Simplify your journey from data to knowledge
Timbr delivers x10 faster data engineering and consumption with agile semantic modeling, relationships, data virtualization, and
up to 90% shorter SQL queries.
Across industries, data practitioners, and business users – Our customers love Timbr
Timbr delivers the powers of knowledge graphs to consume data fast and with minimum effort
- Create a semantic graph model that endows data with meaning and relationships.
- Map data sources to the model to gain a 360° access to data.
- Query the model in SQL using relationships that replace JOINs.
- Query the model in Spark, Python, R, Java or Scala to power data science and ML.
- Virtualize and cache data to optimize query performance.
- Visualize and explore data as a web of relationships.
- Use of-the-shelf graph algorithms for advanced analytics without need of transforming data.
- Accelerate delivery of data products and enable universal data consumption via REST / ODBC / JDBC to power web applications and analytical tools.
Knowledge Graph Superpowers for the SQL Ecosystem
Semantic Representation
of Data
Model and integrate data using common concepts to unify meaning and align business metrics across data products
Semantic Relationships
Define relationships that substitute complex JOIN and UNION statements so queries become much simpler and shorter
Hierarchies and Classifications
Organize concepts using hierarchies and classifications that provide better understanding of the data
Logic
Use SQL logic and math operators to filter the mapped data to a concept to facilitate and accelerate consumption
Inference
Derive new knowledge from existing knowledge based on relationships and a formal set of inference rules
Data from everywhere to anyone
Timbr semantic graph enables seamless engineering and consumption of data from multiple sources, connected as a semantic graph characterized by relationships, unified metrics and business context.
Agile semantic data modeling
The platform features visual no-code data modeling of data using business concepts and relationships, aligning business metrics and unifiying naming conventions. Users leverage graph capabilities to model data for easy understanding and consumption with hierarchies, classifications and business rules, Learn more…
Short SQL Queries
Without Timbr: 22 lines of SQL
SELECT `order`.`order_id`, `order_date`,
`home_office_customer`.`customer_name`,
`first_class_shipment`.`shipping_date`,
`fitness_product`.`product_name`
FROM `scdata`.`datacosupplychaindataset_order` as `order`
LEFT JOIN (
SELECT product_id, product_name
FROM `supply_chain_demo`.`product`
WHERE `department` = 'Fitness') as `fitness_product`
ON `order`.`product_id` = `fitness_product`.`product_id`
LEFT JOIN (
SELECT `order_id`, `shipping_date`
FROM `scdata`.`datacosupplychaindataset_shipment`
WHERE `shipping_mode` = 'First Class') as `first_class_shipment`
ON `order`.`order_id` = `first_class_shipment`.`order_id`
LEFT JOIN (
SELECT `customer_id`,
concat(`customer_first_name`, ' ', `customer_last_name`) as `customer_name`
FROM `scdata`.`datacosupplychaindataset_customer`
WHERE `customer_segment` = 'Home Office') as `home_office_customer`
ON `order`.`customer_id` = `home_office_customer`.`customer_id`
WHERE `product_price` * `order_item_quantity` - `order_item_discount` > 500
With Timbr: 6 lines of SQL
SELECT order_id, order_date,
`ordered_by[home_office_customer].customer_name`,
`in_shipment[first_class_shipment].shipping_date`,
`includes_product[fitness_product].product_name`
FROM dtimbr.`order`
WHERE revenue > 500
- Query the model
- Quickly answer complex questions
- Write and debug queries easily
- Leverage explicit relationships to eliminate JOINs and UNIONs
- Simplify recursive queries
Visually exploring data as a graph
Timbr’s Graph Data Explorer allows users to visualize the underlying data as a graph, to explore and discover relationships and dependencies in the data. The module enables traversing the entire organizational data so users can better understand the data, discover hidden value, visually find answers and expose the data without need of extracting tables or views before running a query.
Read our latest blogs
Blog
Delivering Hyper-Personalized Products and Services
In collaboration with Capgemini, this use case delves into hyper-personalization, presenting how by harnessing Timbr’s capabilities, companies can accelerate their journey to hyper-personalization.
Blog
Timbr is the Intelligent Semantic Layer for Databricks Lakehouse
In this article, we explore the benefits of combining Databricks Lakehouse with Timbr to enable organizations to create scalable semantic graph models that accelerate utilization of data.
Blog
Timbr+Databricks deliver an Insights First Architecture to Power Data Consumption
Timbr plays a crucial role in powering the Insights First Architecture, which aims to enable enterprise data consumption patterns that facilitate efficient insight delivery and acceleration.
