

Briefer
Overview :
Briefer is an open-source data platform that allows users to run SQL and Python code, transforming notebooks into dashboards and data applications. It supports connections to various data sources, such as Postgres, BigQuery, Redshift, and enables the direct use of query results in Python code blocks. Additionally, it features pre-installed libraries and an integrated AI assistant to help users write code faster. The dashboard and data application functionalities of Briefer empower users to create interactive pages for data exploration and decision support.
Target Users :
The primary target audience includes data scientists, engineers, and teams that require data analysis and visualization. They can use Briefer to enhance the efficiency of data processing and analysis, quickly create dashboards and data applications, and facilitate team collaboration.
Use Cases
Data scientists use Briefer for complex data analysis and model training.
Engineering teams collaborate on developing data-driven applications using Briefer.
Businesses utilize Briefer to create interactive dashboards for real-time monitoring of business metrics.
Features
Supports connections to various data sources, such as Postgres, BigQuery, Redshift, etc.
Enables writing SQL directly in Briefer, automatically converting query results into data frames.
Includes an integrated AI assistant to help users quickly write Python code.
Allows notebooks to be transformed into interactive dashboards and data applications.
Provides functionality to schedule Python code blocks for running, suitable for data rewriting or training machine learning models.
Enables creation of public links to share reports and dashboards.
Supports real-time collaborative editing with live visibility of editors and their modifications.
How to Use
Visit the Briefer official website and create an account.
Connect your data sources, such as Postgres, BigQuery, etc.
Write and execute SQL queries in the Briefer documentation.
Use the query results to process and analyze data within Python code blocks.
Convert analysis results into dashboards or data applications.
Set up scheduled tasks to automatically run Python code blocks.
Collaborate with team members by editing documents and viewing their edits in real time.
Publish dashboards or data applications and create public links to share with others.
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