timbr for Notebooks: The data scientists’ encyclopedia of Enterprise Knowledge

More Science, Less Data Preparation

Spark-native 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 web capabilities that empower
data scientists’ work with Notebooks

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’s Notebooks integration enables
fast, smart and context-based:

Hypothesis Testing

Feature Engineering

Data Mining

Use timbr with your favorite Notebook

timbr is natively accessible in Apache Spark, SQL, R, Python, Scala and Java.
Available through Python’s JPype, JayDeBeAPI, and SQL-Alchemy libraries.