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Watch On-Demand: The Advantages of a Semantic Layer

Backed by Real Success Stories and Expert Insights

  • GigaOm
  • Drift
  • Breakthrough

Check out our expert panel discussing "The Advantages of a Semantic Layer." Led by Artyom Keydunov, Cube's CEO, this session delves into how semantic layers impact the modern data stack.

"The mission of a semantic layer is to bring consistency to data across different places where it is consumed and visualized." - Andrew Brust, GigaOm

Andrew Brust from GigaOm joins us to share his valuable insights on semantic layers in the industry, highlighting why Cube stands out as a leader. We also have customer testimonials from Drift and Breakthrough. They share their experiences with Cube's semantic layer, addressing unique challenges and achieving tangible ROI with inspiring success stories.

To wrap up the event, our industry experts discuss various topics, emphasizing the importance of a semantic layer in the data stack.

Watch the webinar here and gain a deeper understanding of Cube's semantic layer.

Key Takeaways from the discussion:

  1. Semantic Layers' Importance: Semantic layers are crucial in providing consistency to data across different platforms where it is consumed and visualized. They bring all metrics definitions and semantics from multiple places into a single place between data warehouses and all visualization tools.
  2. Brust Praises Cube: Andrew Brust from Giga OM emphasized the importance of semantic layers in making data more approachable and self-explanatory. He praised Cube for its code-first orientation, open-source background, and multiple APIs, which make the model accessible anywhere.
  3. Cube's Impact at Drift: Andrew McGlathery from Drift shared how Cube has helped them achieve consistency in their metrics, handle scale, and manage governance of the model. He also mentioned how Cube has made adding new data quicker and easier.
  4. Cube Boosts Productivity: Juan Munoz from Breakthrough highlighted how Cube has improved productivity and flexibility in their company by providing consistency across all their measures and dimensions for both customer-facing and internal analytics.
  5. Data Understanding for AI: The panelists agreed that a well-understood data is critical for successful AI projects. They noted that semantic modeling not only makes data projects more successful but also lays the foundation for successful AI projects.
  6. Semantic Layers' AI Future: The future of semantic layers in the context of generative AI was discussed. The panelists predicted that more SQL will be generated by AI agents in the future for different use cases. They also highlighted the importance of explainability in AI and how semantic layers can provide the necessary context for AI to work accurately.

Click here to watch the webinar on-demand and hear all the key takeaways discussed during the talk.