Querying Timbr with Notebooks:
The data scientists’ encyclopedia of Enterprise Knowledge
More Science, Less Data Preparation
Timbr connects seamlessly to Jupyter and Zeppelin notebooks to reduce time spent by data scientists in the menial tasks of organizing, collecting and mining the data.
Prepare data for modeling using Timbr’s graph data exploration tool that facilitates data discovery, and perform data mining and data analysis by querying Timbr’s ontology in SQL queries that are up to 90% shorter than non-semantic SQL queries.
Semantic graph capabilities that empower
data scientists’ with ready to consume data
Connected, context-enriched, smart concepts: Timbr enables the most productive architecture for ML algorithms and data enrichment, accelerating procurement of data and enhancing its quality for efficient creation of feature tables.
Simplified queries from notebooks enable convenient extraction of information and new insights from large repositories of unstructured and structured data that resides in multiple sources such as file systems, databases, streams, APIs, and other platforms and applications.
Timbr equips data scientists' notebooks with
fast, smart and context-based:
Hypothesis Testing
Feature Engineering
Data Mining
Use Timbr with your favorite data science tool
Timbr is natively accessible in Apache Spark, SQL, R, Python, Scala and Java.
Available through Python’s JPype, JayDeBeAPI, and SQL-Alchemy libraries.
