As a user, you are looking for agile and low cost iterations during your Data Science Project.
Datalayer targets 3 types of users who can create and/or use cost effective clusters of size they need without prior IT skills.
- Standalone Data Scientists.
- Small to medium analytics teams of data-driven business.
- Small analytics teams of non-data-driven business.
The typical stakeholders of a Data Science project are the
Data Scientist or a
You may have more than one hat (more than one profile) during the lifecycle of your Data Science project. For those 3 profiles, Datalayer solutions make life easier, more productive, cheaper and bring more business value.
- Data Scientists use the
Explorerto explore and share insights in a visual way, in private or in public (e.g. on Twitter). You also use the
Explorerto request cluster resources for a certain period of time.
- Business review the Data Scientist insights in his favorite environment like Microsoft Office 365 or Google.
- Devops use the
Kuber CLIto create the Kubernetes cluster and deploy the needed Applications. You also get the
Kuber UIif you prefer beautiful screens.
This is applicable in
Alpha environments at every steps of your Projects
Big Data Science adoption trend can be summarized as such.
Business Stakeholders read and comment
Data Scientists use their Google drive and publish stories on twitter. They feed and search a
- Datasets on HDFS and IPFS.
- Notes to write, publish and reuse.
- Models to create, publish and reuse.
- APIs assembled based on Notes, to be deployed and monetized.
They analyse data and share results with
Spark on the
Datalayer Explorer, a
JupyterLab Web Notebook with extensions on a
Devops use a
Datalayer to setup, secure and operate the distributed components that build up the Kubernetes platform.