Computing power depending on the application size: Small-Medium: 4 CPU 16 GB RAM server. Large : 8 CPU 32 GB RAM server. Deployment options (Docker or Kubernetes): Docker/Linux: Linux image or automatic deployment via docker-compose can be installed on any Linux server (you can extend our YAML to add your security protocols, configurations, and customizations. …
FAQs
Yes, any tool that can create an ERD from a JDBC connection can also create an ERD from a Timbr ontology.
Timbr connects to all popular data lakes, databases, BI tools, data science tools and notebooks, as well as various applications (APIs). Once connected, the data can be queried in SQL, Python/R, dataframes, and natively in Apache Spark (SQL, Python, R, Java, Scala). GraphQL can be supported by integrating external open source projects that support the …
What interfaces are available to connect with Timbr? Read More »
Yes. Timbr’s users can connect to Timbr with the help of REST APIs. Click here to learn more about how this is done.
Data mesh tries to solve three challenges with a centralized data lake/warehouse: Lack of ownership: who owns the data – the data source team or the infrastructure team? Lack of quality: the infrastructure team is responsible for quality but does not know the data well Organizational scaling: the central team becomes the bottleneck, such as …
What is a data mesh? and how does Timbr help implement a data mesh architecture? Read More »
Yes. Timbr provides a comprehensive solution to integrate multiple databases located in varied locations. In terms of deployment, Timbr is deployed in Kubernetes or Docker at the user’s choices. Timbr also supports multi-cluster deployments so users can deploy Timbr on Azure, GC or AWS. In general, Timbr recommends cloud because of the managed services, though …
Does Timbr work in a Hybrid/multi-Cloud environment? Read More »
Yes. Timbr’s default implementation for graph algorithms is networkX and it happens automatically, meaning that when a user writes an SQL query Timbr automatically runs the algorithm behind the scenes. Timbr also supports Nvidia’s Cugraph (GPU) enabling graph algorithms with advanced performance.
No. With Timbr a user can map multiple tables from multiple data sources to the same concept. Therefore, users can leave their data wherever it is and just use Timbr’s data mapping tool to conveniently map data from the various data sources to the ontology model.
Timbr fully complies with OWL DL (the computable closed world version of OWL). Timbr’s SQL re-write engine is an inference engine that automatically generates queries based on inference rules, including inheritance, transitivity clause and others. Defining inference in Timbr can be done through Timbr’s visual interface or via SQL statements. In addition, Timbr adds capabilities …
Timbr can leverage any data catalogs into a knowledge catalog of the business, automatically generating the semantic model. Timbr can also work with the data catalog’s business glossary and the data mappings.
