and push updates to your code to your source code repository.

Conda Environment per Project#

Hopsworks supports the self-service installation of Python libraries using PyPi, Conda, Wheel files, or GitHub URLs. The Python libraries are installed in a Conda environment linked with your project. Each project has a base Docker image and its custom conda environment. Jobs are run as Docker images, but they are compiled transparently for you when you update your Conda environment. That is, there is no need to write a Dockerfile, users install Python libraries in their project. You can setup custom development and production environments by creating new projects, each with their own conda environment.


In Hopsworks, a Job is a schedulable program that is allocated compute and memory resources. You can run a Job in Hopsworks:

  • from the UI;
  • programmatically with the Hopsworks SDK (Python, Java) or REST API;
  • from Airflow programs (either inside our outside Hopsworks);
  • from your IDE using a plugin (PyCharm/IntelliJ plugin);


Airflow comes out-of-the box with Hopsworks, but you can also use an external Airflow cluster (with the Hopsworks Job operator) if you have one. Airflow can be used to schedule the execution of Jobs, individually or as part of Airflow DAGs.