Semantic Graph Pilot

**Overview**

This pilot showcases how our Semantic Layer enables Databricks users to extract significantly more value from their Lakehouse data using
Kobai's Knowledge Graph technology.

Kobai's Semantic Layer enhances a Lakehouse architecture by making data more accessible and easier to work with, increasing utilization of your investment. The Semantic Layer grows with your Lakehouse, accommodating new data sources and formats without requiring extensive rework, lowering the friction of on-boarding new use cases. Using our Knowledge Graph platform, Kobai brings natural language to enterprise queries, making data more accessible to non-technical users and expanding the utility of the Lakehouse, while retaining the governance and transparency that is important to sophisticated users.

- Collaborative: Kobai's support of natural language queries drives faster knowledge discovery by enabling non-technical experts to query the Lakehouse.
- Integration: Experience streamlined knowledge creation with no silos or bottlenecks slowing integration with AI workflows as Kobai's knowledge graph lives natively in your Databricks Lakehouse.
- Semantic Augmentation: Improve AI outcomes by augmenting your data with semantic metadata. Create a semantically informed chat interface with Kobai's integration to Databricks Genie Spaces.
- Reusable: Maximize the value of data products by building the next generation on top of them. Make your data curation investments easier to reuse by making them visible and easily understandable.
- Flexible: Capture unexpected learning from AI, and merge disparate data initiatives into a more valuable tapestry. Graph relationships future-proof your data platform with more flexibility than an RDBMS can provide. Run what-if scenarios in our intuitive UI.

**Example Use Case**

*Predictive Maintenance*

Reduce Operating Costs for Predictive Maintenance using Kobai's Semantic Graph integrated with Databricks

[Click here](https://uploads-ssl.webflow.com/62d1cb85683aff61548561ea/666087c22e2815e7e78605f3_Predictive_Maintenance_-_Reduce_Operating_Costs_and_resource_planning_with_Databricks.pdf) to read the case study.

*Others*

Kobai’s semantic layer is extensively used by Aerospace, Energy, CPG, Manufacturing and Financial Services companies.

**Pilot Timeline**

- Week 1 - Land & validate data via Kobai Studio. Create context using a graph semantic model.
- Week 2 - Author codeless queries and logic. Map data in Databricks.
- Week 3 - Iterate on new curated datasets, sharing via APIs, Delta views and Notebooks.
- Week 4 - Present Pilot results and insights.

**Who is a good fit for a Databricks pilot with Kobai?**

- Already has customer data in Databricks.
- Users with understanding of the business problem and the data needs.
- Users with understanding of data integration and data modeling.
- Lots of disparate data frustrating efforts to get real insights.