Advanced Data Analytics and ML using Logical Data Fabric
summary
Claims that a Denodo logical data fabric uses data virtualization to create a middle layer that integrates Snowflake, Oracle, Salesforce, CSV and log files without replication, pushes computation to sources, exposes a semantic layer and central governance, and supplies reusable building‑block views for analytics and ML. Implementation reduces end‑to‑end analytics prep time by about 30% faster, cuts data availability from 3 days to 3 hours, and frees analysts by delivering Denodo logical data fabric assets directly into BI and model pipelines.
AI-written summary · about
chapters (16)
- 0:00Introduction
- 0:26Agenda
- 1:09What is Advanced Analytics
- 1:36Marketing
- 2:34Operations
- 3:53Supply Chain
- 5:00Customer
- 10:49Business Agility
- 14:14gartner Data Fabric
- 15:55What is Data Fabric
- 16:28Data Fabric Architecture
- 17:35What is a Logical Data Fabric
- 17:53Data Virtualization
- 21:23Logical Data Fabric
- 29:31Data Science
- 31:54Conclusion
related talks
25mDemocratizing Data With A Logical Data Fabric, Powered By Data Virtualization
26mLogical data fabric for building a unified data warehouse and data lake
22mData virtualization - Unlocking data - From silos to success?
20mData Mesh, Data Fabric & Co - the End of Monolithic Architecture Concepts?
32mConnecting Silos In Real-time With Data Virtualization
47mData Mesh vs Data Fabric - in Microsoft Fabric
27mEmpowering Generative AI with Unified Data Integration: A Logical Approach.
17mOpen Data Lakes: Latest stop in the big data journey