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Overview

This section contains documentation for various Timbr integrations, demonstrating how to connect Timbr’s semantic layer to different tools and platforms for data exploration, analytics, and application development. Below is an extensive summary of each document:

Datasources​

Offers guidance on connecting to:

BI Tools​

Demonstrates connecting BI Tools to Timbr either via Spark or via Databricks. Explains how to specify servers, URLs, and tokens, and how to import or directly query Timbr concepts as if they were tables. Shows how to configure advanced options, handle multi-relationship concepts, and create visualizations using Timbr-based data.

Supported BI​

  • PowerBI
  • Tableau
  • Domo
  • MicroStrategy
  • Looker Studio
  • Sisense
  • TIBCO
  • Or any other BI tool with a JDBC/ODBC connection

Python Integrations​

How to connect Python to Timbr by using either

Databricks Integration​

Provides an overview of Timbr’s semantic lakehouse approach and how Timbr semantically models data stored in Databricks (or other lakehouses). Demonstrates how to install Timbr in databricks, and surface these semantic relationships directly in Unity Catalog/Hive metastore and query them using SQL or Python from Databricks notebooks. Highlights the simplification of queries by replacing complex joins with natural relationships, and how to leverage data virtualization in Timbr

Microsoft Excel​

Explains how to connect Microsoft Excel to Timbr's semantic layer for data analysis and reporting. Details multiple connection methods including MDX, ODBC, and direct query capabilities. Shows how to create PivotTables from semantic concepts, build interactive dashboards, and leverage Excel's familiar interface for complex data exploration. Includes step-by-step configuration instructions, recommended settings for optimal performance, and troubleshooting common connection issues. Features a dedicated section on the Timbr Natural Language Query (NLQ) add-in, which enables business users to query data using plain English, automatically translating natural language questions into semantic SQL. The NLQ add-in supports follow-up questions, provides result explanations, and allows direct export to Excel worksheets for further analysis.

Retool​

Explains how to connect Timbr's semantic layer to Retool for building internal tools and business applications. Details multiple connection methods including OpenAPI integration for structured API access, Databricks connection leveraging the data lakehouse capabilities, and direct REST API connections for maximum flexibility. Shows how developers can create custom UIs, dashboards, and operational tools that query semantic concepts without writing complex SQL. Covers authentication configuration, query optimization, and how to build reusable Retool components that leverage Timbr's semantic relationships. Demonstrates how business logic defined in the ontology automatically propagates to Retool applications, ensuring consistent data interpretation across the organization's tools. Includes examples of transforming semantic query results into interactive visualizations, admin panels, and operational interfaces.

JDBC Connectors​

Provides information on connecting to Timbr using popular database visualization and management tools through JDBC. These tools enable users to explore the ontology, visualize relationships, and execute semantic queries against the Timbr platform.

DbVisualizer​

Shows steps to configure DbVisualizer to query Timbr concepts as virtual tables. Details driver setup using the Timbr JDBC, how to create and manage connections, and how to browse concepts' relationships as an ERD. Illustrates exploring the ontology visually for streamlined data discovery.

DBeaver​

Describes how to integrate Timbr with DBeaver via the Timbr Hive-based JDBC. Explains creating a new database connection with a custom driver, setting up server URLs, authentication with tokens, and exploring Timbr schemas. Provides instructions for running queries, previewing data, and viewing concept relationships.

LLM Integrations​

Demonstrates how to integrate Timbr's semantic layer with Large Language Model (LLM) frameworks for natural language processing and semantic data retrieval with higher accuracy. Enables applications to interpret user queries, automatically generate appropriate Timbr SQL, and return structured results.

LangChain SDK​

Explains how to connect Timbr with the LangChain ecosystem to process natural language queries, automatically map them to Timbr SQL, and return results. Demonstrates agent and chain configurations for generating semantic SQL, validating queries, and executing them. Covers multi-LLM support and instructions on installation, usage, and customization.

LangGraph SDK​

Shows how to integrate Timbr's ontology with the LangGraph framework for building natural language understanding pipelines. Uses large language models (LLMs) to parse user requests, generate Timbr SQL, and fetch data. Demonstrates concept identification, SQL generation, validation, and query execution steps within LangGraph nodes.