Establishing AI agent for self-service analytics running with Databricks: what enterprises can leverage to make it 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀 𝗵𝗶𝗴𝗵𝗹𝘆 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗮𝗻𝗱 𝗶𝗻𝘀𝘁𝗮𝗻𝘁?
⏹️ The foundations: Your scalable data platform, where data being integrated and optimized through layers for refinement and governance.
✔️ 𝘉𝘳𝘰𝘯𝘻𝘦 𝘭𝘢𝘺𝘦𝘳: The foundational entry point where data from various sources is ingested and stored "as-is".
✔️ 𝘚𝘪𝘭𝘷𝘦𝘳 𝘭𝘢𝘺𝘦𝘳: An intermediary layer integrating raw data from the bronze layer and cleaning, transforming, standardizing it so it's trustworthy and ready for business-level analysis.
✔️ 𝘎𝘰𝘭𝘥 𝘭𝘢𝘺𝘦𝘳: Business-friendly layer where data is stored domain-specific. 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 is highly recommended to simplify sharing and retrieval by aligning data directly with how consumers naturally ask questions, allowing for much 𝗳𝗮𝘀𝘁𝗲𝗿 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀.
✔️ 𝘚𝘦𝘮𝘢𝘯𝘵𝘪𝘤 𝘭𝘢𝘺𝘦𝘳: The translation hub provides a unified business view of data across organization. This's where your Intelligent Analytics agent (and other consumer apps) prioritizes to connect for contexts & metrics. Acting as source of truth to provide accuracy & reliability. The complexity and performance of semantic definitions are heavily derived from the Gold layer: Less joins and clear attributes are desired.
⏹️ Genie Spaces: Your Intelligent Analytics agent, where consumers ask natural-language questions for visual insights
⏹️ Instructions for Genie Spaces: Your Agent Skills, where additional configurations can be added to guide Genie Spaces on how to behave through ambiguous data, vague requests, or what the outcome looks like.
Happy to discuss further on how to engineer a data platform for intelligent analytics!
My Linkedin: https://www.linkedin.com/in/quoc-n
