Dataiku 1.2 Shiso Release and Community Edition

Dataiku Product Clément Stenac

We are very proud to announce today the release of Dataiku v 1.2, codenamed Shiso. With this release, we now offer a free version: Dataiku Community Edition !

Heads Up!

This blog post is about an older version of Dataiku. See the release notes for the latest version.

Let's go >

 

Less than two months after the release of the Yuzu version, Dataiku 1.2 Shiso introduces new features that keep making data science more accessible to everyone. Let's have a look at what's new in this Shiso version!

Community Edition

Dataiku now provides a free edition: the Community Edition. It is a great way for a data scientist or any person interested in data science to get started with our software. You can download and install it on a Linux server. We support Ubuntu, Debian, CentOS, RHEL, and Amazon Linux.

You may also sign up for a free online trial to get a hosted instance of the full edition.

Scheduling

Our advanced users need tools to runtheir best predictives models in production. Dataiku now allows you to schedule jobs every day or every hour directly within the UI (no command line anymore!).

It leverages our incremental build support in order to recompute and rebuild predictions on new data or data that has changed.

Dataiku v1.2 Scheduling

Cassandra and Impala Support

We keep adding new integrations to our platform to let you work more efficiently with all the major big data technologies.

In Dataiku v1.2, we are happy to announce support for Cassandra (read and write) and Impala (queries and charts).

Pinboards

We recently introduced Pinboards that let you share charts and others insights (web applications, IPython notebooks, datasets, etc.) with your collaborators and present them on a global dashboard.

Dataiku v1.2 Pinboards

Beta H2O support

Some Machine Learning algorithms can take quite a long time to run. Dataiku now features integration with H2O, a distributed machine learning framework.

This integration brings distributed implementations of a selected set of machine learning algorithms (Random Forest, Deep Learning, Gradient Boosting Methods).

Which better fits your data, scikit-learn or H2O? Find out with Dataiku.

This is just a highlight of the main features in Dataiku 1.2 Shiso. For more details, you can read our Release notes.

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