Integrating ClickHouse and Deepnote for Enhanced Collaborative Analytics
Technology Category
- Infrastructure as a Service (IaaS) - Cloud Databases
- Robots - Collaborative Robots
About The Customer
The customers in this case are the users of ClickHouse, a fast OLAP database. These users are typically data scientists, analysts, and other professionals who need to derive insights from data. They work on data science projects and need a platform that allows them to collaborate efficiently. They use SQL for querying their database and Python for their data science tasks. They need a seamless transition between these two languages and a SQL editor with advanced features right in their notebook.
The Challenge
The challenge at hand was to provide a seamless and efficient platform for teams to discover and share insights from their data. The existing systems lacked a central place for collaboration and efficient work on data science projects. Moreover, the transitions between Python and SQL were not smooth, requiring a Python connector. There was also a need for a SQL editor with features like formatting, autocomplete, and linting right in the notebook.
The Solution
The solution came in the form of an integration between ClickHouse and Deepnote. Deepnote is a collaborative data notebook that works in the cloud, providing a central place for teams to work on data science projects. It is Jupyter-compatible and provides first-class support for SQL. This means users can query their ClickHouse database right from their notebook. The integration eliminates the need for a Python connector, making transitions between Python and SQL seamless. With Deepnote, users get all the features of a SQL editor right in their notebook, including formatting, autocomplete, and linting. Users can explore an interactive example of querying ClickHouse from Deepnote data notebooks through a template project connected to the ClickHouse playground.
Operational Impact
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